Intelligent energy scheduling method and system of flow battery

Abstract

The invention discloses an intelligent energy scheduling method and system for a flow battery, and relates to the technical field of flow batteries, and the method comprises the following steps: collecting energy operation parameters of the flow battery in real time, and dynamically estimating an electrolyte state to obtain multiple pieces of energy state information; a target to be dispatched is set, and a fuzzy control strategy is formulated through retrograde multi-target optimization; executing a fuzzy control strategy to generate a regulation and control signal set and scheduling; and finally, according to a scheduling result, performing reverse tracing to optimize a fuzzy control strategy, and updating a regulation signal set to obtain a multi-stage regulation instruction to perform intelligent scheduling on the energy of the flow battery. The method solves the technical problems of low matching degree between a scheduling strategy and an actual demand, large power loss and fast battery life attenuation caused by difficulty in accurately capturing an energy state in real time in a traditional flow battery scheduling method, and achieves the purposes of intelligent flow battery energy scheduling, power loss reduction, battery life prolonging and energy conservation. And the matching degree between the scheduling and the actual demand is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of liquid flow batteries, and in particular to an intelligent energy scheduling method and system for liquid flow batteries. Background Art

[0002] Liquid flow batteries, which convert chemical energy into electrical energy, are widely used in the field of energy storage, and their energy scheduling efficiency is crucial to system performance. In existing technologies, liquid flow battery energy scheduling mostly relies on fixed strategies or simple parameter feedback, which can play a certain role under stable operating conditions. However, as the application scenarios become more complex, traditional scheduling methods have gradually shown their limitations. Due to the dynamic changes in the state of the electrolyte in liquid flow batteries, involving the coupling of multiple parameters such as concentration, temperature, and flow rate, traditional methods are difficult to accurately capture the energy state in real time, resulting in a low match between the scheduling strategy and actual demand, prone to problems such as large power loss and rapid battery life decay, and difficult to meet the needs of efficient and long-term energy scheduling. Summary of the Invention

[0003] The present application provides an intelligent energy scheduling method and system for liquid flow batteries, which is used to solve the technical problems that traditional liquid flow battery scheduling methods are difficult to accurately capture the energy status in real time, resulting in a low match between scheduling strategies and actual needs, large power losses, and rapid battery life degradation.

[0004] The first aspect of the present application provides a method for intelligent energy scheduling of a liquid flow battery, the method comprising: real-time collection of energy operating parameters of the liquid flow battery, dynamic estimation of the electrolyte state based on the energy operating parameters, and acquisition of multiple energy state information; scheduling analysis of the liquid flow battery based on the multiple energy state information, setting multiple targets to be scheduled, performing multi-objective optimization based on the multiple targets to be scheduled, and formulating a fuzzy control strategy based on the optimization results; executing the fuzzy control strategy to dynamically update the multiple targets to be scheduled, generating a control signal set, performing bidirectional collaborative scheduling of the liquid flow battery based on the control signal set, and generating a scheduling result; performing reverse tracing according to the scheduling result, performing incremental learning of the fuzzy control strategy according to the tracing path, generating a fuzzy control optimization strategy to replace the fuzzy control strategy, updating the control signal set, and obtaining multi-level control instructions to intelligently schedule the energy of the liquid flow battery.

[0005] According to a second aspect of the present application, a system for intelligent energy scheduling of a liquid flow battery is provided, the system comprising: an energy state information acquisition module for real-time collection of energy operating parameters of the liquid flow battery, dynamic estimation of the electrolyte state according to the energy operating parameters, and acquisition of multiple energy state information; a fuzzy control strategy acquisition module for scheduling and analyzing the liquid flow battery based on the multiple energy state information, setting multiple targets to be scheduled, performing multi-objective optimization according to the multiple targets to be scheduled, and formulating a fuzzy control strategy according to the optimization results; a scheduling result acquisition module for executing the fuzzy control strategy to dynamically update the multiple targets to be scheduled, generating a control signal set, performing bidirectional collaborative scheduling of the liquid flow battery based on the control signal set, and generating a scheduling result; an energy intelligent scheduling execution module for performing reverse tracing according to the scheduling result, performing incremental learning of the fuzzy control strategy according to the tracing path, generating a fuzzy control optimization strategy to replace the fuzzy control strategy, updating the control signal set, and obtaining multi-level control instructions for intelligent scheduling of the energy of the liquid flow battery.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages: This application collects the energy operating parameters of the liquid flow battery in real time, obtains multiple energy state information through dynamic estimation, sets the scheduling target based on this information and performs multi-objective optimization to formulate a fuzzy control strategy, executes the strategy to generate a control signal set for two-way collaborative scheduling, and then performs incremental learning optimization on the strategy through reverse tracing, updates the control signal set to obtain multi-level control instructions, thereby realizing intelligent scheduling of the liquid flow battery energy, making the scheduling more efficient and accurate, and achieving the technical effect of intelligent scheduling of the liquid flow battery energy, reducing power loss, extending battery life, and improving the matching degree between scheduling and actual needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0008] Figure 1 This is a flow chart of an intelligent energy scheduling method for a flow battery provided in an embodiment of the present application.

[0009] Figure 2 This is a structural diagram of an intelligent energy scheduling system for a flow battery provided in an embodiment of the present application.

[0010] Explanation of the reference numerals: energy status information acquisition module 1, fuzzy control strategy acquisition module 2, scheduling result acquisition module 3, energy intelligent scheduling execution module 4. DETAILED DESCRIPTION

[0011] The present application provides an intelligent energy scheduling method and system for liquid flow batteries, which is used to solve the technical problems that traditional liquid flow battery scheduling methods are difficult to accurately capture the energy status in real time, resulting in a low match between scheduling strategies and actual needs, large power losses, and rapid battery life degradation.

[0012] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only some of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0013] It should be noted that the terms "first", "second", etc. in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products or devices.

[0014] Example 1, as Figure 1 As shown, a method for intelligent energy scheduling of a flow battery, wherein the method comprises: Step A100: collecting energy operation parameters of the flow battery in real time, dynamically estimating the electrolyte state according to the energy operation parameters, and obtaining a plurality of energy state information.

[0015] In the embodiments of this application, a flow battery is a battery device that achieves energy conversion and storage through changes in the electrolyte state (e.g., changes in flow rate, concentration, temperature, and other parameters). Intelligent energy scheduling is required to optimize power loss and battery life. Its energy operating parameters are the subject of real-time collection and analysis, and are the subject of the intelligent energy scheduling method. Energy state information includes the electrolyte state of charge (SOC) and state of health (SOH).

[0016] Specifically, first, the energy operating parameters of the flow battery are collected in real time. The core is to obtain key data reflecting the operating status of the flow battery through real-time monitoring by deploying high-precision sensors: for electrolyte flow parameters, electromagnetic flowmeters or turbine flowmeters can be used, which are installed on the electrolyte circulation main pipeline and branch pipelines of the flow battery. By sensing the electromagnetic signal or turbine rotation frequency generated when the electrolyte flows, the circulation flow of the electrolyte per unit time is monitored in real time; for electrolyte concentration parameters, concentration sensors based on ultraviolet-visible spectroscopy can be used, and their probes are inserted into the electrolyte flow channels of the electrolyte storage tank and the inlet and outlet of the battery stack. By detecting the absorption intensity of the electrolyte to light of a specific wavelength, the concentration data of the active substance in the electrolyte is obtained in real time; for electrolyte temperature parameters, platinum resistance temperature sensors or thermocouple sensors can be used, which are close to the inner wall of the pipeline through which the electrolyte flows or embedded in the electrolyte channel of the battery stack, directly contacting the electrolyte to sense temperature changes and collecting electrolyte temperature information in real time. The above sensors are all connected to the system through data transmission lines, and continuously output the collected parameter data at a sampling frequency of milliseconds or seconds, realizing real-time acquisition of energy operation parameters.

[0017] Next, the energy operating parameters of the flow battery are analyzed, and an electrolyte state coupling model is constructed through concentration distribution calculation and extreme value correlation analysis. The decoupling calculation obtains the electrolyte charge and health status information and adds it to the energy state information. The specific steps are detailed in A110-A180.

[0018] Step A200: performing scheduling analysis on the flow battery based on the multiple energy state information, setting multiple targets to be scheduled, performing multi-objective optimization based on the multiple targets to be scheduled, and formulating a fuzzy control strategy based on the optimization results.

[0019] In the embodiment of the present application, the fuzzy control strategy is a strategy formulated based on multiple energy state information of the flow battery by setting multiple targets to be scheduled and performing multi-objective optimization.

[0020] Optionally, power loss and battery life targets are set based on energy status information, mapped to a unified decision space, and the target constraint satisfaction is calculated and ranked in combination with the operating range and attenuation rate constraints, thereby optimizing and formulating the fuzzy control strategy. The specific steps are described in detail in A210-A260.

[0021] Step A300: Execute the fuzzy control strategy to dynamically update the multiple targets to be scheduled, generate a control signal set, perform bidirectional collaborative scheduling on the flow battery based on the control signal set, and generate a scheduling result.

[0022] In the embodiment of the present application, the control signal set includes the real-time charge and discharge power signal of the flow battery and the electrolyte flow control signal.

[0023] In one embodiment of the present application, a fuzzy control strategy is executed, the target priority weight coefficient is dynamically updated, a real-time charge and discharge power signal is generated, and parameters are obtained through electro-hydraulic bidirectional timing collaborative optimization. Based on this, dynamic scheduling is used to generate scheduling results. The specific steps are described in detail in A310-A350.

[0024] Step A400: perform reverse tracing according to the scheduling result, perform incremental learning on the fuzzy control strategy according to the tracing path, generate a fuzzy control optimization strategy to replace the fuzzy control strategy, update the control signal set, obtain multi-level control instructions to intelligently schedule the energy of the flow battery.

[0025] Specifically, first, set the historical cycle and collect execution data according to the scheduling results, analyze the deviation generation parameters, build a traceability path to locate defects, and perform incremental learning on the fuzzy control strategy based on the defects to generate a fuzzy control optimization strategy. The specific steps are detailed in A410-A440.

[0026] Next, after verifying whether the fuzzy control optimization strategy can eliminate the execution deviation, the original strategy is replaced and the signal cascade reconstruction space is constructed to reconstruct the control signal set. The scheduling level is matched to generate multi-level control instructions for intelligent scheduling of energy. The specific steps are detailed in A450-A480.

[0027] Furthermore, step A100 in the method provided in the embodiment of the present application includes: A110: Analyze the energy operation parameters to obtain electrolyte flow parameters, electrolyte concentration parameters, and electrolyte temperature parameters of the flow battery.

[0028] A120: Calculate the concentration distribution of the flow battery based on the electrolyte concentration parameters to generate electrolyte concentration distribution data, perform extreme value correlation analysis based on the electrolyte concentration distribution data, and obtain a concentration polarization dynamic correlation relationship.

[0029] A130: According to the dynamic correlation relationship of concentration polarization, the electrolyte flow parameter is changed and mapped to an equivalent circuit to obtain a variable resistance parameter.

[0030] A140: Based on the variable resistance parameter, the open circuit voltage value of the flow battery is retrieved, the electrolyte concentration distribution data is associated with the open circuit voltage value, and a concentration-voltage compensation coefficient table is constructed.

[0031] A150: Perform nonlinear correction on the electrolyte temperature parameter and the concentration-voltage compensation coefficient table to construct an electrolyte state coupling model.

[0032] A160: Perform dynamic decoupling calculation of electrolyte charge using the electrolyte state coupling model to obtain electrolyte charge state information.

[0033] A170: Perform dynamic decoupling calculation of electrolyte health using the electrolyte state coupling model to obtain health status information.

[0034] A180: Add the electrolyte charge state information and the health state information to the multiple energy state information.

[0035] In the embodiments of this application, the electrolyte's state of charge (SOC) information directly reflects the remaining proportion of active material currently available for energy conversion, serving as a key basis for determining the battery's real-time energy storage capacity and charging and discharging requirements. Health status information reflects the long-term performance of the electrolyte, including the extent of active material loss and concentration stability. It serves as an important reference for assessing battery life degradation trends and developing long-term scheduling strategies.

[0036] Specifically, after collecting the energy operating parameters of the flow battery in real time, these parameters are first analyzed to extract key indicators that reflect the core state of the electrolyte, including electrolyte flow parameters, electrolyte concentration parameters, and electrolyte temperature parameters. For example, assuming that the collected energy operating parameters include the amount of electrolyte circulating in the battery circulation system per unit time, the electrolyte flow parameter is obtained after analysis; the molar concentration of the active material obtained by the concentration sensor is obtained after analysis to obtain the electrolyte concentration parameter; and the electrolyte temperature parameter is obtained through real-time monitoring by the temperature sensor.

[0037] Next, when calculating the concentration distribution based on the electrolyte concentration parameters obtained by analysis, it is necessary to combine the internal structure of the flow battery, such as the positive electrode cavity, negative electrode cavity, electrolyte flow channel, liquid storage tank and other areas, and use computational fluid dynamics and mass transfer coupling model. First, the internal space of the battery is gridded, and the positive electrode reaction area, negative electrode reaction area, inlet and outlet flow channels, etc. are divided into several calculation units; then, the initial electrolyte concentration parameters obtained by analysis are input, for example, the electrolyte concentration in the liquid storage tank is 2.8 mol / L, and parameters such as electrolyte flow rate, diffusion coefficient, and electrode reaction rate are introduced. The concentration changes in each unit are calculated through numerical iteration. For example, the positive electrode area consumes active substances due to oxidation reaction, and the concentration gradually decreases as the reaction proceeds, while the negative electrode area generates active substances due to reduction reaction, and the concentration gradually increases. Finally, concentration distribution data covering all areas of the battery are generated, such as the concentration in the center area of ​​the positive electrode cavity is 2.6 mol / L and the edge area is 2.5 mol / L, the center area of ​​the negative electrode cavity is 3.0 mol / L and the edge area is 2.9 mol / L, and the concentrations at the inlet and outlet of the flow channel are 2.7 mol / L and 2.8 mol / L, respectively.

[0038] Based on these concentration distribution data, extreme value correlation analysis was performed. First, the concentration extreme values ​​at each moment were extracted from the concentration distribution data. From the above distribution data, it can be determined that the maximum concentration at a certain moment is 3.0 mol / L in the center of the negative electrode cavity, and the minimum is 2.5 mol / L in the edge of the positive electrode cavity. Subsequently, the extreme value changes at different moments are continuously tracked, and the maximum and minimum values ​​and their difference are recorded. For example, at this moment, the difference is 3.0-2.5=0.5 mol / L. The battery polarization voltage at the corresponding moment is also monitored synchronously, which reflects the degree of concentration polarization. For example, statistical analysis of multiple sets of data found that when the extreme value difference was 0.4 mol / L, the polarization voltage increased by 0.02 V; when the difference rose to 0.6 mol / L, the polarization voltage jumped to 0.05 V; when the difference exceeded 0.7 mol / L, the polarization voltage suddenly increased to 0.08 V. This established a dynamic correspondence between the concentration extreme value difference and the polarization voltage increase, that is, the dynamic correlation relationship of concentration polarization. The greater the concentration extreme value difference, the more significant the voltage loss caused by concentration polarization.

[0039] Then, according to the aforementioned dynamic correlation with concentration polarization, the greater the extreme concentration difference, the more significant the voltage loss caused by concentration polarization. Changes in electrolyte flow parameters directly affect the extreme concentration difference: when the flow rate increases, the electrolyte circulates faster within the battery, the electrolyte mixes more thoroughly in each region, the concentration distribution becomes more uniform, the extreme concentration difference decreases, and the degree of concentration polarization decreases. When the flow rate decreases, the electrolyte circulation slows down, the concentration difference between each region increases, the extreme concentration difference increases, and the degree of concentration polarization increases. The voltage loss caused by concentration polarization can be represented in the equivalent circuit by a variable resistor, the resistance of which is positively correlated with the degree of concentration polarization.

[0040] When mapping the change of flow parameters to the equivalent circuit, it is necessary to first determine the corresponding change in concentration extreme value difference and the resulting polarization voltage loss increment based on the flow change and the dynamic correlation relationship of concentration polarization; then, based on the relationship of polarization voltage loss = current × variable resistance in the equivalent circuit model, inversely deduce the variable resistance parameters under the flow change. For example, when the flow rate drops from 5L / min to 3L / min, according to the correlation relationship, it can be seen that the concentration extreme value difference increases from 0.4mol / L to 0.6mol / L, and the polarization voltage loss increases from 0.02V to 0.05V. If the circuit current is 10A at this time, the variable resistance parameter increases from 0.02V / 10A=0.002 Increase to 0.05V / 10A=0.005 , realizing the mapping of flow changes to variable resistance parameters of equivalent circuit.

[0041] Based on the obtained variable resistance parameters, the open circuit voltage of the flow battery is retrieved, assuming that the open circuit voltage is 1.4 V. The previously generated electrolyte concentration distribution data is associated with this open circuit voltage value. For example, a positive electrode concentration of 3.0 mol / L corresponds to a voltage of 1.45 V, and a negative electrode concentration of 2.5 mol / L corresponds to a voltage of 1.35 V. Using this associated data, a concentration-voltage compensation coefficient table is constructed. The table records the compensation values ​​required for the open circuit voltage at different concentrations. For example, for every 0.1 mol / L decrease in concentration, the voltage compensation is -0.01 V.

[0042] Afterwards, when performing nonlinear correction fusion of the electrolyte temperature parameters and the concentration-voltage compensation coefficient table, it is necessary to first clarify that the concentration-voltage compensation coefficient table originally only reflects the relationship between different electrolyte concentrations and corresponding voltage compensation values, but temperature changes will change the physical and chemical properties of the electrolyte, such as the diffusion capacity of active substances, the electrode reaction rate, etc., and thus nonlinearly affect the correspondence between concentration and voltage. That is, the compensation coefficient does not necessarily change at a fixed ratio when the temperature increases or decreases, but the degree of influence of different temperature ranges on the concentration-voltage relationship varies.

[0043] During the fusion process, technicians in this field need to use the concentration-voltage compensation coefficient at the reference temperature as a basis, combined with the actual collected temperature parameters, to analyze the specific impact of the deviation between the temperature and the reference temperature on the concentration-voltage relationship. By introducing a nonlinear correction function, such as a nonlinear mapping algorithm based on an improved BP neural network, the values ​​in the compensation coefficient table are dynamically adjusted. Specifically, when constructing the model, the historically collected electrolyte concentration parameters, temperature parameters, and corresponding actual voltage compensation values ​​are first used as a training data set, where the input layer is set as the concentration and temperature parameters, and the output layer is set as the corrected voltage compensation coefficient. The hidden layer weights and thresholds are iteratively optimized through a neural network algorithm to learn the nonlinear correlation between the concentration and voltage compensation coefficients in different temperature ranges. For example, the effect of temperature on the compensation coefficient changes logarithmically at low temperatures and exponentially at high temperatures, so that the model can accurately capture the nonlinear coupling relationship between the two. After multiple iterative training until the prediction error is lower than the set threshold, the model can receive the current concentration and temperature parameters in real time and output a temperature-corrected voltage compensation coefficient, which not only reflects the voltage compensation demand caused by concentration changes, but also accurately reflects the nonlinear interference of temperature fluctuations on this compensation relationship. Through this fusion process, an electrolyte state coupling model was finally constructed that can simultaneously reflect the effects of concentration and temperature on the electrolyte state.

[0044] Next, when performing dynamic decoupling calculations of electrolyte charge through the electrolyte state coupling model, the core is to separate the cross-interference of factors such as concentration and temperature, and focus solely on the quantification of the state of charge. The state of charge of the electrolyte essentially reflects the remaining proportion of active substances that can participate in energy conversion, and concentration changes and temperature fluctuations will indirectly interfere with the calculation of this proportion by affecting the voltage signal. The coupling model has integrated the concentration-voltage compensation relationship and the nonlinear correction law of temperature on the compensation coefficient. During the decoupling calculation, the model will first eliminate the interference of temperature fluctuations on the voltage signal, and lock the true correlation between concentration and voltage based on the voltage compensation value after temperature correction; then eliminate the local signal deviation caused by uneven concentration distribution, combine the overall conversion ratio of active substances in the concentration distribution data, such as the completion rate of the redox reaction of active substances during charging and discharging, and remove the cross-influence through the model algorithm. Finally, the state of charge information that only reflects the remaining amount of active substances is calculated.

[0045] When performing dynamic decoupling calculations of electrolyte health using an electrolyte state coupling model, it is necessary to isolate the interference of short-term operating parameters (such as instantaneous concentration fluctuations and temperature changes) on long-term performance evaluation and focus on the health decay characteristics of the electrolyte. The health state primarily reflects the long-term effectiveness of the electrolyte and is directly related to long-term indicators such as the degree of active material loss and concentration stability. Based on historical operating data and real-time monitoring results, the coupling model first eliminates the impact of short-term concentration fluctuations (such as concentration changes caused by a single charge and discharge cycle) and temperature fluctuations, focusing on long-term trend analysis. For example, by comparing the initial total active material amount with the current total effective active material amount, the cumulative loss rate of the active material is calculated; by analyzing the long-term concentration distribution data for persistent stratification and precipitation, the concentration stability is evaluated. After the model decouples and isolates short-term interference, these long-term indicators are combined to obtain health status information reflecting the overall health of the electrolyte.

[0046] Finally, the obtained electrolyte charge state information and health state information are added to the multiple energy state information. The addition of the two makes the multiple energy state information cover both the real-time energy available state of the battery and its long-term health performance state.

[0047] By analyzing key parameters, building correlation and coupling models, and performing dynamic decoupling calculations, accurate acquisition of the electrolyte charge state and health state is achieved, providing a reliable status basis for the energy scheduling of the flow battery.

[0048] Furthermore, step A200 in the method provided in the embodiment of the present application includes: A210: Perform temperature analysis based on the electrolyte charge state information, perform charge and discharge analysis on the flow battery according to the operating temperature parameters, and set a power loss target.

[0049] A220: Perform attenuation analysis based on the health status information, perform battery life analysis on the flow battery according to the health attenuation rate, and set a battery life target.

[0050] A230: Map the power loss target and the battery life target to a unified decision space, and set multiple targets to be scheduled.

[0051] A240: Setting an operating range constraint based on the electrolyte charge state information, and performing a decay rate constraint based on the health state information.

[0052] A250: Calculate the multiple targets to be scheduled based on the operating range constraint and the decay rate constraint, generate target constraint satisfaction, sort them in descending order according to the target constraint satisfaction, and generate a target dominance ranking.

[0053] A260: Perform multi-objective optimization on the multiple targets to be scheduled based on the target dominance ranking, and formulate the fuzzy control strategy according to the optimization results.

[0054] In an embodiment of the present application, the targets to be scheduled include a power loss target set based on the electrolyte charge state information through temperature analysis and charge and discharge analysis, and a battery life target set based on the health status information through attenuation analysis and battery life analysis.

[0055] Optionally, when performing temperature analysis based on electrolyte state of charge information, the current accurate electrolyte state of charge information is first obtained through dynamic decoupling calculation of the electrolyte state coupling model. For example, the state of charge obtained by decoupling calculation is 72%, which comes from the dynamic estimation and model solution of parameters such as electrolyte flow, concentration, and temperature. Subsequently, combined with the real-time collected operating temperature parameters, such as continuous monitoring by a temperature sensor, the current battery operating temperature is stable at 30°C. Those skilled in the art can carry out charge and discharge simulations and experiments under different temperature conditions: For example, based on the state of charge of 72%, the charge and discharge processes at 25°C, 30°C, 35°C, and 40°C are tested respectively, and the internal resistance changes and power output data at each temperature are recorded. Analysis found that when the temperature rises from 30°C to 36°C, due to the change in the diffusion rate of the active material in the electrolyte, the charge and discharge internal resistance increases from 0.03Ω to 0.05Ω, and the power loss rate increases from 1.8% to 4.2%; when the temperature is below 28°C, although the loss rate is low, the charge and discharge efficiency decreases. The comprehensive efficiency and loss balance are taken into consideration. Based on this analysis result, the power loss target is set at no more than 3% of the total output power to ensure high-efficiency and low-loss operation within a reasonable temperature range.

[0056] When performing decay analysis based on health status information, the current electrolyte health status is first obtained through dynamic decoupling calculations within the electrolyte state coupling model. For example, a calculated health status of 85% is derived, incorporating long-term monitoring indicators such as active material loss and concentration stability. Next, the health decay rate is calculated based on historical operating data. For example, by recording the health status changes over the past six months, from an initial 88% to the current 85%, an average monthly decay of 0.5% is obtained. Further analysis is conducted in conjunction with battery life decay: If a monthly decay rate of 0.5% is maintained, combined with battery design life parameters such as the theoretical number of active material cycles, the battery can be estimated to maintain a service life of 10 years. If the decay rate rises to 0.7% per month, the service life will be shortened to less than 8 years. To balance usage and lifespan, the battery lifespan target is set based on this analysis to keep the health decay rate below 0.5% per month, ensuring that the actual battery service life is at least 90% of the design lifespan.

[0057] Next, the power loss target ≤ 3% and the battery life target, that is, the decay rate ≤ 0.5% / month, are mapped to a unified decision space. This decision space uses power loss rate and health decay rate as two dimensions, and converts the two targets into specific coordinate points in the space. For example, the power loss target corresponds to the coordinates (3%, 0), and the battery life target corresponds to the coordinates (0, 0.5% / month). This mapping clarifies the specific locations and ranges of multiple targets to be scheduled.

[0058] Next, operating range constraints are set based on the electrolyte state of charge information. For example, based on the characteristics of the state of charge, the operating range is set to 20%-90% to avoid performance degradation caused by excessive discharge when the state of charge is below 20%, or the risk of overcharging when the state of charge is above 90%. The decay rate is constrained based on the health status information. For example, when the health status is below 85%, the upper limit of the decay rate is further tightened from 0.5% / month to 0.4% / month to prevent rapid deterioration of the health status.

[0059] Then, when calculating the target constraint satisfaction for multiple scheduled targets based on the operating range constraints and decay rate constraints, the specific requirements of each constraint must be clarified: the operating range constraint requires that the electrolyte state of charge must be maintained within a set range, the decay rate constraint requires that the health decay rate not exceed a set threshold, and the scheduled target itself must meet the power loss rate not exceeding a set target. During the calculation, each scheduled target is checked item by item to see if it meets these constraints: first, determine whether its corresponding state of charge is within the operating range of 20%-90%, then check whether the health decay rate does not exceed 0.5% / month, and finally confirm whether the power loss rate is controlled within 3%. The target dominance sorting rules are shown in Table 1.

[0060] For example, if a target to be scheduled has a power loss rate of 2.8%, which satisfies ≤3%, a health decay rate of 0.45% / month, which satisfies ≤0.5% / month, and the state of charge is maintained at 50%, falling between 20% and 90%, then the constraint satisfaction for this target is 100%. Another target to be scheduled has a power loss rate of 3.2%, which does not meet ≤3%, a health decay rate of 0.55% / month, which does not meet ≤0.5% / month, and thus the constraint satisfaction is 0%. The targets are sorted in descending order of constraint satisfaction, generating a target dominance ranking. The target with 100% satisfaction is ranked first and prioritized for optimization.

[0061] Finally, based on the target dominance sorting rules, multiple targets to be scheduled are prioritized and hierarchical to generate a target dominance relationship map. After multi-target conflict analysis and optimization resolution, weights are allocated to generate the dominance target strength and add it to the fuzzy control strategy. The specific steps are described in detail in A261-A264.

[0062] By setting specific goals based on energy state information, mapping them to a unified space, setting constraints and sorting them, accurate planning of flow battery scheduling goals is achieved, providing a clear basis for the subsequent multi-objective optimization and fuzzy control strategy formulation.

[0063] Table 1: Target dominance sorting rules table Furthermore, step A260 in the method provided in the embodiment of the present application includes: A261: Prioritize the multiple targets to be scheduled based on the target dominance sorting rule to generate a target dominance relationship graph.

[0064] A262: Perform multi-objective conflict analysis based on the objective dominance relationship graph to generate a conflict path, optimize and resolve the multiple objectives to be scheduled based on the conflict path, and generate an optimization result.

[0065] A263: Perform weight allocation based on the optimization result to obtain multiple priority weight coefficients, analyze the optimization result according to the multiple priority weight coefficients, and generate a dominant target strength.

[0066] A264: Add the dominant target strength to the fuzzy control strategy.

[0067] Specifically, when prioritizing multiple targets to be scheduled based on the target dominance sorting rule, they need to be divided into four layers according to the constraint satisfaction of each target, 100%, 67%, 33%, and 0%. For example, there are four targets to be scheduled: the state of charge of target 1 is 60%, which is in the range of 20%-90%; the health decay rate is 0.4% / month≤0.5% / month; the power loss rate is 2.8%≤3%. All three items are met, the constraint satisfaction is 100%, and they are classified into the first layer; the second, third, and fourth layers are based on the target dominance sorting rules of step A250, and so on. Through this layering, a target dominance relationship map is generated. In the map, the first layer target dominates the second, third, and fourth layer targets, the second layer dominates the third and fourth layers, and so on, clearly presenting the priority hierarchy relationship of each target.

[0068] Next, when performing multi-objective conflict analysis based on the objective dominance relationship graph, it is necessary to identify conflicts between objectives at different levels. For example, in the first-level graph, Objective 1 (power loss rate 2.8%, life decay rate 0.4%) conflicts with Objective 5 (power loss 2.5%, life decay 0.52%) at the second level. Objective 5 offers lower power loss, but the life decay rate exceeds the 0.5% / month constraint, creating a conflict with the life target of Objective 1. By tracing the associated paths of the objectives in the graph, a conflicting path is generated: reduced power loss → increased charge and discharge power → faster electrolyte circulation → increased active material loss → increased life decay rate. This conflicting path is then optimized and resolved. For example, by adjusting the charge and discharge power to a moderate level, power loss is controlled at 2.6%, while the life decay rate is reduced to 0.48%. This satisfies both the power loss target of ≤3% and the life decay constraint of ≤0.5% / month, generating an optimized result.

[0069] When assigning weights based on optimization results, the priority weights for each objective must be determined based on the grid operating scenario. Assume that the optimization results include two feasible options: Option 1, with a power loss of 2.6% and a lifespan reduction of 0.48%; Option 2, with a power loss of 2.9% and a lifespan reduction of 0.42%. If the grid is currently experiencing peak demand and prioritizing energy output efficiency, the power loss objective can be weighted at 0.6, and the battery life objective at 0.4. The overall score for Option 1 is calculated as: 2.6 × 0.6 + 0.48 × 0.4 = 1.56 + 0.192 = 1.752; the overall score for Option 2 is: 2.9 × 0.6 + 0.42 × 0.4 = 1.74 + 0.168 = 1.908. Option 1, with the lower score, is superior, and its priority weights are determined as: 0.6 for power loss and 0.4 for lifespan. The optimization results are analyzed according to the weight to generate the dominant target intensity: Scheme 1 is better than Scheme 2 in terms of power loss index and has a higher weight, so the dominant target intensity of Scheme 1 to Scheme 2 is 0.75.

[0070] Finally, the dominant objective strength is added to the fuzzy control strategy, allowing the strategy to prioritize the more favorable objective based on its strength. For example, when scheduling, Option 1 is prioritized, ensuring that efficiency requirements are met while also considering battery life, making the fuzzy control strategy more aligned with actual operational needs.

[0071] By clarifying the priority of goals according to the degree of constraint satisfaction, analyzing conflict paths and optimizing to resolve contradictions, generating dominant intensity based on scenario allocation weights, and integrating intensity into the control strategy, the fuzzy control strategy can ultimately respond accurately to the multi-objective optimization results and improve the scientificity and effectiveness of liquid flow battery energy scheduling.

[0072] Furthermore, step A300 in the method provided in the embodiment of the present application includes: A310: Execute the fuzzy control strategy, dynamically update the priority weight coefficients of multiple targets to be scheduled according to the parameters of the power grid operating condition change, and generate multiple target weight coefficients.

[0073] A320: Perform power analysis on the charge and discharge of the flow battery based on the multiple target weight coefficients to generate a real-time charge and discharge power signal.

[0074] A330: Perform flow compensation analysis on the temperature distribution of the flow battery based on the multiple target weight coefficients to generate an electrolyte flow control signal.

[0075] A340: Performing electro-liquid bidirectional timing collaborative optimization on the flow battery according to the real-time charge and discharge power signal and the electrolyte flow control signal to obtain timing collaborative optimization parameters.

[0076] A350: Dynamically schedule the flow battery according to the timing collaborative optimization parameters to generate the scheduling result.

[0077] Specifically, the system first records operating condition changes based on the flow battery's energy operating parameters, executes a fuzzy control strategy to detect power loss and / or battery lifespan mutations, and then determines the corresponding target weight coefficients. The specific steps are detailed in A311-A315. Furthermore, the control signal set is constructed from the flow battery's real-time charge and discharge power signals and the electrolyte flow control signal.

[0078] Next, after generating multiple target weight coefficients, when performing power analysis on the charge and discharge of the flow battery based on these coefficients, the optimal power range must be determined in combination with the weight distribution results. For example, if the target weight coefficients are 0.7 for the power loss target and 0.3 for the battery life target, it means that the current focus is on reducing energy loss. Through the power analysis model, the real-time load demand of the power grid is input, for example 120kW, and combined with the weight calculation, it is concluded that the charge and discharge power must be controlled within the range of 90-100kW. This range can not only meet most load demands but also avoid the increase in internal resistance caused by excessive power. Based on this, a real-time charge and discharge power signal is generated, such as 95kW.

[0079] Furthermore, the construction of a power analysis model must be guided by the target weight coefficient, integrating grid load characteristics and battery operating constraints. First, the model input parameters are determined, including the real-time grid load demand, the target weight coefficient, the characteristic curve of the battery internal resistance as a function of power, such as the nonlinear relationship between power and internal resistance, and the charge and discharge efficiency threshold. Next, a two-layer analysis structure is established. The first layer is the load matching unit. Based on the high priority of power loss in the target weight, it initially selects a power range that can meet at least 80% of the load demand, such as 80-120kW for a 120kW load. The second layer is the loss assessment unit. Based on the internal resistance characteristic curve, it calculates the power loss rate corresponding to different power levels within this range. Furthermore, it considers the battery life weight to eliminate ranges where excessive power leads to a sharp increase in health degradation rate. Finally, through iterative calibration, the target weight coefficient is converted into a balanced weight between loss and life, narrowing the initial range to ultimately determine the optimal power range that both matches the load and meets the weighting requirements, such as 90-100kW. A real-time response algorithm is then embedded to ensure dynamic output of specific power values ​​based on load fluctuations, completing the model construction.

[0080] Then, when performing flow compensation analysis on the temperature distribution of the flow battery based on multiple target weight coefficients, it is necessary to balance the impact of temperature on different targets according to the weight priority. Assuming that the current temperature distribution of the battery is 36°C in the positive area and 32°C in the negative area, with a temperature difference of 4°C, combined with the target weight, that is, the battery life target of 0.3, it can be seen that excessive temperature difference will accelerate the decay of active substances. For example, when the temperature difference exceeds 5°C, the health decay rate increases from 0.4% / month to 0.6% / month. Through the flow compensation model calculation: the electrolyte flow rate needs to be adjusted from the original 4L / min to 5L / min to enhance the circulation heat exchange and reduce the temperature difference to within 2°C. Based on this, an electrolyte flow control signal is generated, such as 5L / min.

[0081] Furthermore, the construction of the flow compensation model needs to take temperature distribution data and target weights as core inputs, and combine the correlation characteristics of electrolyte flow to temperature regulation to achieve dynamic compensation. First, determine the model input parameters, including the real-time temperature distribution of the battery, the target weight coefficient, and the historical correlation data of the electrolyte flow and temperature field changes, such as the rate of change of temperature difference under different flow rates and the correspondence between the health decay rate and the temperature difference. Secondly, build a temperature difference-flow correlation module, and obtain the temperature difference regulation effect under different flow rates through experiments or simulations. For example, record the corresponding relationship that the temperature difference is maintained at 4°C when the flow rate is 4L / min, the temperature difference is reduced to 3°C when the flow rate is 4.5L / min, and the temperature difference is reduced to 2°C when the flow rate is 5L / min, and establish a nonlinear mapping curve between flow rate and temperature difference.

[0082] At the same time, a target weighting mechanism is embedded. When the battery life target weight is high, the model prioritizes suppressing health decay. A temperature difference safety threshold is set, such as ≤2°C, and the minimum flow adjustment value required to meet this threshold is reversed based on the mapping curve, such as increasing it from 4L / min to 5L / min. Finally, a constraint verification step is added to ensure that the calculated flow value does not lead to excessive power loss. For example, excessive flow will increase pump consumption. By balancing the temperature regulation effect with additional losses, the final electrolyte flow control signal is output, completing the construction of the flow compensation model.

[0083] Subsequently, when performing bidirectional electro-hydraulic time-series coordinated optimization based on the real-time charge and discharge power signal of 95kW and the electrolyte flow control signal of 5L / min, dynamic matching of power changes and flow adjustments is required. For example, if the grid load suddenly increases to 130kW, the real-time charge and discharge power signal must be increased to 105kW, and the flow control signal must be simultaneously increased to 5.5L / min. If the flow rate is not adjusted in a timely manner, the temperature may rise to 38°C, causing the power loss rate to exceed 3%. If the flow rate is over-adjusted to 6L / min, while the temperature can be controlled, it will increase pump consumption and increase overall losses. Through the time-series coordinated algorithm, the corresponding relationship between power and flow rate is determined. For example, a power of 95-105kW corresponds to a flow rate of 5-5.5L / min, and the adjustment delay does not exceed 2 seconds. Ultimately, the time-series coordinated optimization parameters are obtained.

[0084] Finally, when dynamically scheduling flow batteries according to time-series collaborative optimization parameters, the system automatically adjusts based on real-time operating conditions. For example, during stable load conditions, 95kW power and 5L / min flow rate are maintained, with a stable power loss rate of 2.2% and a healthy decay rate of 0.38% per month. During load fluctuations, the system rapidly responds to these parameters, with power and flow rate changing synchronously to ensure both losses and decay are within target ranges. This ultimately generates a scheduling result that includes time period, power, flow rate, loss rate, and decay rate.

[0085] Through target weight-guided power analysis, flow compensation, and electro-hydraulic timing collaborative optimization, precise linkage between the electrical system and the hydraulic system of the flow battery is achieved, balancing power loss and battery life while meeting grid demand, thereby improving the stability and economy of energy scheduling.

[0086] Furthermore, step A310 in the method provided in the embodiment of the present application includes: A311: Record the environmental operating conditions based on the energy operating parameters of the flow battery to obtain battery operating condition change parameters.

[0087] A312: Execute the fuzzy control strategy, detect the multiple targets to be scheduled according to the battery operating condition change parameters, and generate a detection result, which is a power loss mutation result and / or a battery life mutation result.

[0088] A313: When the detection result is the power loss mutation result, the priority weight of the power loss target is increased to the highest level, and the first target weight coefficient is determined.

[0089] A314: When the detection result is a sudden change in the battery life, the priority weight of the battery life target is increased to the highest level, and a second target weight coefficient is determined.

[0090] A315: When the detection result is the power loss mutation result and the battery life mutation result, a scheduling impact analysis is performed on the power loss mutation result and the battery life mutation result, and a third target weight coefficient is determined based on the impact factor.

[0091] Specifically, when recording environmental operating conditions based on the energy operating parameters of a flow battery, it is necessary to continuously collect key operating data such as electrolyte flow, concentration, and temperature, and record their changing trends over time. For example, electrolyte flow, concentration, and temperature could be recorded every five minutes, while also correlating this data with information such as grid-side load fluctuations. By integrating this data, battery operating condition change parameters can be generated, reflecting real-time changes in the battery's operating environment and its own status.

[0092] When executing the fuzzy control strategy, multiple targets to be scheduled, namely power loss targets and battery life targets, are monitored in real time based on the above-mentioned battery operating condition change parameters. Specifically, the power loss mutation threshold is set at 2%, meaning that any increase in the power loss rate by more than 2% within 1 hour is considered a mutation; the battery life mutation threshold is set at 0.3% / month, meaning that any increase in the health decay rate by more than 0.3% / month within 1 hour is considered a mutation. If the power loss rate is detected to have increased from 1.8% to 4.2%, exceeding the 2% threshold, a power loss mutation result is generated; if the health decay rate is detected to have increased from 0.4% / month to 0.8% / month, exceeding the 0.3% threshold, a battery life mutation result is generated; if both exceed the corresponding thresholds, both mutation results are generated simultaneously.

[0093] If the detection result indicates a sudden change in power loss, it indicates that the current energy output stability is severely affected and that power transmission efficiency must be prioritized. Therefore, the power loss target priority weight is increased to the highest level. For example, if the original power loss target weight is 0.5 and the battery life target weight is 0.5, the power loss target weight is adjusted to 0.8 and the battery life target weight is 0.2, which are determined as the first target weight coefficients.

[0094] If the test result indicates a sudden change in battery life, it indicates that the long-term battery performance is at risk of rapid degradation and that protecting battery life is a priority. Therefore, the priority weight of the battery life target is increased to the highest level. For example, the battery life target weight may be adjusted to 0.8 and the power loss target weight may be adjusted to 0.2, with these weights determined as the second target weight coefficients.

[0095] When the test results include both power loss mutations and battery life mutations, the scheduling impact analysis needs to quantify the impact of both on scheduling from the two dimensions of real-time operation and long-term performance. First, for power loss mutations, analyze their impact on current energy output: by comparing the output power data before and after the mutation, determine the degree of interference of a 15% power reduction on the grid load balance and power supply stability. For example, whether this reduction causes local power supply gaps or triggers the activation of backup power sources, so as to assess its urgency and impact range on real-time scheduling; for battery life mutations, analyze its impact on long-term operation: based on the battery design life, assuming it is 10 years, calculate the increase in life cycle costs and maintenance frequency caused by a 3-year life reduction. For example, a shortened life will shorten the replacement cycle and increase the total maintenance cost by 20%, so as to assess its sustained impact on the long-term scheduling strategy. On this basis, by combining the real-time power supply priority and the long-term economic weight, the real-time impact of the power loss mutation is quantified as an impact factor of 0.6, and the long-term impact of the battery life mutation is quantified as an impact factor of 0.4. The sum of the two is 1, and it conforms to the principle that the greater the impact, the higher the weight. Finally, the weight ratio of the power loss target and the battery life target in the third target weight coefficient is determined based on this factor.

[0096] By recording changes in operating conditions, detecting mutation results, and dynamically adjusting priority weight coefficients by scenario, we achieve precise adaptation of the importance of the targets to be scheduled under different operating conditions, ensuring that the energy scheduling of the flow battery can both respond to emergencies and balance short-term efficiency and long-term performance.

[0097] Furthermore, step A400 in the method provided in the embodiment of the present application includes: A410: Setting a historical scheduling period according to the scheduling result, and collecting execution result data of the historical scheduling period.

[0098] A420: Perform deviation analysis based on the execution result data in combination with the fuzzy control strategy to generate execution deviation parameters.

[0099] A430: Construct a reverse traceback path according to the execution deviation parameter, locate the deviation according to the reverse traceback path, and obtain a traceback defect location point.

[0100] A440: Perform incremental learning on the fuzzy control strategy based on the traceable defect location point to generate the fuzzy control optimization strategy.

[0101] In one embodiment, when setting a historical scheduling cycle according to the scheduling results, the length of the cycle needs to be determined in combination with the time span and fluctuation characteristics of the scheduling results. For example, if the scheduling results show that there have been three obvious cases of excessive power loss in the past 30 days, and the interval is about 10 days, then 10 days is set as a historical scheduling cycle. In each cycle, execution result data is collected, including key indicators such as actual charging and discharging power, power loss rate, health decay rate, electrolyte flow rate, temperature distribution, etc. For example, the actual average power loss rate in a certain period is 3.2%, while the expected value of the fuzzy control strategy is 3%; the actual health decay rate is 0.52% / month, and the strategy expects 0.5% / month. These data need to be recorded in detail and associated with the strategy parameters of the corresponding time period.

[0102] Next, when performing deviation analysis based on the execution result data combined with the fuzzy control strategy, the actual indicators must be compared with the target values ​​preset by the strategy. For example, for the power loss indicator, the absolute deviation between the actual value and the expected value is calculated as 3.2% - 3% = 0.2%, and the relative deviation is 0.2% / 3% ≈ 6.7%. For the health decay rate, the deviation is calculated as 0.52% - 0.5% = 0.02% / month, and the relative deviation is 0.02% / 0.5% = 4%. At the same time, the associated factors causing the deviation are analyzed, such as whether the power loss deviation is related to excessively high charge and discharge power signals, and whether the health decay deviation is related to insufficient electrolyte flow control. Combining these deviation values ​​and the correlation analysis results, execution deviation parameters are generated, such as 0.2% power loss deviation, 0.02% health decay deviation / month, and corresponding associated factor labels.

[0103] Then, when constructing a reverse tracing path based on the execution deviation parameter, it is necessary to trace back from the deviation indicator layer by layer to each link of the strategy. For example, for a power loss deviation of 0.2%, first trace back to the real-time charging and discharging power signal, and find that the power signal in a certain period is 105kW, which is higher than the 95-100kW range recommended by the strategy model; then trace back to the target weight coefficient, the power loss target weight for this period is 0.6, and the battery life target is 0.4, but the internal resistance growth under high power is not fully considered during power analysis; continue to trace back to the target dominance ranking, and find that there are omissions in the target constraint satisfaction calculation for this period, and the impact of high power on loss is not fully included. Through such hierarchical tracing, a reverse tracing path of power loss deviation → power signal → target weight coefficient → target constraint satisfaction calculation is constructed, and the defect location point is traced according to the path location. For example, it is determined that the defect point is that the target constraint satisfaction calculation link does not fully consider the nonlinear relationship between power and loss.

[0104] Finally, based on the traceability of defect location points, the electrical-hydraulic collaborative timing and life target defect points are determined. After corresponding tests and confidence analysis, the fuzzy control strategy is incrementally learned to generate a fuzzy control optimization strategy, which is described in detail in A441-A444.

[0105] By setting up a periodic data collection, analyzing deviations, and tracing back to locate defects, the key links that lead to execution deviations in the fuzzy control strategy are accurately identified, providing a clear improvement direction for subsequent incremental learning of the strategy and generation of optimized strategies, thereby improving the adaptability and accuracy of the strategy.

[0106] Furthermore, step A440 in the method provided in the embodiment of the present application includes: A441: Perform defect analysis based on the traceable defect location points to determine the electrical-hydraulic collaborative timing defect points and life target defect points.

[0107] A442: Perform a power reduction test based on the electric-hydraulic coordinated timing defect point, generate a first test result for confidence analysis, and obtain a first confidence level.

[0108] A443: Perform an incremental attenuation test based on the lifetime target defect point, generate a second test result for confidence analysis, and obtain a second confidence level.

[0109] A444: Perform incremental learning on the fuzzy control strategy based on the first test result combined with the first confidence level, and the second test result combined with the second confidence level to generate the fuzzy control optimization strategy.

[0110] Optionally, when performing defect analysis based on tracing defect location points, first combine the specific links of the location points. For example, if the tracing defect location point is that the target constraint satisfaction calculation does not fully consider the nonlinear relationship between power and loss, further analysis will find that: in the electric-hydraulic coordinated scheduling, when the charge and discharge power signal suddenly increased from 95kW to 105kW, the electrolyte flow control signal did not follow up synchronously, causing the internal temperature of the battery to rise from 32°C to 36°C in a short period of time, and the power loss rate increased from 2.2% to 3.2%. This is an electric-hydraulic coordinated timing defect point; at the same time, in the life target setting, the original strategy did not include the accelerated effect of temperatures above 35°C on health decay. When the temperature continued to be above 35°C, the health decay rate increased from 0.5% / month to 0.65% / month, exceeding the expected target. This is a life target defect point.

[0111] When conducting power reduction tests based on the timing defects of electro-hydraulic synergy, it is necessary to simulate the timing synergy effects at different power levels. The specific steps are: starting with the charge and discharge power at 105kW, gradually decreasing in steps of 5kW to 85kW, maintaining each power point for 10 minutes, and recording the response delay time of the electrolyte flow control signal and the corresponding power loss rate. For example, the test found that: at 105kW power, the flow response delay was 2 seconds, with a loss rate of 3.2%; when the power dropped to 95kW, the flow response delay was shortened to 0.5 seconds, with a loss rate of 2.3%; when the power dropped to 90kW, there was no flow delay, with a loss rate of 2.1%. The 95kW power point, which optimally balances loss rate and response delay, was selected as the key test result, i.e., the first test result. A confidence analysis was performed on this result, repeating the test 10 times. If the loss rate remained stable between 2.2% and 2.4% and the response delay was ≤ 0.5 seconds in 9 of the 10 tests, the first confidence level was 90%.

[0112] When performing a decay-increase test based on a target lifetime defect point, it is necessary to simulate the health decay trend at different temperatures. The specific steps are: starting from 30°C, the battery temperature is gradually increased by 2°C to 40°C, and the battery is operated continuously for 24 hours at each temperature point, and the changes in the health decay rate are recorded. For example, the test found that at a temperature of 30°C, the decay rate was 0.5% / month; at 32°C, 0.52% / month; at 35°C, 0.58% / month; and at 38°C, 0.68% / month. When the electrolyte flow rate was increased from 5L / min to 5.5L / min at 35°C, the decay rate dropped to 0.53% / month. This is the second test result. A confidence analysis was performed on this result. The test was repeated 8 times, and the decay rate was stable between 0.52% and 0.54% in 7 of the 8 tests, resulting in a second confidence level of 87.5%.

[0113] Finally, based on the first test results with a 90% confidence level, the fuzzy control strategy's power and flow coordination timing parameters were adjusted to shorten the response threshold. Based on the second test results with an 87.5% confidence level, the strategy's correlation coefficient between temperature and flow was modified. Through incremental learning in these two areas, a fuzzy control optimization strategy was ultimately generated that simultaneously optimizes electro-hydraulic synergy efficiency and life degradation control.

[0114] Through targeted defect analysis, step-by-step testing and confidence verification, accurate iteration of the fuzzy control strategy was achieved, so that the optimized strategy can more efficiently balance power loss and battery life, and improve the stability and long-term effectiveness of liquid flow battery energy scheduling.

[0115] Furthermore, step A400 in the method provided in the embodiment of the present application includes: A450: Verify whether the fuzzy control optimization strategy can eliminate the execution deviation parameter of the fuzzy control strategy, and generate a verification result.

[0116] A460: When the verification result shows that the fuzzy control optimization strategy can eliminate the execution deviation parameter of the fuzzy control strategy, a replacement instruction is generated, and the fuzzy control optimization strategy is replaced by the fuzzy control strategy through the replacement instruction to construct a signal cascade reconstruction space.

[0117] A470: Mapping the control signal set to the signal cascade reconstruction space to perform reconstruction compensation to generate a control reconstruction signal set.

[0118] A480: Perform scheduling analysis based on the control and reconstruction signal set, define multiple scheduling levels, match the multiple scheduling levels with the control and reconstruction signal set, and generate the multi-level control instructions.

[0119] In one embodiment, first, a simulation verification is performed based on historical execution data. For example, the original fuzzy control strategy has execution deviation parameters of 0.2% power loss deviation and 0.02% health decay deviation per month. The fuzzy control optimization strategy is applied to the same historical scheduling scenario, and the corresponding power loss rate and health decay rate are simulated and calculated. If the simulation results show that the power loss deviation is reduced to within 0.05% and the health decay deviation is reduced to within 0.005% per month, and they are stable in this range for 10 consecutive scheduling cycles, a verification result is generated that the optimization strategy can eliminate the execution deviation; if there is still a deviation outside the acceptable range, a verification result is generated that it cannot be eliminated temporarily.

[0120] Next, when the verification result shows that the fuzzy control optimization strategy can eliminate the execution deviation parameters, the system generates a replacement instruction, which formally replaces the original fuzzy control strategy with the optimized strategy. At the same time, a signal cascade reconstruction space is constructed to integrate the relationships between the various signals in the control signal set. For example, a dynamic mapping model is established between the real-time charge and discharge power signal and the electrolyte flow control signal. This ensures that the replaced strategy maintains logical consistency when calling various signals.

[0121] Then, when mapping the control signal set to the signal cascade reconstruction space for reconstruction compensation, the synergistic relationship of the original signal needs to be adjusted according to the parameters of the optimization strategy. For example, in the original control signal set, when the charge and discharge power signal is 100kW, the corresponding electrolyte flow signal is 5L / min, resulting in large temperature fluctuations. Through reconstruction compensation, based on the modified temperature-flow correlation coefficient in the optimization strategy, the flow signal is adjusted to 5.2L / min, stabilizing the temperature at 32°C and reducing the power loss rate from 2.5% to 2.2%, generating a new control reconstruction signal set.

[0122] Finally, a dispatch analysis is performed based on the control reconstruction signal set, and multiple dispatch levels are defined based on the characteristics of grid load fluctuations. For example, the grid operating conditions are divided into three levels: peak (load > 120kW); flat (80-120kW); and valley (< 80kW). These correspond to the high-power-high flow, medium-power-medium flow, and low-power-low flow signal intervals in the control reconstruction signal set, respectively. Each level is matched with the corresponding reconstructed signal to generate multi-level control instructions. For example, the peak level instructions are 110kW charge / discharge power and 5.5L / min electrolyte flow, the flat level instructions are 90kW and 5L / min, and the valley level instructions are 70kW and 4.5L / min.

[0123] By verifying the effectiveness of the optimization strategy, replacing the strategy and reconstructing the control signal, and matching the scheduling level to generate instructions, precise coordination and hierarchical response of the control signal are achieved, which improves the adaptability and control accuracy of the intelligent scheduling of liquid flow battery energy.

[0124] In summary, the intelligent energy scheduling method for a flow battery provided in the embodiments of the present application has the following technical effects: This application collects the energy operating parameters of the liquid flow battery in real time, obtains energy status information and scheduling-related data through dynamic estimation, multi-objective optimization, fuzzy control strategy formulation and other processes, calculates the optimization results and control signals of the target to be scheduled, and combines the incremental learning of reverse tracing and strategy optimization for adjustment, thereby accurately realizing the intelligent energy scheduling of the liquid flow battery, making the energy scheduling results of the liquid flow battery more accurate and reliable, and achieving the technical effects of intelligent energy scheduling of the liquid flow battery, reducing power loss, extending battery life, and improving the matching degree between scheduling and actual needs.

[0125] Example 2, as Figure 2 As shown, based on the same inventive concept as the aforementioned embodiment 1, this embodiment of the present application provides an intelligent energy scheduling system for a flow battery, the system comprising: The energy state information acquisition module 1 is used to collect energy operation parameters of the flow battery in real time, dynamically estimate the electrolyte state according to the energy operation parameters, and obtain multiple energy state information.

[0126] A fuzzy control strategy acquisition module 2 performs scheduling analysis on the flow battery based on the multiple energy state information, sets multiple targets to be scheduled, performs multi-objective optimization based on the multiple targets to be scheduled, and formulates a fuzzy control strategy based on the optimization results.

[0127] The scheduling result acquisition module 3 is used to execute the fuzzy control strategy to dynamically update the multiple targets to be scheduled, generate a control signal set, perform bidirectional collaborative scheduling on the flow battery based on the control signal set, and generate a scheduling result.

[0128] The energy intelligent scheduling execution module 4 is used to perform reverse tracing according to the scheduling results, perform incremental learning on the fuzzy control strategy according to the tracing path, generate a fuzzy control optimization strategy to replace the fuzzy control strategy, update the control signal set, and obtain multi-level control instructions to intelligently schedule the energy of the flow battery.

[0129] Furthermore, the energy status information acquisition module 1 is configured to perform the following steps: The energy operation parameters are analyzed to obtain the electrolyte flow parameter, electrolyte concentration parameter, and electrolyte temperature parameter of the flow battery; the concentration distribution of the flow battery is calculated based on the electrolyte concentration parameters to generate electrolyte concentration distribution data; extreme value correlation analysis is performed based on the electrolyte concentration distribution data to obtain a concentration polarization dynamic correlation relationship; the electrolyte flow parameter is changed and mapped to an equivalent circuit according to the concentration polarization dynamic correlation relationship to obtain a variable resistance parameter; the open circuit voltage value of the flow battery is retrieved based on the variable resistance parameter, and the electrolyte is converted to an equivalent circuit. The liquid concentration distribution data is associated with the open circuit voltage value to construct a concentration-voltage compensation coefficient table; the electrolyte temperature parameter is nonlinearly corrected and fused with the concentration-voltage compensation coefficient table to construct an electrolyte state coupling model; the dynamic decoupling calculation of the electrolyte charge is performed through the electrolyte state coupling model to obtain the electrolyte charge state information; the dynamic decoupling calculation of the electrolyte health is performed through the electrolyte state coupling model to obtain the health state information; the electrolyte charge state information and the health state information are added to the multiple energy state information.

[0130] Furthermore, the fuzzy control strategy acquisition module 2 is used to perform the following steps: A temperature analysis is performed based on the electrolyte state of charge information, and a charge and discharge analysis of the flow battery is performed according to the operating temperature parameters to set a power loss target; an attenuation analysis is performed based on the health status information, and a battery life analysis of the flow battery is performed according to the health attenuation rate to set a battery life target; the power loss target and the battery life target are mapped to a unified decision space, and a plurality of targets to be scheduled are set; an operating range constraint is set based on the electrolyte state of charge information, and a attenuation rate constraint is performed based on the health status information; the plurality of targets to be scheduled are calculated based on the operating range constraint and the attenuation rate constraint to generate target constraint satisfaction, and are sorted in descending order according to the target constraint satisfaction to generate a target dominance ranking; multi-objective optimization is performed on the plurality of targets to be scheduled based on the target dominance ranking, and the fuzzy control strategy is formulated according to the optimization results.

[0131] Furthermore, the fuzzy control strategy acquisition module 2 is used to perform the following steps: Based on the target dominance sorting rule, the multiple targets to be scheduled are prioritized and layered to generate a target dominance relationship map; based on the target dominance relationship map, multi-target conflict analysis is performed to generate a conflict path; based on the conflict path, the multiple targets to be scheduled are optimized and resolved to generate an optimization result; based on the optimization result, weight allocation is performed to obtain multiple priority weight coefficients; the optimization result is analyzed according to the multiple priority weight coefficients to generate a dominance target strength; and the dominance target strength is added to the fuzzy control strategy.

[0132] Furthermore, the scheduling result acquisition module 3 is used to perform the following steps: Execute the fuzzy control strategy, dynamically update the priority weight coefficients of multiple targets to be scheduled according to the parameters of the power grid operating condition change, and generate multiple target weight coefficients; perform power analysis on the charging and discharging of the flow battery based on the multiple target weight coefficients, and generate a real-time charging and discharging power signal; perform flow compensation analysis on the temperature distribution of the flow battery based on the multiple target weight coefficients, and generate an electrolyte flow control signal; perform electric-liquid bidirectional timing collaborative optimization on the flow battery according to the real-time charging and discharging power signal and the electrolyte flow control signal, and obtain timing collaborative optimization parameters; dynamically schedule the flow battery according to the timing collaborative optimization parameters, and generate the scheduling result.

[0133] Furthermore, the scheduling result acquisition module 3 is used to perform the following steps: Based on the energy operating parameters of the liquid flow battery, the environmental operating conditions are recorded to obtain the battery operating condition change parameters; the fuzzy control strategy is executed, and the multiple targets to be scheduled are detected according to the battery operating condition change parameters to generate detection results, and the detection results are power loss mutation results and / or battery life mutation results; when the detection result is the power loss mutation result, the priority weight of the power loss target is increased to the highest level, and the first target weight coefficient is determined; when the detection result is the battery life mutation result, the priority weight of the battery life target is increased to the highest level, and the second target weight coefficient is determined; when the detection result is the power loss mutation result and the battery life mutation result, the scheduling impact analysis is performed on the power loss mutation result and the battery life mutation result, and the third target weight coefficient is determined according to the impact factor.

[0134] Furthermore, the energy intelligent scheduling execution module 4 is used to perform the following steps: A historical scheduling cycle is set according to the scheduling result, and execution result data of the historical scheduling cycle is collected; deviation analysis is performed based on the execution result data in combination with the fuzzy control strategy to generate execution deviation parameters; a reverse tracing path is constructed according to the execution deviation parameters, and deviation positioning is performed according to the reverse tracing path to obtain a tracing defect positioning point; incremental learning of the fuzzy control strategy is performed based on the tracing defect positioning point to generate the fuzzy control optimization strategy.

[0135] Furthermore, the energy intelligent scheduling execution module 4 is used to perform the following steps: A defect analysis is performed based on the traceable defect location point to determine the electric-hydraulic coordinated timing defect point and the life target defect point; a power decline test is performed based on the electric-hydraulic coordinated timing defect point, and a first test result is generated for confidence analysis to obtain a first confidence level; an attenuation increase test is performed based on the life target defect point to generate a second test result for confidence analysis to obtain a second confidence level; and incremental learning is performed on the fuzzy control strategy based on the first test result combined with the first confidence level and the second test result combined with the second confidence level to generate the fuzzy control optimization strategy.

[0136] Furthermore, the energy intelligent scheduling execution module 4 is used to perform the following steps: Verify whether the fuzzy control optimization strategy can eliminate the execution deviation parameters of the fuzzy control strategy, and generate a verification result; when the verification result is that the fuzzy control optimization strategy can eliminate the execution deviation parameters of the fuzzy control strategy, generate a replacement instruction, replace the fuzzy control strategy with the fuzzy control optimization strategy through the replacement instruction, and construct a signal cascade reconstruction space; map the control signal set to the signal cascade reconstruction space for reconstruction compensation, and generate a control reconstruction signal set; perform scheduling analysis based on the control reconstruction signal set, define multiple scheduling levels, match the multiple scheduling levels with the control reconstruction signal set, and generate the multi-level control instruction.

[0137] An intelligent energy scheduling system for a liquid flow battery provided in an embodiment of the present invention can execute an intelligent energy scheduling method for a liquid flow battery provided in any embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution method.

[0138] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other and are not used to limit the scope of protection of the present invention.

[0139] The above specific embodiments do not constitute a limitation to the scope of protection of this application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements and improvements made within the spirit and principles of this application should be included in the scope of protection of this application. In some cases, the actions or steps recorded in this application can be performed in an order different from that in the embodiments and can still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A method for intelligent energy scheduling of a flow battery, characterized in that: The method comprises: Real-time collection of energy operating parameters of the flow battery, dynamic estimation of the electrolyte state based on the energy operating parameters, and acquisition of multiple energy state information; Performing scheduling analysis on the flow battery based on the multiple energy state information, setting multiple targets to be scheduled, performing multi-objective optimization based on the multiple targets to be scheduled, and formulating a fuzzy control strategy based on the optimization results; Executing the fuzzy control strategy to dynamically update the multiple targets to be scheduled, generating a control signal set, performing bidirectional collaborative scheduling on the flow battery based on the control signal set, and generating a scheduling result; According to the scheduling result, reverse tracing is performed, the fuzzy control strategy is incrementally learned according to the tracing path, a fuzzy control optimization strategy is generated to replace the fuzzy control strategy, the control signal set is updated, and multi-level control instructions are obtained to intelligently schedule the energy of the flow battery.

2. The method for intelligent energy scheduling of a flow battery according to claim 1, wherein: Real-time collection of energy operating parameters of a flow battery, dynamic estimation of the electrolyte state based on the energy operating parameters, and acquisition of multiple energy state information, the method includes: Analyzing the energy operation parameters to obtain electrolyte flow parameters, electrolyte concentration parameters, and electrolyte temperature parameters of the flow battery; performing concentration distribution calculation on the flow battery based on the electrolyte concentration parameter to generate electrolyte concentration distribution data, and performing extreme value correlation analysis on the electrolyte concentration distribution data to obtain a concentration polarization dynamic correlation relationship; According to the concentration polarization dynamic correlation relationship, the electrolyte flow parameter is changed and mapped to an equivalent circuit to obtain a variable resistance parameter; Retrieving an open circuit voltage value of the flow battery based on the variable resistance parameter, correlating the electrolyte concentration distribution data with the open circuit voltage value, and constructing a concentration-voltage compensation coefficient table; Performing nonlinear correction fusion on the electrolyte temperature parameter and the concentration-voltage compensation coefficient table to construct an electrolyte state coupling model; Performing dynamic decoupling calculation of electrolyte charge using the electrolyte state coupling model to obtain electrolyte charge state information; Performing dynamic decoupling calculation of electrolyte health using the electrolyte state coupling model to obtain health state information; The electrolyte charge state information and the health state information are added to the plurality of energy state information.

3. The method for intelligent energy scheduling of a flow battery according to claim 2, characterized in that: Performing scheduling analysis on the flow battery based on the multiple energy state information, setting multiple targets to be scheduled, performing multi-objective optimization based on the multiple targets to be scheduled, and formulating a fuzzy control strategy based on the optimization results, the method comprising: Performing temperature analysis based on the electrolyte state of charge information, performing charge and discharge analysis on the flow battery according to operating temperature parameters, and setting power loss targets; Performing attenuation analysis based on the health status information, performing battery life analysis on the flow battery according to the health attenuation rate, and setting a battery life target; Mapping the power loss target and the battery life target to a unified decision space, and setting multiple targets to be scheduled; Setting an operating range constraint based on the electrolyte state of charge information and performing a decay rate constraint based on the health state information; Calculating the multiple targets to be scheduled based on the operating range constraint and the decay rate constraint to generate target constraint satisfaction, sorting the targets in descending order according to the target constraint satisfaction to generate a target dominance ranking; Based on the target dominance ranking, multi-objective optimization is performed on the multiple targets to be scheduled, and the fuzzy control strategy is formulated according to the optimization results.

4. The method for intelligent energy scheduling of a flow battery according to claim 3, wherein: Performing multi-objective optimization on the multiple targets to be scheduled based on the target dominance ranking, and formulating the fuzzy control strategy according to the optimization results, the method comprising: Prioritizing the plurality of targets to be scheduled based on the target dominance sorting rule to generate a target dominance relationship graph; Perform multi-objective conflict analysis based on the objective dominance relationship graph to generate conflict paths, and optimize and resolve the multiple objectives to be scheduled based on the conflict paths to generate optimization results; Performing weight assignment based on the optimization results to obtain a plurality of priority weight coefficients, parsing the optimization results according to the plurality of priority weight coefficients to generate a dominant target strength; The dominance target strength is added to the fuzzy control strategy.

5. The method for intelligent energy scheduling of a flow battery according to claim 4, characterized in that: The method includes executing the fuzzy control strategy to dynamically update the multiple targets to be scheduled, generating a control signal set, performing bidirectional collaborative scheduling on the flow battery based on the control signal set, and generating a scheduling result. Executing the fuzzy control strategy, dynamically updating the priority weight coefficients of multiple targets to be scheduled according to the parameters of the power grid operating condition change, and generating multiple target weight coefficients; Performing power analysis on the charge and discharge of the flow battery based on the multiple target weight coefficients to generate a real-time charge and discharge power signal; performing flow compensation analysis on the temperature distribution of the flow battery based on the multiple target weight coefficients to generate an electrolyte flow control signal; Performing electro-liquid bidirectional timing collaborative optimization on the flow battery according to the real-time charge and discharge power signal and the electrolyte flow control signal to obtain timing collaborative optimization parameters; The flow battery is dynamically scheduled according to the timing collaborative optimization parameters to generate the scheduling result.

6. The method for intelligent energy scheduling of a flow battery according to claim 5, characterized in that: The fuzzy control strategy is executed to dynamically update the priority weight coefficients of multiple targets to be scheduled according to the parameters of the power grid working condition change, and multiple target weight coefficients are generated. The method includes: Recording environmental operating conditions based on the energy operating parameters of the flow battery to obtain battery operating condition change parameters; executing the fuzzy control strategy, detecting the plurality of targets to be scheduled according to the battery operating condition change parameter, and generating a detection result, wherein the detection result is a power loss mutation result and / or a battery life mutation result; When the detection result is the power loss mutation result, the priority weight of the power loss target is increased to the highest level, and a first target weight coefficient is determined; When the detection result is the battery life mutation result, the priority weight of the battery life target is increased to the highest level, and a second target weight coefficient is determined; When the detection results are the power loss mutation result and the battery life mutation result, a scheduling impact analysis is performed on the power loss mutation result and the battery life mutation result, and a third target weight coefficient is determined based on the impact factor.

7. The method for intelligent energy scheduling of a flow battery according to claim 1, wherein: According to the scheduling result, reverse tracing is performed, and the fuzzy control strategy is incrementally learned according to the tracing path to generate a fuzzy control optimization strategy. The method includes: Setting a historical scheduling period according to the scheduling result, and collecting execution result data of the historical scheduling period; Performing deviation analysis based on the execution result data in combination with the fuzzy control strategy to generate execution deviation parameters; Constructing a reverse tracing path based on the execution deviation parameter, locating the deviation according to the reverse tracing path, and obtaining a tracing defect location point; Incremental learning is performed on the fuzzy control strategy based on the traceable defect location point to generate the fuzzy control optimization strategy.

8. The method for intelligent energy scheduling of a flow battery according to claim 7, characterized in that: Incremental learning of the fuzzy control strategy is performed based on the traceable defect location point to generate the fuzzy control optimization strategy, the method comprising: Perform defect analysis based on the traceable defect location points to determine the electrical-hydraulic coordination timing defect points and the life target defect points; Performing a power degradation test based on the electric-hydraulic coordinated timing defect point, generating a first test result, performing a confidence analysis, and obtaining a first confidence level; Performing a decay incremental test based on the lifetime target defect point, generating a second test result for performing confidence analysis, and obtaining a second confidence level; Incremental learning is performed on the fuzzy control strategy according to the first test result combined with the first confidence level, and the second test result combined with the second confidence level to generate the fuzzy control optimization strategy.

9. The method for intelligent energy scheduling of a flow battery according to claim 7, wherein: Generating a fuzzy control optimization strategy to replace the fuzzy control strategy, updating the control signal set, and obtaining multi-level control instructions to intelligently dispatch the energy of the flow battery, the method includes: Verifying whether the fuzzy control optimization strategy can eliminate the execution deviation parameter of the fuzzy control strategy, and generating a verification result; When the verification result shows that the fuzzy control optimization strategy can eliminate the execution deviation parameter of the fuzzy control strategy, a replacement instruction is generated, and the fuzzy control strategy is replaced by the fuzzy control optimization strategy through the replacement instruction to construct a signal cascade reconstruction space; Mapping the control signal set to the signal cascade reconstruction space for reconstruction compensation to generate a control reconstruction signal set; A scheduling analysis is performed based on the control and reconstruction signal set, a plurality of scheduling levels are defined, the plurality of scheduling levels are matched with the control and reconstruction signal set, and the multi-level control instructions are generated.

10. An intelligent energy dispatching system for a flow battery, characterized in that: A method for intelligent energy scheduling of a flow battery according to any one of claims 1 to 9, the system comprising: An energy state information acquisition module is used to collect energy operation parameters of the flow battery in real time, dynamically estimate the electrolyte state according to the energy operation parameters, and obtain multiple energy state information; a fuzzy control strategy acquisition module, which performs scheduling analysis on the flow battery based on the multiple energy state information, sets multiple targets to be scheduled, performs multi-objective optimization based on the multiple targets to be scheduled, and formulates a fuzzy control strategy based on the optimization results; a scheduling result acquisition module, configured to execute the fuzzy control strategy to dynamically update the multiple targets to be scheduled, generate a control signal set, perform bidirectional collaborative scheduling on the flow battery based on the control signal set, and generate a scheduling result; The energy intelligent scheduling execution module is used to perform reverse tracing according to the scheduling results, perform incremental learning on the fuzzy control strategy according to the tracing path, generate a fuzzy control optimization strategy to replace the fuzzy control strategy, update the control signal set, and obtain multi-level control instructions to intelligently schedule the energy of the flow battery.

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