Intelligent management method and system of a flow battery

Abstract

The application relates to the technical field of batteries and discloses an intelligent management method and system for a flow battery, wherein the method comprises the following steps: calculating the real-time heat generation power of a battery stack by 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 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 attenuation 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. Through the deep interaction of the three functions, all-round and fine 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 of flow battery, which realizes the paradigm change from passive response to active foresight and from local optimization to system optimization by means of deep coupling and intelligent cooperation of the three functions of thermal management, state estimation and preventive maintenance, and improves the operation efficiency, reliability and life cycle economy of the flow battery system.

[0005] In a first aspect, an intelligent management method of flow battery is provided, comprising the following steps:

[0006] calculating the real-time heat generation power of the stack using a heat generation model, and then obtaining the real-time heat generation efficiency;

[0007] 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;

[0008] 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;

[0009] dynamically adjusting the maintenance cycle threshold based on the estimation deviation of the flow battery SOC and the heat generation efficiency decay rate;

[0010] 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.

[0011] Further, the heat generation model is used to calculate the heat generation power of the stack, and then the heat generation efficiency is obtained, specifically including:

[0012] Based on the heat balance principle, a heat generation model is constructed: Q actual = c p ×ρ×F×ΔT, wherein Q actual represents the real-time heat generation power of the stack, c p and p respectively represent the specific heat capacity and density of the electrolyte, F represents the real-time flow rate of the electrolyte, and ΔT represents the real-time temperature difference between the inlet and outlet of the electrolyte of the stack.

[0013] The real-time flow rate F of the electrolyte and the real-time temperature difference ΔT between the inlet and outlet of the electrolyte of the stack are obtained, and input into the heat generation model to obtain the real-time heat generation power Q actual of the stack.

[0014] The real-time heat generation efficiency is calculated using the following formula: η actual = P electrical / ( P electrical + Q actual ), wherein P electrical is the real-time electrical power of the stack.

[0015] Further, the fusion weight of the ampere-hour integral method SOC and the calibrated SOC of the flow battery is dynamically updated in combination with the real-time heat generation efficiency, the reference heat generation efficiency and the temperature data, specifically including:

[0016] When the real-time heat generation efficiency η actual decreases by more than a first threshold, or the difference between the current system temperature T and the optimal system temperature T opt exceeds a second threshold, or the absolute value of the current temperature change rate dT / dt exceeds a third threshold, the fusion weight of the ampere-hour integral method SOC and the calibrated SOC of the flow battery is updated:

[0017] k = k base × (η actual / η base ) a × exp (- ((T - T opt ) / σ)²) × exp(-c × |dT / dt|);

[0018] Wherein, k represents the updated fusion weight, k base represents the initial fusion weight, η actual and η base respectively represent the real-time heat generation efficiency and the reference heat generation efficiency, and a, σ and c are preset constants.

[0019] Further, the process of fusing the ampere-hour integral method SOC of the flow battery and the calibrated SOC based on the updated fusion weight is represented as follows:

[0020] SOC fused = k × SOC ah + (1-k) × SOC ocv ;

[0021] In the formula, SOC fused represents the fused flow battery SOC, k represents the updated fusion weight, SOC ah and SOC ocv respectively represent the ampere-hour integral method SOC and the calibrated SOC of the flow battery.

[0022] Further, the maintenance cycle threshold is dynamically adjusted based on the estimated deviation of the flow battery SOC and the heat generation efficiency decay rate, specifically including:

[0023] Record the maximum deviation ΔSOC max between the ampere-hour integral method SOC and the calibrated SOC during the adjacent two times of SOC calibration.

[0024] Calculate the moving average of the real-time heat generation efficiency η actual , and calculate the heat generation efficiency decay rate Δη / Δt.

[0025] The maintenance cycle threshold is dynamically adjusted using the following formula:

[0026] N set_new = N set × [1-γ1×max(0,(ΔSOC max -ΔSOC threshold ) / ΔSOC threshold )

[0027] -γ2×max(0, (η base -η actual ) / η base )];

[0028] In the formula, N set_new represents the adjusted maintenance cycle threshold, N set represents the initial maintenance cycle threshold, ΔSOC threshold represents the maximum SOC deviation safety threshold allowed, η actual and η base respectively represent the real-time heat generation efficiency and the reference heat generation efficiency; γ1 and γ2 are adjustable weight coefficients, and the values of γ1 and γ2 are adjusted based on the heat generation efficiency decay rate Δη / Δt.

[0029] Further, after completing the maintenance operation 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, specifically including:

[0030] After completing the maintenance operation of the flow battery, the self-learning mode is entered;

[0031] In the self-learning mode, a series of standardized charge and discharge power pulses are applied according to a preset program, the actual heat generation power at different power points is synchronously collected, and is compared with the calculated value of the current heat generation model, the parameters of the heat generation model are adjusted so that the calculated value tends to the actual heat generation power; at the same time, the baseline heat generation efficiency, the initial fusion weight, and the initial maintenance cycle threshold are calculated and updated based on the collected data.

[0032] Further, after completing the parameter update of the heat generation model, if the heat generation model shows that the heat generation decreases and the internal resistance decreases after maintenance, the starting temperature threshold of the cooling system of the flow battery is increased, and the auxiliary energy consumption is reduced.

[0033] In a second aspect, an intelligent management system of a flow battery is provided, including:

[0034] A real-time heat generation efficiency calculation module is configured to calculate the real-time heat generation power of the stack by using the heat generation model, and further obtain the real-time heat generation efficiency;

[0035] A fusion weight update module is configured to dynamically update the fusion weight of the ampere-hour integral method SOC and the calibrated SOC of the flow battery in combination with the real-time heat generation efficiency, the baseline heat generation efficiency, and the temperature data;

[0036] A flow battery SOC fusion module is configured to fuse the ampere-hour integral method SOC and the calibrated SOC of the flow battery based on the updated fusion weight, and obtain the fused flow battery SOC;

[0037] A maintenance cycle threshold adjustment module is configured to dynamically adjust the maintenance cycle threshold based on the estimated deviation of the flow battery SOC and the heat generation efficiency attenuation rate;

[0038] A maintenance and parameter update module is configured to generate a maintenance work order based on the adjusted maintenance cycle threshold, and update the parameters of the heat generation model, the baseline heat generation efficiency, the initial fusion weight, and the initial maintenance cycle threshold after completing the maintenance operation of the flow battery.

[0039] Further, the maintenance and parameter update module is further configured to, after completing the parameter update of the heat generation model, if the heat generation model shows that the heat generation decreases and the internal resistance decreases after maintenance, increase the starting temperature threshold of the cooling system of the flow battery, and reduce the auxiliary energy consumption.

[0040] In a third aspect, an intelligent management system of a flow battery is provided, including:

[0041] A flow battery system;

[0042] A temperature sensor assembly for collecting inlet temperature and outlet temperature of a positive electrode stack of the flow battery system;

[0043] A flow acquisition module for collecting real-time flow of electrolyte;

[0044] A voltage and current acquisition module for collecting voltage and current of the flow battery system, and collecting open circuit voltage of the flow battery system;

[0045] A control system, the flow battery system, the temperature sensor assembly, the flow acquisition module and the voltage and current acquisition module are connected with the control system, and the control system is configured to execute the intelligent management method of the flow battery as described above.

[0046] The present application provides an intelligent management method and system for a flow battery, which has the following beneficial effects:

[0047] 1. Coordination of thermal management feedforward control and SOC estimation: the heat generation efficiency of thermal management calculation is used as a key parameter for flow battery SOC estimation. When the heat generation efficiency or temperature data fluctuation exceeds the corresponding threshold value, it indicates that the flow battery is aging. At this time, in the weighted fusion of the relationship model between ampere-hour integral method SOC and calibrated SOC, the fusion weight is dynamically adjusted to offset the estimation error caused by aging and ensure high-precision estimation of flow battery SOC throughout its life cycle.

[0048] 2. Coordination of SOC estimation and preventive maintenance: the long-term data of SOC estimation and the trend of change of SOC curve relationship are key indicators reflecting battery health status and capacity decay. The present application uses SOC estimation data to dynamically optimize the trigger maintenance cycle threshold of the maintenance module. When the system judges that capacity decay is accelerating through SOC data, it can automatically lower the maintenance cycle threshold to achieve more timely preventive maintenance.

[0049] 3. Coordination of preventive maintenance, thermal management and SOC estimation: after the maintenance module performs maintenance operation, the internal state of the flow battery system is restored. At this time, the system will automatically initialize or fine-tune the baseline parameters of heat generation model, SOC estimation and maintenance, to ensure that all models can quickly adapt to the system after maintenance and maintain long-term accuracy, realizing multi-dimensional data-driven and having closed-loop optimization capability. BRIEF DESCRIPTION OF DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description only constitute some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained from these drawings without creative labor.

[0051] Figure 1 is a flow chart of an intelligent management method of a flow battery provided by an embodiment of the present application;

[0052] Figure 2 is a structural schematic diagram of an intelligent management system of a flow battery provided by an embodiment of the present application, in which the dotted arrows represent electrolyte flow paths, and the dotted lines represent electric signal / control signal transmission paths. DETAILED DESCRIPTION

[0053] In order to make the objects, technical solutions and advantages of the present application clearer, the technical solutions of the present application will be described in detail below. Obviously, the described embodiments only constitute some embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of the present application.

[0054] In order to solve the problem that heat management, state estimation and maintenance are independent of each other in the prior art, the present application provides an intelligent management method and system of a flow battery, and the core inventive concept is that the present application breaks the limit of traditional independent modules, and makes the three modules of heat management, state estimation and maintenance provide data feedback and decision basis for each other, to form a self-adaptive and self-optimized organic whole. This system-level architecture design makes the overall technical effect far exceed the sum of simple addition of each part function, and produces a significant synergistic effect. The key models (such as heat production model, SOC fusion, maintenance threshold) in the present application are all designed to have online learning or self-adaptive adjustment capability. For example, the heat production model can be self-corrected through real-time running data; the fusion weight of SOC can be dynamically changed according to the working conditions; and the maintenance threshold is dynamically optimized based on historical performance data. This dynamic characteristic enables the system to adapt to the aging process of the battery, and always maintains the optimal management performance. The technical solutions of the present application will be described in detail below in combination with specific embodiments.

[0055] Embodiment 1

[0056] As shown in Figure 1 , the present embodiment provides an intelligent management method of a flow battery, including the following steps:

[0057] S1: calculating the real-time heat production power of the stack by using a heat production model, and then obtaining the real-time heat production efficiency.

[0058] Specifically, step S1 comprises:

[0059] S11: constructing a heat generation model Q actual = c p ×ρ×F×ΔT, wherein Q actual represents the real-time heat generation power of the stack, c p and p respectively represent the specific heat capacity and density of the electrolyte, F represents the real-time flow rate of the electrolyte, and ΔT represents the real-time temperature difference between the inlet and outlet of the electrolyte of the stack;

[0060] S12: obtaining the real-time flow rate F of the electrolyte and the real-time temperature difference ΔT between the inlet and outlet of the electrolyte of the stack, and inputting the heat generation model to obtain the real-time heat generation power Q actual of the stack;

[0061] S13: calculating the real-time heat generation efficiency η actual = P electrical / (P electrical + Q actual ), wherein P electrical is the real-time electrical power of the stack.

[0062] S2: dynamically updating the fusion weight of the ampere-hour integral method SOC and the calibrated SOC of the flow battery in combination with the real-time heat generation efficiency, the reference heat generation efficiency and the temperature data.

[0063] Specifically, step S2 comprises:

[0064] When the heat generation efficiency decreases or the temperature fluctuates sharply, that is, when the real-time heat generation efficiency η actual decreases by more than a first threshold value, or the difference between the current system temperature T and the optimal system temperature T opt exceeds a second threshold value, or the absolute value of the current temperature change rate dT / dt exceeds a third threshold value, the fusion weight of the ampere-hour integral method SOC and the calibrated SOC of the flow battery is updated:

[0065] k = k base × (η actual / η base ) a × exp (- ((T - T opt ) / σ)²) × exp(-c × |dT / dt|);

[0066] wherein k represents the updated fusion weight, k base represents the initial fusion weight, η actual and η base respectively represent the real-time heat generation efficiency and the reference heat generation efficiency, and a, σ and c are preset constants. (η actual / η base ) a η is the efficiency factor, evaluating the pros and cons of the current performance relative to the benchmark; exp (- ((T - T opt ) / σ)²) is the temperature factor, when T deviates from the optimal system temperature T opt , the factor value decreases, reducing the weight; exp(-c × |dT / dt|) is the trend factor, which adjusts the weight according to the absolute value of the temperature change rate. base and k base can be obtained by testing at the initial health state of the battery system, which is prior art and will not be described here.

[0067] η actual directly reflects the efficiency of converting electrical energy into chemical energy, and its decline is often an early sensitive indicator of aging phenomena such as increased internal resistance of the stack, catalyst activity decay, or reduced ion conduction efficiency. By fusing with temperature data, it can reveal changes in internal state better than pure temperature values.

[0068] S3: Fuse the ampere-hour integration method SOC of the flow battery and the calibrated SOC (OCV calibration or calibration based on electrolyte physical and chemical parameters) based on the updated fusion weight to obtain the fused flow battery SOC.

[0069] Specifically, the process of fusing the ampere-hour integration method SOC of the flow battery and the calibrated SOC based on the updated fusion weight is represented as follows:

[0070] SOC fused =k×SOC ah + (1-k) ×SOC ocv ;

[0071] In the formula, SOC fused represents the fused flow battery SOC, k represents the updated fusion weight, SOC ah and SOC ocv represent the ampere-hour integration method SOC and the calibrated SOC of the flow battery, respectively.

[0072] S4: Dynamically adjust the maintenance cycle threshold based on the estimated deviation of the flow battery SOC and the heat generation efficiency decay rate.

[0073] Specifically, step S4 includes:

[0074] S41: Record the maximum deviation ΔSOC max of the ampere-hour integration method SOC and the calibrated SOC during the calibration of the SOC adjacent to the two times.

[0075] S42: Calculate the real-time heat generation efficiency η actualthe moving average value of the heat generation efficiency, and analyze the long-term descending slope, i.e. calculate the heat generation efficiency decay rate Δη / Δt;

[0076] S43: dynamically adjust the maintenance cycle threshold value by using the following formula:

[0077] N set_new = N set × [1-γ1×max(0, (ΔSOC max -ΔSOC threshold ) / ΔSOC threshold )

[0078] -γ2×max(0, (η base -η actual ) / η base )];

[0079] wherein N set_new represents the adjusted maintenance cycle threshold value, N set represents the initial maintenance cycle threshold value, ΔSOC threshold represents the maximum SOC deviation safety threshold value allowed, η actual and η base represent the real-time heat generation efficiency and the reference heat generation efficiency, respectively; γ1 and γ2 are adjustable weight coefficients, the values of γ1 and γ2 are adjusted based on the heat generation efficiency decay rate Δη / Δt, and γ1 and γ2 can be obtained by mapping based on the heat generation efficiency decay rate Δη / Δt through a preset mapping rule, for example, when the heat generation efficiency decay rate Δη / Δt increases, γ2 is automatically increased to make the system more sensitive to the instantaneous change of the efficiency.

[0080] The maintenance cycle threshold value is no longer a fixed value, but a dynamic variable determined by the SOC estimation deviation and the heat generation efficiency decay. When the SOC estimation deviation increases and the heat generation efficiency significantly decreases, the system can more accurately determine that the battery health state is deteriorating rapidly, so as to actively and accurately reduce the maintenance cycle threshold value, realizing the transition from timely maintenance to on-demand maintenance.

[0081] S5: generate a maintenance work order based on the adjusted maintenance cycle threshold value, and update the parameters of the heat generation model, the reference heat generation efficiency, the initial fusion weight, and the initial maintenance cycle threshold value after completing the maintenance operation of the flow battery.

[0082] Specifically, when the calculated new maintenance cycle threshold value is lower than the current preset maintenance cycle threshold value, a maintenance work order is automatically generated, prompting that the liquid mixing and other maintenance operations need to be performed in advance. After completing the maintenance operation of the flow battery, the parameters of the heat generation model, the reference heat generation efficiency, the initial fusion weight, and the initial maintenance cycle threshold value are updated, specifically including:

[0083] After completing the maintenance operation of the flow battery, enter the self-learning mode;

[0084] In the self-learning mode, a series of standardized charge and discharge power pulses are applied according to a preset program, the actual heat generation power at different power points is synchronously collected, and the calculated value of the current heat generation model is compared, the parameters of the heat generation model are adjusted, so that the calculated value tends to the actual heat generation power; at the same time, the reference heat generation efficiency, the initial fusion weight and the initial maintenance period threshold are calculated and updated based on the collected data, which is a prior art and will not be described in detail here.

[0085] In some preferred embodiments, after the parameter updating of the heat generation model is completed, if the heat generation model shows that the heat generation after maintenance is reduced and the internal resistance is reduced (the heat generation model Q actual = c p ×ρ×F×ΔT, all data are known, the heat generation is calculated according to the known data, and according to the Joule law, the smaller the heat generation is under the same current, the lower the resistance is), the starting temperature threshold of the cooling system of the flow battery is increased, and the auxiliary energy consumption is reduced.

[0086] The intelligent management method of the flow battery provided in the above embodiments has the following advantages: system synergy: through the deep interaction of the three functions, the intelligent management of the flow battery is realized in all directions and in detail; management foresight: the heat management is changed from passive feedback to feedforward control, and the maintenance strategy is changed from fixed period to predictive maintenance based on state; estimation precision: through dynamic fusion correction, high-precision SOC estimation is realized in all working conditions and throughout the life cycle; system self-adaptation: the key parameters have online learning and self-adaptive adjustment capabilities, making the system more intelligent.

[0087] Embodiment 2

[0088] The embodiment also provides an intelligent management system of a flow battery, which comprises:

[0089] A real-time heat generation efficiency calculation module is configured to calculate the real-time heat generation power of the stack by using the heat generation model, and further obtain the real-time heat generation efficiency;

[0090] A fusion weight updating module is configured to dynamically update the fusion weight of the ampere-hour integral method SOC and the calibrated SOC of the flow battery in combination with the real-time heat generation efficiency, the reference heat generation efficiency and the temperature data;

[0091] A flow battery SOC fusion module is configured to fuse the ampere-hour integral method SOC and the calibrated SOC of the flow battery based on the updated fusion weight, and obtain the fused flow battery SOC;

[0092] A maintenance period threshold adjusting module is configured to dynamically adjust the maintenance period threshold based on the estimation deviation of the flow battery SOC and the heat generation efficiency decay rate;

[0093] The maintenance and parameter updating module is configured to generate a maintenance work order based on the adjusted maintenance cycle threshold, and update the parameters of the heat generation model, the baseline heat generation efficiency, the initial fusion weight, and the initial maintenance cycle threshold after completing the maintenance operation of the flow battery.

[0094] In some preferred embodiments, the maintenance and parameter updating module is further configured to, after completing the parameter updating of the heat generation model, increase the start temperature threshold of the cooling system of the flow battery and reduce the auxiliary energy consumption if the heat generation model shows that the heat generation decreases and the internal resistance decreases after the maintenance.

[0095] It should be understood that the functional unit modules in each embodiment of the present application can be concentrated in one processing unit, or each unit module can exist physically alone, or two or more unit modules can be integrated into one unit module, and can be realized in the form of hardware or software.

[0096] Embodiment 3

[0097] The present embodiment provides an intelligent management system for a flow battery, as shown in the accompanying drawings, comprising: Figure 2

[0098] The flow battery system comprises 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 pipelines and valves;

[0099] The cooling system comprises a refrigeration unit 303 and a positive heat exchanger 301 and a negative heat exchanger 302 connected thereto;

[0100] The positive electrolyte storage tank 101, the positive circulation pump 401, the positive heat exchanger 301, the positive electrode of the stack unit 103, and the positive electrolyte storage tank 101 are sequentially connected by connecting pipelines and valves to form a circulation, and the negative electrolyte storage tank 102, the negative circulation pump 402, the negative heat exchanger 302, the negative electrode of the stack unit 103, and the negative electrolyte storage tank 102 are sequentially connected by connecting pipelines and valves to form a circulation; the positive heat exchanger 301 and the negative heat exchanger 302 are respectively connected to the refrigeration unit 303;

[0101] The temperature sensor assembly is configured to collect the inlet temperature of the positive heat exchanger (which is also the inlet temperature of the positive stack, and the collection positions of the two are the same), the inlet temperature of the negative heat exchanger, the outlet temperature of the positive heat exchanger (i.e. the system temperature), and the outlet temperature of the positive stack; the temperature sensor assembly comprises a positive heat exchanger inlet temperature sensor 201, a negative heat exchanger inlet temperature sensor 202, a positive heat exchanger outlet temperature sensor 203, and a positive stack outlet temperature sensor 204;

[0102] ​The inlet temperature and the outlet temperature of the positive electrode heat exchanger can calculate the heat actually taken away by the positive electrode heat exchanger, so as to evaluate whether the heat exchange effect reaches the expectation and realizes on-demand refrigeration; by comparing the inlet temperature of the positive electrode heat exchanger and the inlet temperature of the negative electrode heat exchanger, whether the thermal state of the positive and negative electrolytes is balanced can be judged.

[0103] A voltage and current acquisition module is configured to acquire the voltage and current of the flow battery system and 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.

[0104] A flow acquisition module (not shown) is configured to acquire the real-time flow of the electrolyte. The flow acquisition module can acquire the real-time flow of the electrolyte by installing a flow meter, directly reading the flow displayed by the flow meter, or calculating and acquiring the flow through the pressure and circulating pump curve.

[0105] A control system 500 is connected with the flow battery system, the cooling system, the temperature sensor assembly, the voltage and current acquisition module, and the flow acquisition module. The control system includes an acquisition unit 501, a control unit 502, and a human-computer interaction unit 503.

[0106] The acquisition unit 501 is responsible for synchronously acquiring the signals of various sensors at a preset frequency and acquiring all key physical parameters of the flow battery system, thereby providing a data basis for control decisions.

[0107] The control unit 502 is connected with the acquisition unit 501 and is configured to execute the intelligent management method of the flow battery as described above.

[0108] The human-computer interaction unit 503 is connected with the control unit 502 and is configured to perform parameter setting, alarm indication, and query operations.

[0109] An energy management system (EMS) 600 is electrically connected with the control system 500 and is mainly used for energy scheduling, interaction with the power grid, and attention to the overall efficiency and economy of the flow battery system.

[0110] It can be understood that the same or similar parts in the above embodiments can be mutually referred to, and the contents not described in detail in some embodiments can be referred to the same or similar contents in other embodiments.

[0111] Any process or method described in flow chart form or otherwise described herein can be understood as representing code modules, segments, or portions of code that include one or more executable instructions for implementing specific logic functions (or steps) of the process, and / or that may

[0112] Although the embodiments of the present application have been shown and described above, it should be understood by those skilled in the art that the above-described embodiments are exemplary only, and that changes, modifications, substitutions and variations can be made therein without departing from the scope of the present application.

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. Specifically, it 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, where a, σ, and c are preset constants; 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 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. 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 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.

4. 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.

5. 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.

6. An intelligent management system for a flow battery, characterized in that, For implementing the intelligent management method of the flow battery as described in any one of claims 1 to 5, the system comprises: 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.

7. The intelligent management system for the flow battery according to claim 6, 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.

8. 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 5.

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