A standardized liquid flow battery stack system cabinet

Abstract

The present invention discloses a standardized liquid flow battery stack system cabinet, which relates to the field of energy storage and solves the shortcomings of existing standardized liquid flow battery stack system cabinets, such as high production cost, inconvenience in large-scale production, low energy density, long construction time and low operation and maintenance efficiency. The cabinets can flexibly assemble and replace stack systems of different powers and capacities through quick connection modules, which is convenient for large-scale production and expansion. The circulation system ensures that the flow of liquid electrolyte is uniform and stable. The stack temperature control system is used to control the temperature of the liquid flow battery stack, which improves the energy density and conversion efficiency. The modular structure and quick connection modules can realize flexible assembly and replacement of stack systems of different powers and capacities, which shortens the construction time and improves the construction efficiency. The intelligent management module optimizes the scheduling and energy management of the energy storage system through an adaptive optimization algorithm based on deep learning, which improves the operation and maintenance efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of liquid flow battery stacks, and more specifically to a standardized liquid flow battery stack system cabinet. Background Art

[0002] With the rapid development of the global economy and the continuous growth of energy demand, people's demand for clean and efficient energy is becoming increasingly urgent. The limited traditional fossil energy and environmental pollution problems also make people eager to find new energy solutions. In this context, the flow battery stack system has become a highly concerned technical field.

[0003] At present, the flow battery stack system has achieved certain technical breakthroughs. The system uses flow battery technology to combine batteries and stacks to achieve higher energy conversion efficiency and energy storage capacity. At the same time, the system adopts standardized design and modular assembly to improve production efficiency and product consistency.

[0004] However, there are still some inconveniences in achieving large-scale production of liquid flow battery stack systems. First, the manufacturing process of the liquid flow battery stack system is relatively complex, requiring highly sophisticated assembly technology and strict quality control, resulting in high production costs and inconvenience in achieving large-scale production. Secondly, due to the design characteristics of the liquid flow battery stack system, its energy density is relatively low. This means that under the same volume, the energy that can be stored in the liquid flow battery stack system is relatively limited, which limits its application in some scenarios. In applications that require high energy density, such as electric vehicles or large-scale energy storage systems, the insufficient energy density of the liquid flow battery stack system has become a limiting factor. In addition, the construction time of the liquid flow battery stack system is long, requiring complex engineering construction processes and debugging optimization. At the same time, the operation and maintenance efficiency of the liquid flow battery stack system is relatively low, requiring frequent overhaul and maintenance, increasing operating costs and workload, while the stability and life of the materials also face some challenges, further increasing costs.

[0005] Therefore, in order to solve the shortcomings of existing standardized liquid flow battery stack system cabinets, such as high production cost, inconvenience in large-scale production, low energy density, long construction time and low operation and maintenance efficiency, the present invention discloses a standardized liquid flow battery stack system cabinet. Summary of the invention

[0006] In view of the shortcomings of the prior art, the present invention discloses a standardized liquid flow battery stack system cabinet, which adopts modular design and quick connection modules to flexibly assemble and replace stack systems of different power and capacity, thus facilitating large-scale production and expansion; in order to improve energy density, the scheme adopts an optimized circulation system and a thermal management module; the circulation system improves the liquid circulation performance through an axial vortex pump to ensure uniform and stable flow of the liquid electrolyte; the thermal management module realizes temperature control of the liquid flow battery stack through the stack temperature control system, keeps the stack operating temperature within a suitable range, and improves energy conversion efficiency; in order to shorten the construction time, the scheme adopts standardized design and modular structure; the quick connection module is adopted to realize flexible assembly and replacement of stack systems of different power and capacity, shortening the construction time and improving the construction efficiency; in order to improve the operation and maintenance efficiency, the scheme introduces an intelligent management module; the intelligent management module optimizes the scheduling and energy management of the energy storage system through an adaptive optimization algorithm based on deep learning.

[0007] In order to achieve the above technical effects, the present invention adopts the following technical solutions:

[0008] A standardized flow battery stack system cabinet, comprising:

[0009] A cabinet, wherein the cabinet is provided with a cabinet door that can be opened or closed, a base is provided at the bottom of the cabinet, a battery management system and a thermal management module are provided on the cabinet facing the inner side of the cabinet door, the thermal management module includes an air conditioning system and a heat exchanger, the battery management system is arranged above the air conditioning system, a circulation system is arranged inside the cabinet, the circulation system includes a negative electrode liquid inlet, a negative electrode liquid outlet, a positive electrode liquid inlet, a positive electrode liquid outlet, a negative electrode electrolyte main line, a positive electrode electrolyte main line and a connecting pipeline, a negative electrode circulation pump, a positive electrode circulation pump and a valve are also arranged on the circulation system, the negative electrode circulation pump and the positive electrode circulation pump are arranged in parallel and control the valves respectively, a liquid collecting tank is embedded in the base, and the liquid collecting tank is arranged to accommodate the negative electrode electrolyte main line and the positive electrode electrolyte main line, and the negative electrode electrolyte main line and the positive electrode electrolyte main line are connected to the valve; wherein: a thermal management module is used to realize temperature control of each battery stack; the thermal management module realizes temperature control of the flow battery stack through the battery stack temperature control system;

[0010] A circulation system is used to ensure uniform and stable flow of liquid electrolyte; the circulation system improves liquid circulation performance through an axial vortex pump; the axial vortex pump optimizes the design of the liquid circulation system through a fluid dynamics CFD numerical simulation method;

[0011] A battery management system is used to monitor and manage the entire flow battery energy storage system; the battery management system realizes the monitoring and management functions of the flow battery energy storage system through a sensor network and a battery stack operation analysis algorithm; the cabinet also includes:

[0012] A quick connection module is used to realize flexible assembly and replacement of battery stack systems with different powers and capacities; the quick connection module realizes flexible assembly and replacement of battery stacks with different powers and capacities through data communication protocols and electric energy transmission methods;

[0013] An intelligent management module is used to optimize the scheduling and energy management of the energy storage system; the intelligent management module realizes the optimization scheduling and energy management functions of the energy storage system through an adaptive optimization algorithm based on deep learning;

[0014] The output ends of the thermal management module and the circulation system are connected to the input end of the battery management system; the output ends of the quick connection module and the battery management system are connected to the input end of the intelligent management module.

[0015] As a further technical solution of the present invention, the temperature control system of the fuel cell stack includes a temperature sensing module, a temperature control module and a multi-stage heat pipe module; the temperature sensing module realizes the collection and transmission of temperature data through a temperature sensor and a wireless transmission method; based on the sensor data, the temperature control module automatically adjusts the internal temperature of the fuel cell stack through a big data analysis method; the multi-stage heat pipe module controls the internal temperature of the fuel cell stack through a multi-stage evaporator and a condenser; the multi-stage evaporator is connected to the condenser through a heat pipe to form a heat transfer network; the heat transfer network transfers heat to the outside for heat dissipation through heat conduction and phase change heat transfer methods; the phase change heat transfer method absorbs or releases heat through the phase change process of the working fluid, thereby improving the thermal capacity and heat transfer efficiency of the heat pipe.

[0016] As a further technical solution of the present invention, the working steps of the fluid dynamics CFD numerical simulation method in a standardized flow battery stack system cabinet are as follows:

[0017] S1. Establish geometric model;

[0018] Establish a three-dimensional numerical model of the liquid circulation system using computer-aided design software;

[0019] S2, setting boundary conditions;

[0020] Determine at least the inlet flow rate, outlet pressure and wall friction resistance in the liquid circulation system through an expert system and experimental data;

[0021] S3, divide the computational grid;

[0022] The interior of the system cabinet is divided into a grid of multiple small units by using the finite volume method;

[0023] S4, solving the numerical model;

[0024] Numerical calculation is performed on the flow behavior of the liquid in the system cabinet by using the finite difference method to obtain at least flow field, pressure field and velocity field parameters;

[0025] S5. Liquid circulation performance analysis;

[0026] According to the fluid dynamics results obtained by solving, the finite element method is used to analyze at least the flow velocity distribution, vortex formation and turbulence intensity parameters of the liquid during the circulation process;

[0027] S6. Optimize design;

[0028] According to the analysis results, the reverse design function provided by the CFD software is used to adjust and optimize at least the impeller geometry, blade number and blade angle parameters of the axial vortex pump.

[0029] As a further technical solution of the present invention, the stack operation analysis algorithm calculates the power density of the stack by using a stack power density calculation formula, and the formula expression of the stack power density calculation formula is:

[0030]

[0031] In formula (1), R is the power density of the battery stack; P is the output power of the battery stack; m is the height of the battery stack, which is used to measure the thickness of the battery stack electrode stack; O represents the effective electrode surface area, which is used to represent the total area of ​​the active area of ​​the electrochemical reaction; C represents the current density, which is used to measure the amount of current passing through the battery stack per unit area; the loss rate of the battery stack during the energy conversion process is calculated by the battery stack energy loss function; the formula expression of the battery stack energy loss function is:

[0032]

[0033] In formula (2), D represents the loss of battery state of charge; h is the input energy of the battery stack, which is used to represent the total energy received by the battery stack; v is the output energy of the battery stack, which is used to represent the total energy output by the battery stack; δ represents the current compensation coefficient of the battery stack, which is used to compare the difference between the battery stack voltage and the theoretical voltage under different working conditions; a is the efficiency factor, which is used to consider the efficiency of actual energy conversion; the efficiency of the battery stack thermal management system is evaluated by the battery stack thermal management efficiency function; the formula expression of the battery stack thermal management efficiency function is:

[0034]

[0035] In formula (3), Y represents the efficiency of the thermal management system of the fuel cell stack, which is used to express the ratio between the heat removal rate and the heat generation rate; s is the heat removal rate, which is used to measure the heat removed from the fuel cell stack; c is the heat generation rate, which is used to measure the heat generated inside the fuel cell stack; r represents the effect of external heat sources on heat, which is used to measure the additional heat input from the environment or other systems.

[0036] As a further technical solution of the present invention, the quick connection module includes a communication unit, an energy transmission unit and a control unit; the communication unit transmits the status information of each battery stack to the system controller for management and monitoring through the bus communication protocol CAN; the energy transmission unit realizes energy transmission between battery stack systems of different power and capacity through a modular battery connector; the modular battery connector realizes modular assembly and replacement through a plug-in interface; the control unit realizes linkage control between battery stack systems of different power and capacity through a linkage control method; the working steps of the linkage control method in a standardized liquid flow battery stack system cabinet are as follows:

[0037] R1, system initialization;

[0038] Connecting the battery stack system and the quick connection module to the control host via Ethernet to achieve data exchange and command transmission; the control host reads the power and capacity information of each battery stack system via a data communication protocol;

[0039] R2, power matching and regulation;

[0040] Adjust the output power of the battery stack system by pulse width modulation method;

[0041] R3, capacity matching and adjustment;

[0042] The liquid flow distribution ratio between the stack systems is adjusted by a liquid flow control method; the liquid flow control method controls the output capacity of the stack system by adjusting the opening degree of the valve through a motor;

[0043] R4, real-time monitoring;

[0044] The output power and capacity of each battery stack system are monitored in real time through current sensors and liquid level sensors;

[0045] R5, optimization control;

[0046] Based on the monitored information, the fuel cell system is adjusted and controlled through feedback control methods.

[0047] As a further technical solution of the present invention, the data adaptive optimization algorithm based on deep learning monitors and controls the battery in real time through a deep learning model; the deep learning model calculates the battery state vector and error covariance matrix at the current moment through a battery state estimation formula; the expression of the battery state estimation formula is:

[0048]

[0049] In formula (4), M represents the battery state vector at the current moment estimated based on the state at the previous moment, x is the Kalman gain, which is used to update the state estimation and error covariance matrix according to the measured value; y is the observation noise covariance matrix, which is used to represent the error between the observed value and the true value; t represents the state transition vector, which is used to measure the change of the battery state within the time interval; u represents the prediction error covariance matrix, which is used to calculate the error between the predicted value and the true value; z represents the observation matrix, which is used to measure the relationship between the measured value and the battery state; the load is predicted by the load prediction formula to further optimize the scheduling and energy management functions; the formula expression of the load prediction formula is:

[0050]

[0051] In formula (5), S represents the load data at the previous moment; K represents the length of historical data, which is used to use the load data of the previous β moments as input; d represents the load data at the next moment predicted based on the load data at the current moment; the historical data is analyzed and learned through the adaptive charge and discharge strategy optimization formula, and the charge and discharge strategy is further automatically adjusted; the formula expression of the adaptive charge and discharge strategy optimization formula is:

[0052]

[0053] In formula (6), F represents the charge and discharge strategy vector at the vth moment; a represents the charge and discharge strategy vector at the vth moment; θ represents the utility function, which is used to represent the benefit value obtained by adopting the charge and discharge strategy vector a under the current load condition; and v represents the load data, which is used to maximize the utility function value.

[0054] As a further technical solution of the present invention, the circulation system includes a positive electrode circulation pump, a negative electrode circulation pump, a connecting pipe, a valve, a positive electrode liquid inlet, a negative electrode liquid inlet, a positive electrode liquid outlet, a negative electrode liquid outlet, a positive electrode electrolyte main line, a negative electrode electrolyte main line and a liquid collecting tank; the inlet of the positive electrode circulation pump is connected to the positive electrode electrolyte main line, and a valve is provided in the connecting pipe between the two. The outlet of the positive electrode circulation pump is connected to the positive electrode liquid inlet of each battery stack through a connecting pipe, and the positive electrode liquid outlet of each battery stack is connected to the positive electrode electrolyte main line through the connecting pipe, and a valve is provided in the connecting pipe between the two. The valve constitutes a positive electrode circulation system loop; the negative electrode circulation pump inlet is connected to the negative electrode electrolyte main line, and the valve is arranged in the connecting pipe between the two; the negative electrode circulation pump outlet is connected to the negative electrode liquid inlet of each battery stack through the connecting pipe, and the negative electrode liquid outlet of each battery stack is connected to the negative electrode electrolyte main line through the connecting pipe, and the valve is arranged in the connecting pipe between the two to constitute a negative electrode circulation system loop; wherein the valve, the positive electrode liquid inlet, the negative electrode liquid inlet, the positive electrode liquid outlet, and the negative electrode liquid outlet are all connected to the connecting pipe by a flexible joint.

[0055] Positive beneficial effects:

[0056] The present invention adopts modular design and quick connection modules to flexibly assemble and replace battery stack systems of different power and capacity, which is convenient for large-scale production and expansion; in order to improve energy density, the scheme adopts an optimized circulation system and a thermal management module; the circulation system improves the liquid circulation performance through an axial vortex pump to ensure the uniform and stable flow of the liquid electrolyte; the thermal management module realizes the temperature control of the flow battery stack through the battery stack temperature control system, keeps the operating temperature of the battery stack within an appropriate range, and improves the energy conversion efficiency; in order to shorten the construction time, the scheme adopts a standardized design and modular structure; the quick connection module is used to realize the flexible assembly and replacement of battery stack systems of different power and capacity, shortening the construction time and improving the construction efficiency; in order to improve the operation and maintenance efficiency, the scheme introduces an intelligent management module; the intelligent management module optimizes the scheduling and energy management of the energy storage system through an adaptive optimization algorithm based on deep learning. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative work, among which:

[0058] Figure 1 This is a framework diagram of a standardized liquid flow battery stack system cabinet of the present invention;

[0059] Figure 2 Schematic diagram of the process steps of the fluid dynamics CFD numerical simulation method of the present invention;

[0060] Figure 3 A forward mechanical schematic diagram of a standardized liquid flow battery stack system cabinet of the present invention;

[0061] Figure 4 It is a principle step diagram of the battery stack operation analysis algorithm of the present invention;

[0062] Figure 5 A working method step diagram of the linkage control method of the present invention;

[0063] Numbers in the figure are: 1. cabinet; 2. cabinet door; 3.1 negative electrode liquid inlet; 3.2 negative electrode liquid outlet; 3.3 positive electrode liquid inlet; 3.4 positive electrode liquid outlet; 3.5 negative electrode electrolyte main line; 3.6 positive electrode electrolyte main line; 3.7 connecting pipeline; 4.1 air conditioning system; 4.2 heat exchanger; 5. battery management system; 6. negative electrode circulation pump; 7. positive electrode circulation pump; 8. valve; 9. base; 10. collecting tank; 11. lifting ring. DETAILED DESCRIPTION

[0064] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0065] like Figure 1-Figure 5 As shown, a standardized liquid flow battery stack system cabinet includes:

[0066] A cabinet 1 is provided with a cabinet door 2 that can be opened or closed, a base 9 is provided at the bottom of the cabinet 1, a battery management system 5 and a thermal management module 4 are provided on the inner side of the cabinet 1 facing the cabinet door 2, the thermal management module 4 includes an air conditioning system 4.1 and a heat exchanger 4.2, the battery management system 5 is arranged above the air conditioning system 4.1, a circulation system 3 is provided inside the cabinet 1, and the circulation system 3 includes a negative electrode liquid inlet 3.1, a negative electrode liquid outlet 3.2, a positive electrode liquid inlet 3.3, and a positive electrode liquid outlet 3.4 , a negative electrode electrolyte main line 3.5, a positive electrode electrolyte main line 3.6 and a connecting line 3.7, the circulation system 3 is also provided with a negative electrode circulation pump 6, a positive electrode circulation pump 7 and a valve 8, the negative electrode circulation pump 6 and the positive electrode circulation pump 7 are arranged in parallel and control the valve 8 respectively, a collecting tank 10 is embedded in the base 9, and the collecting tank 10 is arranged to accommodate the negative electrode electrolyte main line 3.5 and the positive electrode electrolyte main line 3.6, and the negative electrode electrolyte main line 3.5 and the positive electrode electrolyte main line 3.6 are connected to the valve 8;

[0067] In a specific embodiment, the liquid flow battery power module designed by the present invention has a high degree of standardization. The base 9 is provided with a high-strength bracket as the cabinet base, which is used to place the electrolyte main line and the liquid collecting tank. The internal circulation system of the cabinet is connected to the main line at the bottom of the cabinet by a flexible joint, and a valve is provided at the connection, which is conducive to shortening the construction period and improving the operation and maintenance efficiency. In addition, the present invention can complete the assembly of multiple standardized liquid flow battery stack system cabinets through building block splicing, meet the different requirements of different projects for the power unit of the liquid flow battery energy storage system, and shorten the time of customized design of each project.

[0068] In a specific embodiment, the cables in the cabinet are gathered and then connected to the buried cables from the bottom of the cabinet, and are connected to the PCS. A lifting ring 11 is also provided above the cabinet 1.

[0069] The thermal management module 4 is used to realize the temperature control of each battery stack; the thermal management module also realizes the temperature control of the flow battery stack through the battery stack temperature control system;

[0070] A circulation system is used to ensure that the flow of liquid electrolyte is uniform and stable; the circulation system improves the liquid circulation performance through an axial vortex pump; the axial vortex pump optimizes the design of the liquid circulation system through a fluid dynamics CFD numerical simulation method; a battery management system is used to monitor and manage the entire liquid flow battery energy storage system; the battery management system realizes the monitoring and management functions of the liquid flow battery energy storage system through a sensor network and a battery stack operation analysis algorithm;

[0071] The cabinet 1 is also provided with:

[0072] A quick connection module is used to realize flexible assembly and replacement of battery stack systems with different powers and capacities; the quick connection module realizes flexible assembly and replacement of battery stacks with different powers and capacities through data communication protocols and electric energy transmission methods;

[0073] An intelligent management module is used to optimize the scheduling and energy management of the energy storage system; the intelligent management module realizes the optimization scheduling and energy management functions of the energy storage system through an adaptive optimization algorithm based on deep learning;

[0074] The output ends of the thermal management module and the circulation system are connected to the input end of the battery management system; the output ends of the quick connection module and the battery management system are connected to the input end of the intelligent management module.

[0075] In a specific embodiment, Figure 3 As shown: The working principle steps of the present invention are as follows:

[0076] E1. Installation and assembly of flow battery stack support module: Multiple flow battery stacks are placed in the flow battery stack support module, and the stacks are installed and assembled through structural design, material selection and mechanical analysis methods. The flow battery stack support module is usually composed of multiple stacks to achieve the required voltage, capacity and power. The stacks are connected by conductive pipes to form a complete electron transmission path.

[0077] E2. The containment and isolation function of the cabinet structure design module: The liquid flow battery stack bracket module is placed in the cabinet structure design module, and the containment and isolation function of the liquid flow battery stack system cabinet is realized through sealing design and modular design methods. The cabinet structure design module mainly includes components such as cabinet shell, seals and partitions. The cabinet shell is made of high-strength materials and can withstand various natural disasters and man-made damage in the atmospheric environment. Seals and partitions can effectively isolate the liquid electrolyte between the stacks and reduce the risk of liquid leakage and mixing; a bracket is provided in the cabinet 1 for placing the stack and fixing the circulation system, and a lifting ring is provided on the outside of the top of the cabinet 1 to facilitate the installation and transportation of the stack cabinet.

[0078] E3. Temperature control of thermal management module: The temperature of the flow battery stack is controlled by the stack temperature control system to keep the stack operating temperature within a suitable range and improve the energy conversion efficiency. The thermal management module usually includes components such as temperature sensors, electric fans, and heaters. The temperature sensor can monitor the temperature changes of the stack in real time, and the electric fan and heater can adjust the temperature of the stack according to the monitoring results; the thermal management module includes an air conditioning system 4.1 and a heat exchanger 4.2 to ensure that the standardized flow battery stack system cabinet operates at a suitable temperature. The air conditioning system 4.1 is placed at the lower inside of the cabinet door 2; the heat exchanger 4.2 is located in the cabinet 1, and is set at the connecting pipe 3.7 between the positive 6 and negative electrode circulation pumps 7 inlet and the valve 8, and the connecting pipe connected to the positive 3.6 and negative electrode electrolyte 3.5 outlets and the connecting pipe between the valve 8.

[0079] E4. Uniform and stable flow in the circulation system: The circulation system usually uses an axial vortex pump to improve the performance of liquid circulation. The axial vortex pump is a specially designed pump that generates vortices by rotating the impeller to circulate the liquid. The liquid will generate heat after chemical reactions inside the battery stack. If there is no circulation system to cool and distribute the heat, the battery stack may overheat, affecting the performance and life of the battery.

[0080] The axial vortex pump optimizes the design of the liquid circulation system through the fluid dynamics CFD (Computational Fluid Dynamics) numerical simulation method. The CFD method uses mathematical models and calculation methods to simulate and analyze the movement and behavior of fluids. Through CFD simulation, the flow path of the liquid, the impeller design of the pump, and the liquid circulation speed can be optimized to achieve better circulation effects.

[0081] E5. Monitoring and management of the battery management system: The monitoring and management functions of the flow battery energy storage system are realized through the sensor network and the battery stack operation analysis algorithm, and the entire energy storage system is monitored, analyzed and dispatched in real time. The battery management system usually includes components such as sensors, data collectors, and data processors. The sensor can monitor the voltage, current, temperature and other parameters of the battery stack in real time, and the data collector can collect the data generated by the sensor and transmit it to the data processor. The data processor can analyze and process the data, generate reports and realize intelligent dispatch control of the battery stack; the battery management system is placed on the upper inside of the cabinet door, and is connected to the battery stack 206, air conditioning system 4.1, heat exchanger 4.2, circulation pump, sensor and other electrical components through cables. It is mainly used to control the charging and discharging of the battery stack, the human management module and the circulation system.

[0082] E6. Flexible assembly and replacement of quick-connect modules: After the stacks in the standardized liquid flow battery stack system cabinet 1 are connected in series, the cables are connected to the buried cables from the bottom of the cabinet. The standardized liquid flow battery stack system cabinet 1 and the standardized liquid flow battery stack system cabinet n are connected in parallel to the PCS in the DC junction cabinet; the energy storage system battery management system controls each standardized liquid flow battery stack system cabinet as a whole through Ethernet; the flexible assembly and replacement of stacks of different powers and capacities are achieved through data communication protocols and power transmission methods, which is convenient for large-scale production and expansion.

[0083] E7. Optimal scheduling and energy management of the intelligent management module: Through the adaptive optimization algorithm based on deep learning, the optimal scheduling and energy management functions of the energy storage system are realized to improve the energy efficiency and stability of the system.

[0084] In the above embodiment, the temperature control system of the battery stack includes a temperature sensing module, a temperature control module and a multi-stage heat pipe module; the temperature sensing module realizes the collection and transmission of temperature data through a temperature sensor and a wireless transmission method; based on the sensor data, the temperature control module automatically adjusts the internal temperature of the battery stack through a big data analysis method; the multi-stage heat pipe module controls the internal temperature of the battery stack through a multi-stage evaporator and a condenser; the multi-stage evaporator is connected to the condenser through a heat pipe to form a heat transfer network; the heat transfer network transfers heat to the outside for heat dissipation through heat conduction and phase change heat transfer methods; the phase change heat transfer method absorbs or releases heat through the phase change process of the working fluid, thereby improving the heat capacity and heat transfer efficiency of the heat pipe.

[0085] In a specific embodiment, the temperature control system of the battery stack includes a plurality of temperature sensors installed at different positions of the battery stack for real-time monitoring of changes in the temperature inside the battery stack. The temperature control module therein uses a control algorithm to control the temperature inside the battery stack. When the temperature inside the battery stack reaches the set value, the temperature control module controls the multi-stage heat pipe module through a signal, starts the radiator, fan and other equipment, and dissipates the heat inside the battery stack. The multi-stage heat pipe module is an efficient heat transfer device composed of multiple heat pipes. When the temperature control module sends a signal, the multi-stage heat pipe module automatically adjusts the temperature difference of the heat pipe so that the heat inside the battery stack can be quickly transferred to the radiator, fan and other equipment for effective heat dissipation.

[0086] In a specific embodiment of a standardized liquid flow battery stack system cabinet, the stack operates within the internal temperature range of the stack temperature control system, which can improve the operating efficiency and stability of the stack. Through the monitoring and control of the temperature sensing module and the temperature control module, it can be ensured that the stack is always within the optimal operating temperature range. In addition, too high or too low temperature will affect the life and safety of the battery, so controlling the temperature change of the stack is very important for extending the service life of the battery. Secondly, the operation of the stack under too high or too low temperature conditions can easily cause the chemical reaction inside the stack to get out of control, thereby increasing the system failure rate. Through the monitoring and control of the stack temperature control system, the system failure rate can be reduced and the system reliability can be improved. At the same time, the stack temperature control system can automatically adjust the temperature inside the stack, reducing manual intervention and maintenance costs.

[0087] In the above embodiment, the working steps of the fluid dynamics CFD numerical simulation method in a standardized flow battery stack system cabinet are:

[0088] S1. Establish geometric model;

[0089] Establish a three-dimensional numerical model of the liquid circulation system using computer-aided design software;

[0090] S2, setting boundary conditions;

[0091] Determine at least the inlet flow rate, outlet pressure and wall friction resistance in the liquid circulation system through an expert system and experimental data;

[0092] S3, divide the computational grid;

[0093] The interior of the system cabinet is divided into a grid of multiple small units by using the finite volume method;

[0094] S4, solving the numerical model;

[0095] Numerical calculation is performed on the flow behavior of the liquid in the system cabinet by using the finite difference method to obtain at least flow field, pressure field and velocity field parameters;

[0096] S5. Liquid circulation performance analysis;

[0097] According to the fluid dynamics results obtained by solving, the finite element method is used to analyze at least the flow velocity distribution, vortex formation and turbulence intensity parameters of the liquid during the circulation process;

[0098] S6. Optimize design;

[0099] According to the analysis results, the reverse design function provided by the CFD software is used to adjust and optimize at least the impeller geometry, blade number and blade angle parameters of the axial vortex pump.

[0100] In a specific embodiment, in a standardized liquid flow battery stack system cabinet, the fluid dynamics CFD numerical simulation method first establishes a three-dimensional geometric model based on the geometry and component layout of the actual stack system cabinet. This model includes an axial vortex pump, a pipeline network, a battery stack and other related components. The geometric model is then divided into small grid units to form a computational grid. These grid units can be three-dimensional, tetrahedral or other shapes. The fineness of the grid division will affect the accuracy and computational efficiency of the numerical simulation results. Then, appropriate boundary conditions are set for each boundary of the computational grid, such as the inlet liquid flow rate, outlet pressure, wall friction coefficient, etc. These conditions are determined according to actual conditions. Then, according to the basic equations of fluid dynamics, such as the continuity equation, momentum equation and energy equation, the problem is transformed into a set of partial differential equations. These equations describe the flow, heat transfer and mass transfer process of the liquid in the stack system cabinet. Then, the established partial differential equations are discretized using a numerical method and converted into a set of algebraic equations. Then, the numerical solution of the system of equations is calculated using an iterative algorithm or a direct solution method. This numerical solution represents the flow and temperature distribution of the liquid in the stack system cabinet. Finally, based on the results of the numerical simulation, the flow rate, pressure, temperature distribution and other parameters of the liquid in the stack system cabinet can be obtained. By analyzing these parameters, the performance of the system can be evaluated and optimized. For example, the parameters of the vortex pump or the layout of the pipeline network can be adjusted to improve the efficiency and stability of the liquid circulation system.

[0101] In the specific implementation, through numerical simulation, we can deeply understand the flow law and heat conduction characteristics of the liquid in the battery system cabinet, so as to optimize the design of the liquid circulation system. For example, the pipeline layout can be improved and the performance of the pump can be optimized to improve the flow efficiency and thermal management ability of the liquid, thereby improving the performance of the battery stack. In addition, numerical simulation can be performed on a computer, avoiding the high cost and time consumption of traditional experimental methods. By simulating different design schemes, the system performance can be evaluated under different parameters, and the most cost-effective scheme can be selected, thereby saving R&D costs. Secondly, through numerical simulation, the working state of the liquid circulation system in the battery system cabinet can be predicted, and potential problems and risks can be identified. This helps to take appropriate measures in advance to ensure the stability and reliability of the system.

[0102] In the above embodiment, the stack operation analysis algorithm calculates the power density of the stack by using the stack power density calculation formula, and the formula expression of the stack power density calculation formula is:

[0103]

[0104] In formula (1), R is the power density of the battery stack; P is the output power of the battery stack; m is the height of the battery stack, which is used to measure the thickness of the battery stack electrode stack; O represents the effective electrode surface area, which is used to represent the total area of ​​the active area of ​​the electrochemical reaction; C represents the current density, which is used to measure the amount of current passing through the battery stack per unit area; the loss rate of the battery stack during the energy conversion process is calculated by the battery stack energy loss function; the formula expression of the battery stack energy loss function is:

[0105]

[0106] In formula (2), D represents the loss of battery state of charge; h is the input energy of the battery stack, which is used to represent the total energy received by the battery stack; v is the output energy of the battery stack, which is used to represent the total energy output by the battery stack; δ represents the current compensation coefficient of the battery stack, which is used to compare the difference between the battery stack voltage and the theoretical voltage under different working conditions; a is the efficiency factor, which is used to consider the efficiency of actual energy conversion; the efficiency of the battery stack thermal management system is evaluated by the battery stack thermal management efficiency function; the formula expression of the battery stack thermal management efficiency function is:

[0107]

[0108] In formula (3), Y represents the efficiency of the thermal management system of the fuel cell stack, which is used to express the ratio between the heat removal rate and the heat generation rate; s is the heat removal rate, which is used to measure the heat removed from the fuel cell stack; c is the heat generation rate, which is used to measure the heat generated inside the fuel cell stack; r represents the effect of external heat sources on heat, which is used to measure the additional heat input from the environment or other systems.

[0109] In a specific embodiment, the working principle of the battery stack operation analysis algorithm is as follows:

[0110] D1. Data acquisition: The key parameters of the battery stack system cabinet, such as voltage, current, temperature, pressure, etc., are acquired through the sensor network and recorded in the form of a time series.

[0111] D2. Data preprocessing: Preprocess the collected data, including data cleaning, outlier detection and denoising, to ensure the accuracy and reliability of subsequent analysis.

[0112] D3. Feature extraction: Extract meaningful features from the preprocessed data, such as the power change trend of the battery stack, the battery voltage change rate of the battery stack, etc. These features can reflect the operating status and performance of the battery stack.

[0113] D4. Model building: Based on the extracted features, build a suitable mathematical model or machine learning model to describe the operating status and performance of the battery stack. Common models include neural networks, support vector machines, decision trees, etc.

[0114] D5. Model training and optimization: Use historical data to train the established model, and adjust and optimize parameters to improve the accuracy and generalization ability of the model.

[0115] D6. Operation analysis: Input the real-time collected data into the trained model to perform stack operation analysis. The model will predict the stack's operating status, performance indicators, and possible faults or abnormalities based on the input data.

[0116] D7. Result output and alarm: Based on the analysis results of the model, the judgment and prediction results are output to the battery management system. If the battery stack is found to be abnormal or has potential faults, the battery management system will promptly issue an alarm signal and take corresponding measures, such as cutting off the power supply or sending a maintenance request.

[0117] In a standardized flow battery stack system cabinet, the stack operation analysis algorithm can promptly detect potential faults or abnormal conditions by analyzing the stack operation data, and issue timely warnings to avoid further expansion of faults and cause more serious losses. In addition, the stack operation analysis algorithm can evaluate the operating status and performance indicators of the stack, help operators understand the working conditions of the stack, and make optimization adjustments based on the evaluation results to improve the efficiency and stability of the stack. Secondly, by analyzing the stack operation data, the maintenance needs and life of the stack can be predicted, helping to formulate reasonable maintenance plans and management strategies, reducing maintenance costs and improving system reliability. In the specific implementation, the test data table of the stack operation analysis algorithm is shown in Table 1:

[0118] Table 1 Test data table of stack operation analysis algorithm

[0119]

[0120] In Data Table 1, the timestamp records the time point of data collection, expressed in hours and minutes. Battery voltage (V) indicates the voltage value of the battery in the battery stack, in volts (V). In this example, the battery voltage gradually decreases from 50.2V to 48.7V. Battery current (A) indicates the current value of the battery in the battery stack, in amperes (A). In this example, the battery current fluctuates from 10A to between 9.5A and 10.5A. Temperature (℃) indicates the temperature value of the battery stack, in degrees Celsius (℃). In this example, the temperature gradually increases from 35℃ to 39℃. Pressure (MPa) indicates the pressure value of the battery stack, in megapascals (MPa). In this example, the pressure increases from 2.1MPa to 2.3MPa.

[0121] In a standardized flow battery stack system cabinet, the operating hardware environment of the stack operation analysis algorithm includes: a sensor network for collecting stack operation data, including current, voltage, temperature, etc. A battery management system, which is responsible for receiving sensor data and invoking the stack operation analysis algorithm for processing and management. A processor, which is used to perform the calculation tasks of the stack operation analysis algorithm and can be an embedded processor or a general-purpose computer. A memory, which is used to store the code and data of the stack operation analysis algorithm.

[0122] In the above embodiment, the quick connection module includes a communication unit, an energy transmission unit and a control unit; the communication unit transmits the status information of each battery stack to the system controller for management and monitoring through the bus communication protocol CAN; the energy transmission unit realizes the energy transmission between battery stack systems of different power and capacity through a modular battery connector; the modular battery connector realizes modular assembly and replacement through a plug-in interface; the control unit realizes the linkage control between battery stack systems of different power and capacity through a linkage control method; the working steps of the linkage control method in a standardized liquid flow battery stack system cabinet are as follows:

[0123] R1, system initialization;

[0124] Connecting the battery stack system and the quick connection module to the control host via Ethernet to achieve data exchange and command transmission; the control host reads the power and capacity information of each battery stack system via a data communication protocol;

[0125] R2, power matching and regulation;

[0126] Adjust the output power of the battery stack system by pulse width modulation method;

[0127] R3, capacity matching and adjustment;

[0128] The liquid flow distribution ratio between the stack systems is adjusted by a liquid flow control method; the liquid flow control method controls the output capacity of the stack system by adjusting the opening degree of the valve through a motor;

[0129] R4, real-time monitoring;

[0130] The output power and capacity of each battery stack system are monitored in real time through current sensors and liquid level sensors;

[0131] R5, optimization control;

[0132] Based on the monitored information, the fuel cell system is adjusted and controlled through feedback control methods.

[0133] In a specific embodiment, the quick connection module communicates with other modules in the flow battery stack through a communication unit, including transmitting power data, control instructions, etc. The communication unit adopts standardized communication protocols and interfaces to ensure compatibility and reliability with other devices. The power transmission unit realizes power transmission in the flow battery stack, including adjustment of DC voltage, stabilization of current, etc. The power transmission unit adopts efficient power electronic devices to achieve efficient transmission and conversion of electric energy. The control unit is responsible for operation control and fault protection in the flow battery stack, including parameter monitoring, status detection, fault diagnosis, etc. The control unit adopts advanced control algorithms and technologies to ensure the normal operation and safety of the system.

[0134] In a standardized liquid flow battery stack system cabinet, the communication unit and control unit of the quick connection module can monitor the operating parameters and status of the system in real time, detect faults in time and take corresponding measures, thereby improving the reliability and stability of the system. In addition, the power transmission unit of the quick connection module adopts efficient power electronic devices, which can achieve efficient conversion and transmission during power transmission, thereby improving the efficiency of power transmission and energy saving. At the same time, the quick connection module adopts standardized communication protocols and interfaces to facilitate the rapid access and disassembly of the equipment, thereby improving the flexibility and maintainability of the equipment. Secondly, the quick connection module adopts standardized communication protocols and interfaces, which can be compatible and integrated with other devices, reducing the cost and time of system integration.

[0135] In the above embodiment, the data adaptive optimization algorithm based on deep learning monitors and controls the battery in real time through a deep learning model; the deep learning model calculates the battery state vector and error covariance matrix at the current moment through a battery state estimation formula; the expression of the battery state estimation formula is:

[0136]

[0137] In formula (4), M represents the battery state vector at the current moment estimated based on the state at the previous moment, x is the Kalman gain, which is used to update the state estimation and error covariance matrix according to the measured value; y is the observation noise covariance matrix, which is used to represent the error between the observed value and the true value; t represents the state transition vector, which is used to measure the change of the battery state within the time interval; u represents the prediction error covariance matrix, which is used to calculate the error between the predicted value and the true value; z represents the observation matrix, which is used to measure the relationship between the measured value and the battery state; the load is predicted by the load prediction formula to further optimize the scheduling and energy management functions; the formula expression of the load prediction formula is:

[0138]

[0139] In formula (5), S represents the load data at the previous moment; K represents the length of historical data, which is used to use the load data of the previous β moments as input; d represents the load data at the next moment predicted based on the load data at the current moment; the historical data is analyzed and learned through the adaptive charge and discharge strategy optimization formula, and the charge and discharge strategy is further automatically adjusted; the formula expression of the adaptive charge and discharge strategy optimization formula is:

[0140]

[0141] In formula (6), F represents the charge and discharge strategy vector at the vth moment; a represents the charge and discharge strategy vector at the vth moment; θ represents the utility function, which is used to represent the benefit value obtained by adopting the charge and discharge strategy vector a under the current load condition; and v represents the load data, which is used to maximize the utility function value.

[0142] In a specific embodiment, the adaptive optimization algorithm based on deep learning first obtains the key parameters of the battery stack system cabinet, such as voltage, current, temperature, state of the energy storage system, etc., through the sensor network, and records them in the form of a time series. Then, the collected data is preprocessed, including data cleaning, outlier detection and denoising. Then, meaningful features are extracted from the preprocessed data, such as the power change trend of the battery stack, the battery voltage change rate of the battery stack, etc. Then, the adaptive optimization algorithm based on deep learning uses a neural network to build a model. The model has multiple hidden layers and a large number of adjustable parameters, and can learn the complex mapping relationship between input features and output optimization scheduling strategies. Then, the constructed deep learning model is trained using historical data, and the parameters are adjusted and optimized to improve the prediction and generalization capabilities of the model. Then, the real-time collected data is input into the trained deep learning model, and the model will predict the optimal energy storage system scheduling strategy based on the input data to achieve optimal scheduling and energy management of the battery stack. This includes the control of the optimal charging and discharging timing, power distribution and energy storage status. Finally, based on the optimized scheduling strategy output by the deep learning model, the intelligent management module will continuously and dynamically adjust the operating parameters of the energy storage system and provide feedback to the deep learning model based on actual conditions to further optimize the performance of the model.

[0143] In a standardized liquid flow battery stack system cabinet, the adaptive optimization algorithm based on deep learning automatically adjusts the working state and energy distribution of the energy storage system according to the real-time operation status of the stack and external environmental conditions, thereby improving energy utilization efficiency and reducing energy consumption costs. Through the optimization scheduling strategy of the deep learning model, dynamic control and adjustment of the stack can be achieved to keep it within a safe and stable working range to prevent problems such as overload, over-discharge, and overtemperature. In addition, the algorithm based on deep learning has strong data modeling and prediction capabilities, which can accurately predict the changing trend of the stack operation status and adjust the operating parameters of the energy storage system in time to adapt to changes in different loads, weather, etc. In the specific implementation, the test data table of the adaptive optimization algorithm based on deep learning is shown in Table 2:

[0144] Table 2 Test data of adaptive optimization algorithm based on deep learning

[0145]

[0146] In Data Table 2, the predicted power demand (kW) indicates the power demand of the battery stack system at each time point predicted by the deep learning algorithm model, in kilowatts (kW). The actual power distribution (kW) shows the power value actually distributed by the battery stack system at each time point, in kilowatts (kW). Through these data, we can see the changes in the current, voltage, and temperature values ​​of the battery stack system at different time points, and the predicted power demand and actual power distribution are also recorded. Based on this data, the deep learning algorithm can predict the power demand, and the system can distribute the actual power according to the prediction results, thereby realizing the optimal scheduling and energy management of the energy storage system.

[0147] In a standardized liquid flow battery stack system cabinet, the operating hardware environment of the deep learning-based adaptive optimization algorithm includes: a processor, which is used to perform the computing tasks of the deep learning algorithm model, which can be a GPU or a dedicated AI chip. A memory, which is used to store the weight parameters and training data of the deep learning algorithm model. A network communication module, which is responsible for communicating with other devices, such as receiving sensor data and sending control instructions. A battery management system, which is responsible for receiving sensor data and calling the deep learning algorithm model for optimization scheduling and energy management. In the specific implementation, the data test comparison table of the deep learning-based adaptive optimization algorithm and the traditional algorithm is shown in Table 3:

[0148] Table 3 Comparative test table of adaptive optimization algorithms based on deep learning

[0149]

[0150] In data table 3, the battery voltage of the traditional algorithm at 12:00 is 350V, while that of the deep learning-based algorithm is 355V. As time goes by, the battery voltage of both algorithms shows an increasing trend, but the deep learning-based algorithm always maintains a higher voltage value. The battery current of the traditional algorithm at 12:00 is 10A, while that of the deep learning-based algorithm is 12A. As time goes by, the battery current of both algorithms fluctuates, but the deep learning-based algorithm always maintains a higher current value. The battery temperature of the traditional algorithm at 12:00 is 25℃, while that of the deep learning-based algorithm is 24℃. As time goes by, the battery temperature of both algorithms rises slightly, but the deep learning-based algorithm always maintains a lower temperature value. The power of the traditional algorithm at 12:00 is 500W, while that of the deep learning-based algorithm is 540W. As time goes by, the power of both algorithms fluctuates, but the deep learning-based algorithm always maintains a higher power value.

[0151] By comparing the output results of the two algorithms, we can see that the adaptive optimization algorithm based on deep learning achieves higher charging power and better adapts to the actual needs of the battery. At the same time, the algorithm also makes the fluctuations of battery voltage, current and temperature more stable, indicating that the algorithm can effectively improve the stability and service life of the battery.

[0152] In the above embodiment, the circulation system includes a positive electrode circulation pump 7, a negative electrode circulation pump 6, a connecting pipe 3.7, a valve 8, a positive electrode liquid inlet 3.3, a negative electrode liquid inlet 3.1, a positive electrode liquid outlet 3.4, a negative electrode liquid outlet 3.2, a positive electrode electrolyte main line 3.6, a negative electrode electrolyte main line 3.5 and a liquid collecting tank 10. The inlet of the positive electrode circulation pump 7 is connected to the positive electrode electrolyte main line 3.6, and a valve 8 is provided in the connecting pipe between the two. The outlet of the positive electrode circulation pump 7 is connected to the positive electrode liquid inlet 3.3 of each battery stack through a connecting pipe, and the positive electrode liquid outlet 3.4 of each battery stack is connected to the positive electrode electrolyte main line 3.6 through a connecting pipe, and a valve 8 is provided in the connecting pipe between the two, forming a positive electrode circulation system loop; the inlet of the negative electrode circulation pump 6 is connected to the negative electrode electrolyte main line 3.5, and a valve 8 is provided in the connecting pipe between the two, and the outlet of the negative electrode circulation pump 6 is connected to the negative electrode liquid inlet 3.1 of each battery stack through a connecting pipe, and the negative electrode liquid outlet 3.2 of each battery stack is connected to the negative electrode electrolyte main line 3.5 through a connecting pipe, and a valve 8 is provided in the connecting pipe between the two, forming a negative electrode circulation system loop; wherein the valve 8, the positive electrode liquid inlet 3.3, the negative electrode liquid inlet 3.1, the positive electrode liquid outlet 3.4, and the negative electrode liquid outlet 3.2 are all connected to the connecting pipe with a flexible joint.

[0153] In a specific embodiment, the circulation system is composed of a positive electrode circulation pump, a negative electrode circulation pump, connecting pipes, valves, and a liquid collecting tank. The positive electrode circulation pump is connected to the positive electrode electrolyte main line, and the positive electrode electrolyte is circulated to the positive electrode inlet of each battery stack through the connecting pipes and valves, and the electrolyte at the positive electrode outlet is refluxed to the positive electrode electrolyte main line. The negative electrode circulation pump is connected to the negative electrode electrolyte main line, and the negative electrode electrolyte is circulated to the negative electrode inlet of each battery stack through the connecting pipes and valves, and the electrolyte at the negative electrode outlet is refluxed to the negative electrode electrolyte main line. Flexible joints are used to connect valves, inlets, and outlets to adapt to vibration and deformation during system operation.

[0154] In a standardized flow battery stack system cabinet, the positive electrode circulation pump of the circulation system draws the positive electrode electrolyte from the positive electrode electrolyte main line, and enters the positive electrode inlet of each stack through the connecting pipe and valve. In the stack, the positive electrode electrolyte reacts chemically with the positive electrode to generate heat. The heat is transferred to the electrolyte in the circulation system through the stack shell and the heat conductive material. After the heat is dissipated, the electrolyte enters the positive electrode electrolyte main line again to form a cycle.

[0155] The negative electrode circulation pump of the circulation system draws the negative electrode electrolyte from the negative electrode electrolyte main line, and enters the negative electrode liquid inlet of each stack through the connecting pipe and valve. In the stack, the negative electrode electrolyte reacts chemically with the negative electrode, also generating heat. The heat is transferred to the electrolyte in the circulation system through the stack shell and the heat conductive material. After the heat is dissipated, the electrolyte enters the negative electrode electrolyte main line again to form a cycle.

[0156] In specific implementation, the circulation system circulates the electrolyte to effectively dissipate heat, avoid overheating of the battery stack, maintain the temperature of the battery stack within an appropriate range, and improve the working efficiency and life of the battery. In addition, the circulation system ensures the uniform circulation of the electrolyte, so that the active substances in the electrolyte are fully contacted, improves the reaction efficiency, reduces concentration polarization and battery performance degradation. Secondly, the flexible joint can adapt to the vibration and deformation of the system during operation, reduce the risk of leakage and breakage, and improve the reliability and safety of the system.

[0157] Although the specific embodiments of the present invention are described above, it should be understood by those skilled in the art that these specific embodiments are only illustrative, and those skilled in the art may omit, replace, and change the details of the above methods and systems in various ways without departing from the principles and essence of the present invention. For example, merging the above method steps so as to perform substantially the same functions in substantially the same manner to achieve substantially the same results is within the scope of the present invention. Therefore, the scope of the present invention is limited only by the appended claims.

Claims

1. A standardized flow battery stack system cabinet, characterized by: include: A cabinet (1), wherein the cabinet (1) is provided with a cabinet door (2) that can be opened or closed, and a base (9) is provided at the bottom of the cabinet (1). A battery management system (5) and a thermal management module (4) are provided on the inner side of the cabinet (1) facing the cabinet door (2), wherein the thermal management module (4) comprises an air conditioning system (4.1) and a heat exchanger (4.2), and the battery management system (5) is arranged above the air conditioning system (4.1). A circulation system (3) is provided inside the cabinet (1), and the circulation system (3) comprises a negative electrode liquid inlet (3.1), a negative electrode liquid outlet (3.2), a positive electrode liquid inlet (3.3), a positive electrode liquid outlet (3.4), a negative electrode electrolyte main line (3.5), and a positive electrode electrolyte main line (3.6). 6) and a connecting pipeline (3.7), the circulation system (3) is also provided with a negative electrode circulation pump (6), a positive electrode circulation pump (7) and a valve (8), the negative electrode circulation pump (6) and the positive electrode circulation pump (7) are arranged in parallel and control the valve (8) respectively, the collecting tank (10) is embedded in the base (9), and the collecting tank (10) is arranged to accommodate the negative electrode electrolyte main line (3.5) and the positive electrode electrolyte main line (3.6), and the negative electrode electrolyte main line (3.5) and the positive electrode electrolyte main line (3.6) are connected to the valve (8); wherein: a thermal management module (4) is used to realize temperature control of each battery stack; the thermal management module realizes temperature control of the flow battery battery stack through the battery stack temperature control system; A circulation system (3) is used to ensure that the flow of the liquid electrolyte is uniform and stable; the circulation system improves the liquid circulation performance by an axial vortex pump; the axial vortex pump optimizes the design of the liquid circulation system by a fluid dynamics CFD numerical simulation method; A battery management system (5) is used to monitor and manage the entire liquid flow battery energy storage system; the battery management system realizes the monitoring and management functions of the liquid flow battery energy storage system through a sensor network and a battery stack operation analysis algorithm; The cabinet (1) is also provided with: A quick connection module is used to realize flexible assembly and replacement of battery stack systems with different powers and capacities; the quick connection module realizes flexible assembly and replacement of battery stacks with different powers and capacities through data communication protocols and electric energy transmission methods; An intelligent management module is used to optimize the scheduling and energy management of the energy storage system; the intelligent management module realizes the optimization scheduling and energy management functions of the energy storage system through an adaptive optimization algorithm based on deep learning; The output ends of the thermal management module (4) and the circulation system (3) are connected to the input end of the battery management system; the output ends of the quick connection module and the battery management system (5) are connected to the input end of the intelligent management module; The stack operation analysis algorithm calculates the power density of the stack by using the stack power density calculation formula, and the formula expression of the stack power density calculation formula is: In formula (1), R is the power density of the battery stack; P is the output power of the battery stack; m is the height of the battery stack, which is used to measure the thickness of the electrode stack; O represents the effective electrode surface area, which is used to represent the total area of ​​the active area of ​​the electrochemical reaction; C represents the current density, which is used to measure the amount of current passing through the battery stack per unit area; the loss rate of the battery stack during the energy conversion process is calculated by the battery stack energy loss function; the formula expression of the battery stack energy loss function is: In formula (2), D represents the loss of battery state of charge; h represents the input energy of the battery stack, which is used to represent the total energy received by the battery stack; v represents the output energy of the battery stack, which is used to represent the total energy output by the battery stack; represents the stack compensation coefficient at the current moment, which is used to compare the difference between the stack voltage and the theoretical voltage under different working conditions; a is the efficiency factor, which is used to consider the efficiency of actual energy conversion; the efficiency of the stack thermal management system is evaluated by the stack thermal management efficiency function; the formula expression of the stack thermal management efficiency function is: In formula (3), Y represents the efficiency of the thermal management system of the fuel cell stack, which is used to express the ratio between the heat removal rate and the heat generation rate; s is the heat removal rate, which is used to measure the heat removed from the fuel cell stack; c is the heat generation rate, which is used to measure the heat generated inside the fuel cell stack; r represents the effect of external heat sources on heat, which is used to measure the additional heat input from the environment or other systems; The data adaptive optimization algorithm based on deep learning monitors and controls the battery in real time through a deep learning model; the deep learning model calculates the battery state vector and error covariance matrix at the current moment through a battery state estimation formula; the expression of the battery state estimation formula is: In formula (4), M represents the battery state vector at the current moment estimated based on the state at the previous moment, x is the Kalman gain, which is used to update the state estimation and error covariance matrix according to the measured value; y is the observation noise covariance matrix, which is used to represent the error between the observed value and the true value; t represents the state transition vector, which is used to measure the change of the battery state within the time interval; u represents the prediction error covariance matrix, which is used to calculate the error between the predicted value and the true value; z represents the observation matrix, which is used to measure the relationship between the measured value and the battery state; the load is predicted by the load prediction formula to further optimize the scheduling and energy management functions; the formula expression of the load prediction formula is: In formula (5), S represents the load data at the previous moment; K represents the length of historical data, which is used to convert the load data before use into The load data at a certain moment is used as input; d represents the load data at the next moment predicted based on the load data at the current moment; the historical data is analyzed and learned through the adaptive charge and discharge strategy optimization formula, and the charge and discharge strategy is further automatically adjusted; the formula expression of the adaptive charge and discharge strategy optimization formula is: In formula (6), represents the utility function, which is used to represent the benefit value obtained by adopting the charging and discharging strategy vector a under the current load condition; v is the load data, which is used to maximize the utility function value.

2. A standardized liquid flow battery stack system cabinet according to claim 1, characterized in that: The stack temperature control system includes a temperature sensing module, a temperature control module and a multi-stage heat pipe module; the temperature sensing module realizes the collection and transmission of temperature data through a temperature sensor and a wireless transmission method; the temperature control module automatically adjusts the internal temperature of the stack through a big data analysis method; the multi-stage heat pipe module controls the internal temperature of the stack through a multi-stage evaporator and a condenser; the multi-stage evaporator is connected to the condenser through a heat pipe to form a heat transfer network; the heat transfer network transfers heat to the outside for heat dissipation through heat conduction and phase change heat transfer methods; the phase change heat transfer method absorbs or releases heat through the phase change process of the working fluid, thereby improving the heat capacity and heat transfer efficiency of the heat pipe.

3. A standardized liquid flow battery stack system cabinet according to claim 1, characterized in that: The working steps of the fluid dynamics CFD numerical simulation method in a standardized flow battery stack system cabinet are as follows: S1. Establish geometric model; Establish a three-dimensional numerical model of the liquid circulation system using computer-aided design software; S2, setting boundary conditions; Determine at least the inlet flow rate, outlet pressure and wall friction resistance in the liquid circulation system through an expert system and experimental data; S3, divide the computational grid; The interior of the system cabinet is divided into a grid of multiple small units by using the finite volume method; S4, solving the numerical model; Numerical calculation is performed on the flow behavior of the liquid in the system cabinet by using the finite difference method to obtain at least flow field, pressure field and velocity field parameters; S5. Liquid circulation performance analysis; According to the fluid dynamics results obtained by solving, the finite element method is used to analyze at least the flow velocity distribution, vortex formation and turbulence intensity parameters of the liquid during the circulation process; S6. Optimize design; According to the analysis results, the reverse design function provided by the CFD software is used to adjust and optimize at least the impeller geometry, blade number and blade angle parameters of the axial vortex pump.

4. A standardized liquid flow battery stack system cabinet according to claim 1, characterized in that: The quick connection module includes a communication unit, an energy transmission unit and a control unit; the communication unit transmits the status information of each battery stack to the system controller for management and monitoring through the bus communication protocol CAN; the energy transmission unit realizes the energy transmission between battery stack systems with different power and capacity through a modular battery connector; the modular battery connector realizes modular assembly and replacement through a plug-in interface; the control unit realizes the linkage control between battery stack systems with different power and capacity through a linkage control method; the working steps of the linkage control method in a standardized liquid flow battery stack system cabinet are as follows: R1, system initialization; Connecting the battery stack system and the quick connection module to the control host via Ethernet to achieve data exchange and command transmission; the control host reads the power and capacity information of each battery stack system via a data communication protocol; R2, power matching and regulation; Adjust the output power of the battery stack system by pulse width modulation method; R3, capacity matching and adjustment; The liquid flow distribution ratio between the stack systems is adjusted by a liquid flow control method; the liquid flow control method controls the output capacity of the stack system by adjusting the opening degree of the valve through a motor; R4, real-time monitoring; The output power and capacity of each battery stack system are monitored in real time through current sensors and liquid level sensors; R5, optimization control; Based on the monitored information, the fuel cell system is adjusted and controlled through feedback control methods.

5. A standardized liquid flow battery stack system cabinet according to claim 1, characterized in that: The inlet of the positive electrode circulation pump (7) is connected to the positive electrode electrolyte main line (3.6), and a valve (8) is provided in the connecting pipe between the two. The outlet of the positive electrode circulation pump (7) is connected to the positive electrode liquid inlet (3.3) of each battery stack through a connecting pipe, and the positive electrode liquid outlet (3.4) of each battery stack is connected to the positive electrode electrolyte main line (3.6) through the connecting pipe (3.7), and the valve (8) is provided in the connecting pipe between the two to form a positive electrode circulation system loop; the inlet of the negative electrode circulation pump (6) is connected to the negative electrode electrolyte main line (3.5), and the connecting pipe between the two The valve (8) is provided in the middle, the outlet of the negative electrode circulation pump (6) is connected to the negative electrode liquid inlet (3.1) of each battery stack through the connecting pipe, and the negative electrode liquid outlet (3.2) of each battery stack is connected to the negative electrode electrolyte main pipeline (3.5) through the connecting pipe, and the valve (8) is provided in the connecting pipes of the two to form a negative electrode circulation system loop; wherein the valve (8), the positive electrode liquid inlet (3.3), the negative electrode liquid inlet (3.1), the positive electrode liquid outlet (3.4), and the negative electrode liquid outlet (3.2) are all connected to the connecting pipeline by a flexible joint.

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