Intelligent management method and system of flow battery

Abstract

The invention relates to the technical field of batteries, and discloses an intelligent management method and system for a flow battery, and the method comprises the steps: calculating the real-time heat production power of an electric pile through a heat production model, and obtaining the real-time heat production efficiency; dynamically updating the fusion weight of the ampere-hour integral method SOC and the calibration SOC of the flow battery by combining the real-time heat production efficiency, the reference heat production efficiency and the temperature data; fusing the ampere-hour integral method SOC and the calibration SOC of the flow battery based on the updated fusion weight to obtain a fused flow battery SOC; dynamically adjusting a maintenance period threshold value based on the SOC estimation deviation of the flow battery and the heat production efficiency attenuation rate; and generating a maintenance work order based on the adjusted maintenance period threshold value, and updating the parameters, the reference heat production efficiency, the initial fusion weight and the initial maintenance period threshold value of the heat production model after the maintenance operation is completed. Through deep interaction of the three functions, all-around and refined intelligent management of the flow battery is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of battery, in particular to an intelligent management method and system of flow battery. BACKGROUND

[0002] Flow battery has become one of the preferred technologies for large-scale energy storage power station due to its inherent safety, long cycle life and decoupling of power and capacity. However, in the actual engineering application process, its performance, efficiency and life still face three major challenges: first, there is a lag in thermal management; second, the state estimation accuracy is not enough; third, the maintenance strategy is passive.

[0003] Moreover, in the prior art, the three key functions of thermal management, state estimation and maintenance are generally independent and lack of cooperation, which causes the management strategy to lag and be one-sided, and it is difficult to achieve optimal operation of the flow battery from the system level. There is currently a lack of an intelligent overall solution that deeply couples the three functions and has a positive synergistic effect. SUMMARY

[0004] In order to overcome the deficiencies in the prior art, the purpose of the present application is to provide an intelligent management method and system for flow battery, which realizes the paradigm change from passive response to active foresight and from local optimization to system optimization through the deep coupling and intelligent cooperation of the three functions of thermal management, state estimation and preventive maintenance, and improves the operation efficiency, reliability and full life cycle economy of the flow battery system.

[0005] In a first aspect, an intelligent management method for flow battery is provided, comprising the following steps: calculating the real-time heat generation power of the stack using a heat generation model, and then obtaining the real-time heat generation efficiency; combining the real-time heat generation efficiency, the benchmark heat generation efficiency and the temperature data to dynamically update the fusion weight of the ampere-hour integral method SOC and the calibrated SOC of the flow battery; fusing the ampere-hour integral method SOC and the calibrated SOC of the flow battery based on the updated fusion weight to obtain the fused flow battery SOC; dynamically adjusting the maintenance cycle threshold based on the estimation deviation of the flow battery SOC and the heat generation efficiency decay rate; generating a maintenance work order based on the adjusted maintenance cycle threshold, and updating the parameters of the heat generation model, the benchmark heat generation efficiency, the initial fusion weight and the initial maintenance cycle threshold after completing the maintenance operation of the flow battery.

[0006] Further, the heat generation power of the stack is calculated using the heat generation model, and then the heat generation efficiency is obtained, which specifically includes: Based on the principle of heat balance, a heat generation model is constructed: Q actual = cp ×ρ×F×ΔT, where Q actual c represents the real-time heat generation power of the fuel cell stack. p ρ and ρ represent the specific heat capacity and density of the electrolyte, respectively; F represents the real-time flow rate of the electrolyte; and ΔT represents the real-time temperature difference between the electrolyte inlet and outlet of the stack. The real-time flow rate F of the electrolyte and the real-time temperature difference ΔT between the electrolyte inlet and outlet of the fuel cell stack are obtained and input into the heat generation model to obtain the real-time heat generation power Q of the fuel cell stack. actual ; The real-time heat production efficiency is calculated using the following formula: η actual = P electrical / ( P electrical + Q actual ), where P electrical This represents the real-time electrical power of the fuel cell stack.

[0007] Furthermore, the fusion weights of the ampere-hour integral method SOC and the calibration SOC of the flow battery are dynamically updated by combining real-time heat generation efficiency, baseline heat generation efficiency, and temperature data. Specifically, this includes: When the real-time heat generation efficiency η actual The decrease exceeds the first threshold, or the current system temperature T is different from the optimal system temperature T. opt If the difference exceeds the second threshold, or if the absolute value of the current temperature change rate dT / dt exceeds the third threshold, then the fusion weighting of the ampere-hour integral SOC and the calibration SOC of the flow battery is updated. k = k base × (η actual / η base ) a × exp (- ((T - T opt ) / σ)²) × exp(-c × |dT / dt|); Where k represents the updated fusion weight, k base η represents the initial fusion weights. actual and η base These represent the real-time heat production efficiency and the baseline heat production efficiency, respectively, with a, σ, and c being preset constants.

[0008] Furthermore, the process of fusing the ampere-hour integral SOC and the calibration SOC of the flow battery based on the updated fusion weights is represented as follows: SOC fused =k×SOC ah + (1-k) × SOC ocv ; In the formula, SOC fused The SOC of the flow battery after fusion is represented by k, where k represents the updated fusion weight. ahand SOC ocv These represent the state of charge (SOC) of the flow battery using the ampere-hour integration method and the calibration SOC, respectively.

[0009] Furthermore, the maintenance cycle threshold is dynamically adjusted based on the estimation bias of the flow battery's SOC and the rate of heat generation efficiency decay, specifically including:

[0010] Record the maximum deviation ΔSOC between the ampere-hour integration SOC and the calibration SOC between two consecutive SOC calibrations. max ; Calculate the real-time heat production efficiency η actual The moving average value is used to calculate the heat production efficiency decay rate Δη / Δt; The maintenance cycle threshold is dynamically adjusted using the following formula: N set_new =N set ×[1-γ1×max(0,(ΔSOC max -ΔSOC threshold ) / ΔSOC threshold ) -γ2×max(0, (η base -η actual ) / η base )]; In the formula, N set_new N represents the adjusted maintenance cycle threshold. set ΔSOC represents the initial maintenance cycle threshold. threshold η represents the maximum permissible SOC deviation safety threshold. actual and η base γ1 and γ2 represent the real-time heat production efficiency and the baseline heat production efficiency, respectively; γ1 and γ2 are adjustable weighting coefficients, and the values ​​of γ1 and γ2 are adjusted based on the heat production efficiency decay rate Δη / Δt.

[0011] Furthermore, after completing the maintenance operations of the flow battery, the parameters of the heat generation model, the baseline heat generation efficiency, the initial fusion weights, and the initial maintenance cycle threshold are updated, specifically including: After completing the maintenance operation of the flow battery, it enters self-learning mode; In self-learning mode, a series of standardized charge and discharge power pulses are applied according to a preset program, and the actual heat generation power at different power points is collected synchronously and compared with the calculated value of the current heat generation model. The parameters of the heat generation model are adjusted so that its calculated value tends to the actual heat generation power. At the same time, the baseline heat generation efficiency, initial fusion weight and initial maintenance cycle threshold are calculated and updated based on the collected data.

[0012] Furthermore, after updating the parameters of the heat generation model, if the heat generation model shows a reduction in heat generation and a decrease in internal resistance after maintenance, the starting temperature threshold of the flow battery's cooling system should be increased to reduce auxiliary energy consumption.

[0013] Secondly, an intelligent management system for a flow battery is provided, comprising: The real-time heat generation efficiency calculation module is used to calculate the real-time heat generation power of the fuel cell stack using the heat generation model, and then obtain the real-time heat generation efficiency. The fusion weight update module is used to dynamically update the fusion weight of the ampere-hour integral method SOC and the calibration SOC of the flow battery by combining real-time heat generation efficiency, benchmark heat generation efficiency and temperature data. The flow battery SOC fusion module is used to fuse the ampere-hour integral method SOC and the calibration SOC of the flow battery based on the updated fusion weights to obtain the fused flow battery SOC. The maintenance cycle threshold adjustment module is used to dynamically adjust the maintenance cycle threshold based on the estimation deviation of the flow battery's SOC and the rate of heat generation efficiency decay. The maintenance and parameter update module is used to generate maintenance work orders based on the adjusted maintenance cycle threshold. After completing the maintenance operation of the flow battery, it updates the parameters of the heat generation model, the baseline heat generation efficiency, the initial fusion weight, and the initial maintenance cycle threshold.

[0014] Furthermore, the maintenance and parameter update module is also used to, after completing the parameter update of the heat generation model, if the heat generation model shows that the heat generation is reduced and the internal resistance is reduced after maintenance, then the start-up temperature threshold of the flow battery cooling system is increased to reduce auxiliary energy consumption.

[0015] Thirdly, an intelligent management system for a flow battery is provided, comprising: Flow battery system; Temperature sensor assembly used to collect the inlet and outlet temperatures of the positive electrode stack in a flow battery system; The flow acquisition module is used to collect the real-time flow rate of the electrolyte. The voltage and current acquisition module is used to acquire the voltage and current of the flow battery system, as well as the open-circuit voltage of the flow battery system. The control system is connected to the flow battery system, temperature sensor assembly, flow acquisition module, and voltage and current acquisition module. The control system is configured to execute the intelligent management method for the flow battery as described above.

[0016] This invention proposes an intelligent management method and system for flow batteries, which has the following beneficial effects: 1. Synergy between thermal management feedforward control and SOC estimation: The thermal efficiency calculated by thermal management is used as a key parameter for SOC estimation of flow batteries. When the thermal efficiency or temperature data fluctuates beyond the corresponding threshold, it indicates that the flow battery is aging. At this time, in the weighted fusion of the SOC relationship model of ampere-hour integral method and calibration SOC, the fusion weight is dynamically adjusted to offset the estimation error caused by aging and ensure high-precision estimation of the flow battery's SOC throughout its entire life cycle.

[0017] 2. Synergy between SOC estimation and preventative maintenance: Long-term SOC estimation data and the changing trend of the SOC curve are key indicators reflecting battery health and capacity degradation. This invention utilizes SOC estimation data to dynamically optimize the trigger maintenance cycle threshold of the maintenance module. When the system determines that capacity degradation is accelerating based on SOC data, it can automatically lower the maintenance cycle threshold, achieving more timely preventative maintenance.

[0018] 3. Synergy between preventative maintenance, thermal management, and SOC estimation: After the maintenance module performs maintenance operations, the internal state of the flow battery system is restored. At this time, the system automatically initializes or fine-tunes the heat generation model, SOC estimation, and maintenance baseline parameters, ensuring that all models can quickly adapt to the maintained system, maintain long-term accuracy, achieve multi-dimensional data-driven operation, and possess closed-loop optimization capabilities. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a flowchart of an intelligent management method for a flow battery provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of an intelligent management system for a flow battery provided in an embodiment of the present invention. In the figure, the dashed arrows represent the electrolyte flow path, and the dashed lines represent the electrical signal / control signal transmission path. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be described in detail below. Obviously, the described embodiments are merely some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other implementation methods obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0022] To address the problem of existing technologies that isolate thermal management, state estimation, and maintenance, this invention provides an intelligent management method and system for flow batteries. The core inventive concept is to break down the traditional boundaries between independent modules, allowing thermal management, state estimation, and maintenance to provide data feedback and decision-making support to each other, forming an adaptive and self-optimizing organic whole. This system-level architecture design results in an overall technical effect far exceeding the simple sum of the individual functions, producing a significant synergistic effect. Key models in this invention (such as the heat generation model, SOC fusion, and maintenance threshold) are all designed to have online learning or adaptive adjustment capabilities. For example, the heat generation model can self-correct using real-time operating data; the SOC fusion weights can dynamically change according to operating conditions; and the maintenance threshold is dynamically optimized based on historical performance data. This dynamic characteristic enables the system to adapt to the battery aging process and maintain optimal management performance at all times. The technical solution of this invention will be specifically described below with reference to specific embodiments.

[0023] Example 1

[0024] like Figure 1 As shown, this embodiment provides an intelligent management method for a flow battery, including the following steps: S1: Calculate the real-time heat generation power of the fuel cell stack using a heat generation model, and then obtain the real-time heat generation efficiency.

[0025] Specifically, step S1 includes: S11: Based on the principle of heat balance, construct a heat production model: Q actual = c p ×ρ×F×ΔT, where Q actual c represents the real-time heat generation power of the fuel cell stack. p ρ and ρ represent the specific heat capacity and density of the electrolyte, respectively; F represents the real-time flow rate of the electrolyte; and ΔT represents the real-time temperature difference between the electrolyte inlet and outlet of the stack. S12: Obtain the real-time electrolyte flow rate F and the real-time temperature difference ΔT between the electrolyte inlet and outlet of the fuel cell stack, and input them into the heat generation model to obtain the real-time heat generation power Q of the fuel cell stack. actual ; S13: The real-time heat generation efficiency is calculated using the following formula: η actual = P electrical / ( P electrical + Q actual ), where P electrical This represents the real-time electrical power of the fuel cell stack.

[0026] S2: Dynamically update the fusion weight of the ampere-hour integral method SOC and the calibration SOC of the flow battery by combining real-time heat generation efficiency, benchmark heat generation efficiency and temperature data.

[0027] Specifically, step S2 includes: When the heat production efficiency decreases or the temperature fluctuates drastically, i.e., when the real-time heat production efficiency η... actual The decrease exceeds the first threshold, or the current system temperature T is different from the optimal system temperature T. opt If the difference exceeds the second threshold, or if the absolute value of the current temperature change rate dT / dt exceeds the third threshold, then the fusion weighting of the ampere-hour integral SOC and the calibration SOC of the flow battery is updated. k = k base × (η actual / η base ) a × exp (- ((T - T opt ) / σ)²) × exp(-c × |dT / dt|); Where k represents the updated fusion weight, k base η represents the initial fusion weights. actual and η base These represent the real-time heat production efficiency and the baseline heat production efficiency, respectively, where a, σ, and c are preset constants. (η) actual / η base ) a The efficiency factor is used to evaluate the performance of the current system relative to the benchmark; exp (- ((T - T) opt () / σ)²) is the temperature factor, which is determined when T deviates from the optimal system temperature T opt When the factor value decreases, the weight is reduced; exp(-c × |dT / dt|) is the trend factor, and its weight is adjusted according to the absolute value of the temperature change rate. η base and k base This can be obtained through testing under the initial health condition of the battery system. The testing process is existing technology and will not be described in detail here.

[0028] η actual It directly reflects the efficiency of converting electrical energy into chemical energy. A decrease in its value is often an early and sensitive indicator of aging phenomena such as increased internal resistance of the fuel cell stack, decay of catalyst activity, or reduced ion conduction efficiency. By integrating it with temperature data, it can reveal changes in the internal state more effectively than a simple temperature value.

[0029] S3: Based on the updated fusion weights, the ampere-hour integral SOC of the flow battery is fused with the calibrated SOC (OCV calibration or calibration based on electrolyte physicochemical parameters) to obtain the fused flow battery SOC.

[0030] Specifically, the process of fusing the ampere-hour integral SOC and the calibration SOC of the flow battery based on the updated fusion weights is represented as follows: SOC fused =k×SOC ah+ (1-k) ×SOC ocv ;

[0031] In the formula, SOC fused The SOC of the flow battery after fusion is represented by k, where k represents the updated fusion weight. ah and SOC ocv These represent the state of charge (SOC) of the flow battery using the ampere-hour integration method and the calibration SOC, respectively.

[0032] S4: Dynamically adjust the maintenance cycle threshold based on the estimation bias of the flow battery's SOC and the rate of heat generation efficiency decay.

[0033] Specifically, step S4 includes: S41: Record the maximum deviation ΔSOC between the ampere-hour integration SOC and the calibrated SOC between two consecutive SOC calibrations. max ; S42: Calculate the real-time heat production efficiency η actual The moving average of the heat production efficiency is calculated, and its long-term decline slope is analyzed to calculate the heat production efficiency decay rate Δη / Δt. S43: The maintenance cycle threshold is dynamically adjusted using the following formula: N set_new =N set ×[1-γ1×max(0,(ΔSOC max -ΔSOC threshold ) / ΔSOC threshold ) -γ2×max(0, (η base -η actual ) / η base )]; In the formula, N set_new N represents the adjusted maintenance cycle threshold. set ΔSOC represents the initial maintenance cycle threshold. threshold η represents the maximum permissible SOC deviation safety threshold. actual and η base γ1 and γ2 represent the real-time heat production efficiency and the baseline heat production efficiency, respectively. γ1 and γ2 are adjustable weighting coefficients. The values ​​of γ1 and γ2 are adjusted based on the heat production efficiency decay rate Δη / Δt. γ1 and γ2 can be obtained by mapping based on the heat production efficiency decay rate Δη / Δt through preset mapping rules. For example, when the heat production efficiency decay rate Δη / Δt increases, γ2 can be automatically increased, making the system more sensitive to instantaneous changes in efficiency.

[0034] The maintenance cycle threshold is no longer a fixed value, but a dynamic variable jointly determined by the SOC estimation deviation and the decay of heat generation efficiency. When the SOC estimation deviation increases and the heat generation efficiency decreases significantly, the system can more confidently determine that the battery health is deteriorating rapidly, thereby proactively and accurately adjusting the maintenance cycle threshold, achieving a leap from on-time maintenance to on-demand maintenance.

[0035] S5: Generate maintenance work orders based on the adjusted maintenance cycle threshold. After completing the maintenance operation of the flow battery, update the parameters of the heat generation model, the baseline heat generation efficiency, the initial fusion weight, and the initial maintenance cycle threshold.

[0036] Specifically, when the calculated new maintenance cycle threshold is lower than the currently preset maintenance cycle threshold, a maintenance work order is automatically generated, prompting that maintenance operations such as electrolyte mixing need to be performed in advance. After completing the maintenance operations of the flow battery, the parameters of the heat generation model, the baseline heat generation efficiency, the initial fusion weight, and the initial maintenance cycle threshold are updated, including: After completing the maintenance operation of the flow battery, it enters self-learning mode; In self-learning mode, a series of standardized charge and discharge power pulses are applied according to a preset program, and the actual heat generation power at different power points is collected synchronously and compared with the calculated value of the current heat generation model. The parameters of the heat generation model are adjusted so that its calculated value tends to the actual heat generation power. At the same time, the baseline heat generation efficiency, initial fusion weight and initial maintenance cycle threshold are calculated and updated based on the collected data. This calculation process is existing technology and will not be described in detail here.

[0037] In some preferred embodiments, after updating the parameters of the heat generation model, if the heat generation model shows a decrease in heat generation and a reduction in internal resistance after maintenance (heat generation model Q), actual = c p In the equation ×ρ×F×ΔT, all data are known. The heat generation is calculated based on the known data. According to Joule's law, the smaller the heat generation under the same current, the lower the resistance. Therefore, the starting temperature threshold of the flow battery's cooling system is increased to reduce auxiliary energy consumption.

[0038] The intelligent management method for flow batteries provided in the above embodiments has the following advantages: System collaboration: Through deep interaction of the three major functions, comprehensive and refined intelligent management of flow batteries is realized; Forward-looking management: Thermal management changes from passive feedback to feedforward control, and maintenance strategy changes from fixed cycle to state-based predictive maintenance; Accurate estimation: Through dynamic fusion correction, high-precision SOC estimation is achieved throughout the entire operating condition and life cycle; System self-adaptation: Key parameters have online learning and adaptive adjustment capabilities, making the system increasingly intelligent with use.

[0039] Example 2

[0040] This embodiment also provides an intelligent management system for a flow battery, including: The real-time heat generation efficiency calculation module is used to calculate the real-time heat generation power of the fuel cell stack using the heat generation model, and then obtain the real-time heat generation efficiency. The fusion weight update module is used to dynamically update the fusion weight of the ampere-hour integral method SOC and the calibration SOC of the flow battery by combining real-time heat generation efficiency, benchmark heat generation efficiency and temperature data. The flow battery SOC fusion module is used to fuse the ampere-hour integral method SOC and the calibration SOC of the flow battery based on the updated fusion weights to obtain the fused flow battery SOC. The maintenance cycle threshold adjustment module is used to dynamically adjust the maintenance cycle threshold based on the estimation deviation of the flow battery's SOC and the rate of heat generation efficiency decay. The maintenance and parameter update module is used to generate maintenance work orders based on the adjusted maintenance cycle threshold. After completing the maintenance operation of the flow battery, it updates the parameters of the heat generation model, the baseline heat generation efficiency, the initial fusion weight, and the initial maintenance cycle threshold.

[0041] In some preferred embodiments, the maintenance and parameter update module is also used to, after completing the parameter update of the heat generation model, if the heat generation model shows that the heat generation is reduced and the internal resistance is reduced after maintenance, then increase the start-up temperature threshold of the flow battery cooling system to reduce auxiliary energy consumption.

[0042] It should be understood that the functional unit modules in the various embodiments of the present invention can be concentrated in one processing unit, or each unit module can exist physically separately, or two or more unit modules can be integrated into one unit module, and can be implemented in hardware or software.

[0043] Example 3

[0044] This embodiment provides an intelligent management system for a flow battery, such as... Figure 2 As shown, it includes: The flow battery system includes a stack unit 103, a positive electrolyte storage tank 101, a negative electrolyte storage tank 102, a positive circulation pump 401, a negative circulation pump 402, connecting pipes and valves; The cooling system includes a refrigeration unit 303 and a positive heat exchanger 301 and a negative heat exchanger 302 connected thereto; The positive electrode electrolyte storage tank 101, positive electrode circulation pump 401, positive electrode heat exchanger 301, positive electrode electric propeller of fuel cell unit 103, and positive electrode electrolyte storage tank 101 are connected in sequence through connecting pipes and valves to form a circulation. The negative electrode electrolyte storage tank 102, negative electrode circulation pump 402, negative electrode heat exchanger 302, negative electrode electric propeller of fuel cell unit 103, and negative electrode electrolyte storage tank 102 are connected in sequence through connecting pipes and valves to form a circulation. The positive electrode heat exchanger 301 and negative electrode heat exchanger 302 are respectively connected to the refrigeration unit 303. The temperature sensor assembly is used to collect the inlet temperature of the positive electrode heat exchanger (which is also the inlet temperature of the positive electrode stack, and both are collected from the same location), the inlet temperature of the negative electrode heat exchanger, the outlet temperature of the positive electrode heat exchanger (i.e., the system temperature), and the outlet temperature of the positive electrode stack; the temperature sensor assembly includes a positive electrode heat exchanger inlet temperature sensor 201, a negative electrode heat exchanger inlet temperature sensor 202, a positive electrode heat exchanger outlet temperature sensor 203, and a positive electrode stack outlet temperature sensor 204. The inlet and outlet temperatures of the positive electrode heat exchanger can be used to calculate the actual heat removed by the positive electrode heat exchanger, thereby assessing whether the heat exchange effect meets expectations and enabling on-demand cooling. By comparing the inlet temperatures of the positive electrode heat exchanger and the negative electrode heat exchanger, it can be determined whether the thermal state of the electrolytes at the positive and negative electrodes is balanced. The voltage and current acquisition module is used to acquire the voltage and current of the flow battery system, as well as the open-circuit voltage of the flow battery system. The voltage and current acquisition module includes a system voltage and current acquisition instrument 701, a system open-circuit voltage acquisition instrument 702, and a system open-circuit voltage measurement module 800. The voltage and current acquisition instrument 701 acquires the voltage and current of the flow battery system in real time and transmits them to the control system. The system open-circuit voltage measurement module 800 acquires the open-circuit voltage of the positive and negative electrolytes. The system open-circuit voltage acquisition instrument 702 acquires the open-circuit voltage and transmits it to the control system. The flow acquisition module (not shown) is used to collect the real-time flow rate of the electrolyte. The flow acquisition module can collect the real-time flow rate of the electrolyte by installing a flow meter and directly reading the flow rate displayed by the flow meter, or by calculating it through pressure and circulation pump curves. The control system 500 is connected to the flow battery system, cooling system, temperature sensor assembly, voltage and current acquisition module and flow acquisition module. The control system includes an acquisition unit 501, a control unit 502 and a human-machine interaction unit 503. Acquisition Unit 501: Responsible for synchronously acquiring signals from various sensors at a preset frequency, as well as acquiring all key physical parameters of the flow battery system, providing a data basis for control decisions; Control unit 502: connected to acquisition unit 501, configured to execute the intelligent management method for flow batteries as described above; Human-machine interaction unit 503: connected to control unit 502, used for parameter setting, alarm indication and query, etc. The Energy Management System (EMS) 600, which is electrically connected to the control system 500, is mainly used for energy dispatching and interaction with the power grid, focusing on the overall efficiency and economy of the flow battery system.

[0045] It is understood that the same or similar parts in the above embodiments can be referred to each other, and the contents not described in detail in some embodiments can be referred to the same or similar contents in other embodiments.

[0046] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of the invention pertain.

[0047] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A smart management method for a flow battery, characterized in that, Includes the following steps: The real-time heat generation power of the fuel cell stack is calculated using a heat generation model, thereby obtaining the real-time heat generation efficiency. The fusion weights of the ampere-hour integral method SOC and the calibration SOC of the flow battery are dynamically updated by combining real-time heat generation efficiency, benchmark heat generation efficiency and temperature data. The ampere-hour integral method SOC and the calibration SOC of the flow battery are fused based on the updated fusion weights to obtain the fused flow battery SOC. The maintenance cycle threshold is dynamically adjusted based on the estimation bias of the flow battery's SOC and the rate of heat generation efficiency decay. Maintenance work orders are generated based on the adjusted maintenance cycle threshold. After the maintenance operation of the flow battery is completed, the parameters of the heat generation model, the baseline heat generation efficiency, the initial fusion weights, and the initial maintenance cycle threshold are updated.

2. The intelligent management method for a flow battery according to claim 1, characterized in that, The heat generation power of the fuel cell stack is calculated using a heat generation model, and the heat generation efficiency is obtained. Specifically, this includes: Based on the principle of heat balance, a heat production model is constructed: Q actual = c p ×ρ×F×ΔT, where Q actual c represents the real-time heat generation power of the fuel cell stack. p ρ and ρ represent the specific heat capacity and density of the electrolyte, respectively; F represents the real-time flow rate of the electrolyte; and ΔT represents the real-time temperature difference between the electrolyte inlet and outlet of the stack. The real-time flow rate F of the electrolyte and the real-time temperature difference ΔT between the electrolyte inlet and outlet of the fuel cell stack are obtained and input into the heat generation model to obtain the real-time heat generation power Q of the fuel cell stack. actual ; The real-time heat production efficiency is calculated using the following formula: η actual = P electrical / (P) electrical + Q actual ), where P electrical This represents the real-time electrical power of the fuel cell stack.

3. The intelligent management method for a flow battery according to claim 1, characterized in that, The fusion weights of the ampere-hour integral method SOC and the calibration SOC of the flow battery are dynamically updated by combining real-time heat generation efficiency, baseline heat generation efficiency, and temperature data. Specifically, this includes: When the real-time heat generation efficiency η actual The decrease exceeds the first threshold, or the current system temperature T is different from the optimal system temperature T. opt If the difference exceeds the second threshold, or if the absolute value of the current temperature change rate dT / dt exceeds the third threshold, then the fusion weighting of the ampere-hour integral SOC and the calibration SOC of the flow battery is updated. k = k base × (η actual / η base ) a × exp (- ((T - T opt ) / σ)²) × exp(-c × |dT / dt|); Where k represents the updated fusion weight, k base η represents the initial fusion weights. actual and η base These represent the real-time heat production efficiency and the baseline heat production efficiency, respectively, with a, σ, and c being preset constants.

4. The intelligent management method for a flow battery according to claim 1, characterized in that, The process of fusing the ampere-hour integral SOC and the calibration SOC of the flow battery based on the updated fusion weights is represented as follows: SOC fused =k×SOC ah + (1-k) ×SOC ocv； In the formula, SOC fused The SOC of the flow battery after fusion is represented by k, where k represents the updated fusion weight. ah and SOC ocv These represent the state of charge (SOC) of the flow battery using the ampere-hour integration method and the calibration SOC, respectively.

5. The intelligent management method for a flow battery according to claim 1, characterized in that, The maintenance cycle threshold is dynamically adjusted based on the estimation bias of the flow battery's State of Charge (SOC) and the rate of heat generation efficiency decay, specifically including: Record the maximum deviation ΔSOC between the ampere-hour integration SOC and the calibration SOC between two consecutive SOC calibrations. max ; Calculate the real-time heat production efficiency η actual The moving average value is used to calculate the heat production efficiency decay rate Δη / Δt; The maintenance cycle threshold is dynamically adjusted using the following formula: N set_new =N set ×[1-γ1×max(0,(ΔSOC max -ΔSOC threshold ) / ΔSOC threshold ) -γ2×max(0, (η base -or actual ) / or base )]; In the formula, N set_new N represents the adjusted maintenance cycle threshold. set ΔSOC represents the initial maintenance cycle threshold. threshold η represents the maximum permissible SOC deviation safety threshold. actual and η base γ1 and γ2 represent the real-time heat production efficiency and the baseline heat production efficiency, respectively; γ1 and γ2 are adjustable weighting coefficients, and the values ​​of γ1 and γ2 are adjusted based on the heat production efficiency decay rate Δη / Δt.

6. The intelligent management method for a flow battery according to claim 1, characterized in that, After completing the maintenance operations for the flow battery, the parameters of the heat generation model, the baseline heat generation efficiency, the initial fusion weights, and the initial maintenance cycle threshold are updated, specifically including: After completing the maintenance operation of the flow battery, it enters self-learning mode; In self-learning mode, a series of standardized charge and discharge power pulses are applied according to a preset program, and the actual heat generation power at different power points is collected synchronously and compared with the calculated value of the current heat generation model. The parameters of the heat generation model are adjusted so that its calculated value tends to the actual heat generation power. At the same time, the baseline heat generation efficiency, initial fusion weight and initial maintenance cycle threshold are calculated and updated based on the collected data.

7. The intelligent management method for a flow battery according to claim 1, characterized in that, After updating the parameters of the heat generation model, if the heat generation model shows a decrease in heat generation and a reduction in internal resistance after maintenance, then increase the start-up temperature threshold of the flow battery's cooling system.

8. An intelligent management system for a flow battery, characterized in that, include: The real-time heat generation efficiency calculation module is used to calculate the real-time heat generation power of the fuel cell stack using the heat generation model, and then obtain the real-time heat generation efficiency. The fusion weight update module is used to dynamically update the fusion weight of the ampere-hour integral method SOC and the calibration SOC of the flow battery by combining real-time heat generation efficiency, benchmark heat generation efficiency and temperature data. The flow battery SOC fusion module is used to fuse the ampere-hour integral method SOC and the calibration SOC of the flow battery based on the updated fusion weights to obtain the fused flow battery SOC. The maintenance cycle threshold adjustment module is used to dynamically adjust the maintenance cycle threshold based on the estimation deviation of the flow battery's SOC and the rate of heat generation efficiency decay. The maintenance and parameter update module is used to generate maintenance work orders based on the adjusted maintenance cycle threshold. After completing the maintenance operation of the flow battery, it updates the parameters of the heat generation model, the baseline heat generation efficiency, the initial fusion weight, and the initial maintenance cycle threshold.

9. The intelligent management system for a flow battery according to claim 8, characterized in that, The maintenance and parameter update module is also used to increase the start-up temperature threshold of the flow battery's cooling system if the heat generation model shows a decrease in heat generation and a reduction in internal resistance after maintenance, after completing the parameter update of the heat generation model.

10. An intelligent management system for a flow battery, characterized in that, include: Flow battery system; Temperature sensor assembly used to collect the inlet and outlet temperatures of the positive electrode stack in a flow battery system; The flow acquisition module is used to collect the real-time flow rate of the electrolyte. The voltage and current acquisition module is used to acquire the voltage and current of the flow battery system, as well as the open-circuit voltage of the flow battery system. The control system, wherein the flow battery system, temperature sensor assembly, flow acquisition module and voltage and current acquisition module are all connected to the control system, and the control system is configured to execute the intelligent management method of the flow battery as described in any one of claims 1 to 7.

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