All-vanadium redox flow battery capacity recovery method

Abstract

The invention relates to the technical field of energy storage, and discloses an all-vanadium redox flow battery capacity recovery method, which comprises the following steps: S1, establishing an electrochemical kinetic model, and describing a relationship among a divalent vanadium ion oxidation reaction rate, a battery voltage and an electrolyte concentration; s2, carrying out an experiment, carrying out a charge and discharge test under different concentrations, and recording the voltage, current and capacity change of the battery to obtain an optimal operation condition; s3, introducing a reducing agent, adjusting the concentration of the reducing agent and battery operation parameters, and reducing the oxidation rate of V < 2 + >; and S4, dynamically adjusting the concentration of the reducing agent, the charging current of the battery and the flow velocity of the electrolyte by using an optimal control algorithm, and enabling the capacity recovery of the battery to be maximized. According to the technical scheme, the oxidizing reaction rate is effectively controlled by introducing the reducing agent, the effect of reducing the oxidizing reaction rate of the divalent vanadium ions is achieved, meanwhile, possible side effects are avoided by optimizing the concentration and the type of the reducing agent, and recovery of the battery capacity is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage, and specifically to a method for restoring the capacity of a vanadium redox flow battery. Background Art

[0002] With the increasing global demand for energy storage technologies, flow batteries have been widely used in large-scale energy storage systems due to their excellent cycle stability and long service life. Among them, vanadium redox flow batteries have gradually become an important choice in the energy storage field due to their high energy density, long service life, and adjustable battery capacity. However, despite their excellent performance, the battery still exhibits capacity decay during long-term operation, especially during charge-discharge cycles. This problem is closely related to the redox reactions inside the battery, particularly the oxidation reaction rate of divalent vanadium ions to trivalent vanadium ions being too fast, resulting in ineffective restoration of the battery capacity.

[0003] Although existing technologies attempt to restore capacity by adjusting the operating conditions of the battery, these methods often face a series of limitations, especially when reaction control is imprecise. Specifically, it is difficult to precisely control the oxidation reaction rate inside the battery, and existing technologies have not fully utilized reducing agents to adjust the oxidation reaction rate, resulting in unstable or insufficient battery capacity restoration. In addition, excessive reducing agents may cause side reactions, affecting the overall efficiency of the battery. Therefore, how to precisely adjust the concentration of reducing agents without triggering side reactions and optimize the battery operating conditions to maximize the battery capacity restoration effect has become a major challenge in current technologies. Summary of the Invention

[0004] Aiming at the deficiencies of the existing technology, the present invention provides a method for restoring the capacity of a vanadium redox flow battery, which solves the problems of too fast oxidation reaction of divalent vanadium ions, large side reactions, and low restoration efficiency during the capacity restoration process of the vanadium redox flow battery.

[0005] To achieve the above objectives, the present invention is realized through the following technical solutions: A method for restoring the capacity of a vanadium redox flow battery, comprising the following steps:

[0006] S1. Establish an electrochemical kinetics model to describe the relationship between the oxidation reaction rate of divalent vanadium ions and the battery voltage and electrolyte concentration, and the model is based on the Butler-Volmer equation;

[0007] S2. Conduct experiments, perform charge-discharge tests at different concentrations, and record the voltage, current, and capacity changes of the battery to obtain the optimal operating conditions;

[0008] S3. Introduce a reducing agent, adjust the concentration of the reducing agent and the battery operating parameters to reduce the oxidation rate of V 2+ ;

[0009] S4. Use the optimization control algorithm to dynamically adjust the reductant concentration, battery charging current, and electrolyte flow rate to maximize the capacity recovery of the battery.

[0010] Preferably, in the step S1, the expression of the Butler-Volmer equation is:

[0011]

[0012] where r ox is the oxidation reaction rate; k ox is the reaction rate constant; is the concentration of trivalent vanadium ions; α and β are electrochemical parameters; η is the electrochemical overpotential.

[0013] Preferably, in the step S3, the reductant includes but is not limited to polyols, sulfites, or selenites, and the concentration of the reductant is 0.1M to 0.5M.

[0014] Preferably, in the step S4, the optimization control algorithm adopts the particle swarm optimization algorithm and the genetic algorithm. The goal of the algorithm is to maximize the battery capacity recovery rate, and the function established for the goal is the ratio of the restored battery capacity to the initial capacity.

[0015] Preferably, in the step S3, the reductant concentration is adjusted in real time through a feedback control system. The feedback control system adjusts the charging current, electrolyte flow rate, and reductant concentration according to the difference between the target capacity and the actual capacity by monitoring the V 2+ concentration, battery current, and battery voltage in the battery electrolyte.

[0016] Preferably, in the step S1, the electrochemical kinetic model further includes a diffusion-reaction coupling model, which is used to calculate the diffusion effect and reaction rate of divalent vanadium ions and the reductant in the electrolyte.

[0017] Preferably, the diffusion-reaction coupling model is the following mathematical expression:

[0018]

[0019] where is the concentration of divalent vanadium ions; is the diffusion coefficient of divalent vanadium ions; is the spatial diffusion term of the concentration; r ox is the oxidation reaction rate; r Red is the reduction reaction rate.

[0020] Preferably, in the step S2, the experiment includes the following steps:

[0021] At the beginning of the experiment, the same type of all-vanadium redox flow battery with a known initial capacity is selected, and preliminary operating conditions are set, such as the initial electrolyte concentration, charging current, and electrolyte flow rate;

[0022] Charge-discharge tests are carried out at different reductant concentrations. By recording the voltage, current, and capacity changes of the battery, the influence of different concentrations on the capacity recovery effect is evaluated;

[0023] After each charge-discharge cycle, the degree of capacity decay and the capacity recovery of the battery are recorded, and the influence of operating conditions on the recovery efficiency is analyzed;

[0024] According to the experimental data, operating parameters such as reductant concentration, battery current, and electrolyte flow rate are adjusted, and the charge-discharge test is repeated until the capacity recovery reaches the maximum value;

[0025] At the end of the experiment, the final capacity and the initial capacity recorded during the experiment are evaluated and compared to determine the optimal operating conditions and reductant concentration.

[0026] The present invention provides a method for recovering the capacity of an all-vanadium redox flow battery. It has the following beneficial effects:

[0027] 1. The technical solution of the present invention to effectively control the oxidation reaction rate by introducing a reductant achieves the effect of reducing the oxidation reaction rate of divalent vanadium ions. Although the introduction of a reductant may cause side reactions or instability, the present invention has successfully avoided these negative impacts by optimizing the reductant concentration and type, realizing the recovery of the battery capacity. Compared with the prior art solutions that do not fully consider the influence of the reductant, the present invention avoids the possible side effects during the capacity recovery process by reasonably selecting and controlling the reductant.

[0028] 2. The present invention effectively controls the reaction rate and further improves the battery capacity recovery effect through the combined scheme of precisely adjusting the reductant concentration and battery operating conditions. In some embodiments, an excessive amount of reductant may cause incomplete reactions or side reactions, thereby affecting the battery performance. Therefore, the present invention uses a feedback control system to monitor and adjust the reductant concentration in real time, ensuring capacity recovery while avoiding potential negative impacts and solving the problems caused by excessive reductants in the prior art.

[0029] 3. The present invention introduces reductants such as polyols and sulfites, effectively optimizing the control of the oxidation reaction. Considering that an excessive amount of reductant may affect the long-term stability of the battery, the present invention verifies the optimal concentration range of the reductant through experiments, ensuring that there will be no problem of excessive reductant concentration interfering with other metal ions or reducing the overall efficiency of the battery during the capacity recovery process of the battery, thereby ensuring the balance and stability of the reduction reaction. Description of the Drawings

[0030] Figure 1 This is the flowchart of the method steps of the present invention. Specific embodiments

[0031] Next, in combination with the accompanying drawings of the present invention specification, the technical solutions in the embodiments of the present invention will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0032] Please refer to the attached Figure 1 , the embodiment of the present invention provides a method for restoring the capacity of a vanadium redox flow battery, including the following steps:

[0033] S1. Establish an electrochemical kinetic model to describe the relationship between the oxidation reaction rate of divalent vanadium ions and the battery voltage and electrolyte concentration. The model is based on the Butler-Volmer equation;

[0034] S2. Conduct experiments, perform charge and discharge tests at different concentrations, and record the voltage, current, and capacity changes of the battery to obtain the optimal operating conditions;

[0035] S3. Introduce a reducing agent, adjust the concentration of the reducing agent and the battery operating parameters, and reduce the oxidation rate of V 2+ ;

[0036] S4. Use an optimal control algorithm to dynamically adjust the concentration of the reducing agent, the battery charging current, and the electrolyte flow rate to maximize the capacity recovery of the battery.

[0037] In step S1, the expression of the Butler-Volmer equation is:

[0038]

[0039] Among them, r ox is the oxidation reaction rate; k ox is the reaction rate constant; is the concentration of trivalent vanadium ions; α and β are electrochemical parameters; η is the electrochemical overpotential;

[0040] In step S1, the electrochemical kinetic model further includes a diffusion-reaction coupling model, which is used to calculate the diffusion effect and reaction rate of divalent vanadium ions and the reducing agent in the electrolyte;

[0041] The diffusion-reaction coupling model is the following mathematical expression:

[0042]

[0043] Among them, is the concentration of divalent vanadium ions; is the diffusion coefficient of divalent vanadium ions; is the spatial diffusion term of the concentration; r ox is the oxidation reaction rate; r Red is the reduction reaction rate.

[0044] Specifically, in this embodiment, first, an electrochemical kinetics model needs to be established to describe the relationship between the oxidation reaction rate of divalent vanadium ions (V 2 + ) and the battery voltage and the electrolyte concentration. This process is the basis of the battery recovery method and can provide theoretical support and optimization guidance for subsequent steps.

[0045] Generally, the battery capacity recovery effect is closely related to the battery voltage, the electrolyte concentration, and the oxidation reaction rate. The oxidation reaction rate directly affects the rate of conversion of divalent vanadium ions to trivalent vanadium ions, and this conversion process plays an important role in battery capacity decay. Therefore, in this embodiment, the Butler-Volmer equation is selected to establish this electrochemical kinetics model.

[0046] As an option, the Butler-Volmer equation can accurately describe the non-linear relationship between the redox reaction rate and the voltage and the electrolyte concentration. Specifically, in the implementation manner of the present invention, the expression of the oxidation reaction rate is:

[0047]

[0048] Among them, r ox represents the rate of the oxidation reaction; k ox is the reaction rate constant; represents the concentration of trivalent vanadium ions; α and β are electrochemical parameters; η is the electrochemical overpotential. This equation takes into account the reaction rate of the battery at different electrolyte concentrations and is also closely related to the change of the battery voltage.

[0049] In an embodiment of the present invention, the relationship between the electrolyte concentration and the oxidation reaction rate is not only affected by the battery voltage but also by the diffusion properties of the electrolyte in the battery. This is crucial for the capacity recovery process. Specifically, the diffusion behavior of divalent vanadium ions V 2+ in the electrolyte will affect the rate of its reaction with the reducing agent, and the diffusion effect needs to be considered in this model.

[0050] In a possible implementation, considering the coupling effect of diffusion and reaction, the electrochemical kinetics model of this embodiment further includes a diffusion-reaction coupling model. This model is used to calculate the diffusion effect of divalent vanadium ions and reducing agents in the electrolyte, and combine the reaction rate to provide a more accurate prediction of battery capacity recovery. The mathematical expression of this diffusion-reaction coupling model is:

[0051]

[0052] Where, is the concentration of divalent vanadium ions; is the diffusion coefficient of divalent vanadium ions; is the spatial diffusion term of the concentration; r ox is the oxidation reaction rate; r Red is the reduction reaction rate; t is the time point; represents the change in the concentration of divalent vanadium ions over time. Through this diffusion-reaction coupling model, the electrolyte flow and diffusion effect during the reaction can be more accurately described, thereby optimizing the battery capacity recovery process.

[0053] In addition, the parameters in the model, such as reaction rate constants and electrochemical parameters, can be adjusted and optimized according to experimental data. For example, in the experiment, electrolytes with different concentrations are used, and the reaction rates of the battery at different voltages are measured to optimize these parameters, thereby improving the accuracy of the model.

[0054] Specifically, in this case, by precisely adjusting the reaction rate constant and electrochemical overpotential, the reaction rate can be effectively controlled, and the electrolyte concentration and voltage of the battery can be adjusted according to the actual working state of the battery, thereby further improving the battery capacity recovery performance.

[0055] In step S2, the experiment includes the following steps:

[0056] At the beginning of the experiment, select the same type of all-vanadium redox flow battery with a known initial capacity, and set preliminary operating conditions, such as initial electrolyte concentration, charging current, and electrolyte flow rate;

[0057] Conduct charge-discharge tests at different reducing agent concentrations, and evaluate the influence of different concentrations on the capacity recovery effect by recording the voltage, current, and capacity changes of the battery;

[0058] After each charge-discharge cycle, record the capacity attenuation degree and capacity recovery situation of the battery, and analyze the influence of operating conditions on the recovery efficiency;

[0059] According to the experimental data, adjust the operating parameters such as reducing agent concentration, battery current, and electrolyte flow rate, and repeat the charge-discharge test until the capacity recovery reaches the maximum value;

[0060] At the end of the experiment, the final capacity and the initial capacity recorded during the experiment are evaluated and compared to determine the optimal operating conditions and the reducing agent concentration.

[0061] Specifically, in this embodiment, step S2 is mainly used to verify the effectiveness of the aforementioned electrochemical model and further optimize the operating conditions. According to the calculation results of the aforementioned electrochemical kinetics model, the experimental steps can help evaluate the influence of different reducing agent concentrations on the capacity recovery effect. Through the feedback of experimental data, various parameters in the model can be adjusted to achieve the optimization of capacity recovery.

[0062] Generally, the core purpose of the experiment is to explore the influence of these factors on the battery capacity recovery by adjusting parameters such as the reducing agent concentration, battery current, and electrolyte flow rate. Before the experiment starts, a vanadium redox flow battery with a known initial capacity needs to be selected, and preliminary operating conditions are set, including the initial electrolyte concentration, charging current, and electrolyte flow rate, etc. By conducting charge-discharge tests at different concentrations, key parameters such as the battery voltage, current, and capacity change are systematically recorded.

[0063] As an option, a certain number of charge-discharge cycles are adopted in the experiment. During each cycle, the voltage, current, and capacity of the battery will change. By monitoring these changes, the performance data of the battery under different operating conditions can be obtained. These data are guiding for the adjustment of the reducing agent concentration and battery operating parameters in the subsequent step S3.

[0064] Specifically, during the experiment, after each charge-discharge cycle, the degree of battery capacity decay and the situation of capacity recovery are recorded. Through these records, the influence of operating conditions on the battery capacity recovery efficiency can be analyzed. If the battery capacity recovery effect is good under a certain operating condition, the feasibility of this operating condition can be further confirmed. Through the comparison of experimental results, the optimal operating conditions are finally determined.

[0065] In a possible implementation, the operating conditions will be gradually adjusted according to the recorded data during the experiment until the battery capacity recovery effect reaches the best. For example, if the capacity recovery effect is not obvious under the initial operating conditions, the reaction rate during the charge-discharge process can be optimized by adjusting the reducing agent concentration, increasing or decreasing the battery charging current or electrolyte flow rate, so as to further improve the efficiency of battery capacity recovery.

[0066] In some embodiments, the analysis of experimental data is not limited to the evaluation of the battery capacity recovery, but can also comprehensively analyze the service life, cycle stability, etc. of the battery. These analysis results provide an experimental basis for the further operations in step S3. Through the experimental data, the accuracy of the model prediction can be effectively verified, and it helps to optimize the selection of the reducing agent concentration and the battery charge-discharge parameters.

[0067] After the experiment, the final capacity recovery will be compared with the initial capacity. Through this comparison, the influence of the operating conditions on the battery recovery effect can be quantified, providing strong support for determining the optimal operating conditions. In some embodiments, the experimental data is used to adjust the parameters in the electrochemical model to further improve the accuracy of the model and the operability of the operation.

[0068] In step S3, the reducing agent includes but is not limited to polyols, sulfites or selenites, and the concentration of the reducing agent is 0.1M to 0.5M;

[0069] In step S3, the concentration of the reducing agent is adjusted in real time by a feedback control system, and the feedback control system adjusts the charging current, the electrolyte flow rate and the reducing agent concentration according to the difference between the target capacity and the actual capacity by monitoring the V 2+ concentration, the battery current and the battery voltage in the battery electrolyte.

[0070] Specifically, in this embodiment, step S3 mainly adjusts the oxidation reaction rate during the battery capacity recovery on the basis of the foregoing experimental step S2. By introducing an appropriate amount of reducing agent and precisely controlling its concentration and other operating parameters, the oxidation rate of divalent vanadium ions (V 2+ ) can be effectively reduced, thereby promoting the recovery of the battery capacity.

[0071] Generally, the oxidation reaction rate is a key factor affecting the capacity recovery efficiency of the all-vanadium redox flow battery. Too high an oxidation rate will cause the reaction of V 2 to V 3+ to intensify, thereby causing capacity attenuation. Therefore, reducing the oxidation rate of V 2+ is crucial for recovering the battery capacity. To this end, by introducing a reducing agent and adjusting its concentration, the occurrence of the oxidation reaction can be effectively inhibited, thereby improving the capacity recovery effect.

[0072] As an option, the selection of the reducing agent is crucial. According to the foregoing electrochemical kinetic model, certain reducing agents can effectively reduce V 3+ to V 2+, thereby slowing down the oxidation reaction. In this embodiment, the reducing agents used include polyols, sulfites, selenites, and combinations thereof, and the concentration range of these reducing agents is between 0.1 M and 0.5 M. Specifically, by adjusting the concentration of the reducing agent, its reaction rate can be regulated to ensure an appropriate reaction balance during the battery capacity recovery process.

[0073] Specifically, as the concentration of the reducing agent increases, the control effect on the reaction rate becomes more obvious. However, too high a concentration of the reducing agent may lead to side reactions, affecting the overall performance of the battery. Therefore, a reasonable selection and precise control of the reducing agent concentration are crucial for optimizing capacity recovery.

[0074] In a possible implementation, the concentration of the reducing agent and the operating parameters of the battery (such as current, voltage, flow rate) are adjusted in real time through a feedback control system. This control system can adjust the concentration of the reducing agent and other parameters of the battery according to the real-time state of the battery, thereby maximizing the battery capacity recovery efficiency. The feedback control system adjusts the operating conditions in real time by monitoring key parameters such as the V 2+ concentration in the battery electrolyte, battery current, and battery voltage to ensure that the battery is restored in the best state.

[0075] In some embodiments, the feedback control system optimizes capacity recovery by precisely regulating the battery charging current, electrolyte flow rate, and reducing agent concentration. Adjusting the battery charging current and electrolyte flow rate not only affects the rate of the internal reaction of the battery but also affects the temperature and stability of the battery. Too high or too low a charging current may lead to uneven reactions and affect the effect of capacity recovery. Therefore, these parameters need to be precisely adjusted according to experimental data.

[0076] For example, during the experiment, by gradually increasing the concentration of the reducing agent and adjusting the charging current, the change in battery capacity recovery can be observed. Based on the battery voltage, current, and capacity data recorded in the experiment, the selection of the reducing agent concentration and other operating parameters can be further optimized to ensure that the battery always operates in the best state throughout the recovery process.

[0077] In this embodiment, the real-time adjustment process of the feedback control system is not limited to adjusting the concentration of the reducing agent but also includes the dynamic adjustment of the current and electrolyte flow rate during the battery charge and discharge process. This comprehensive adjustment method can effectively improve the battery capacity recovery rate and ensure the stability and efficiency of the recovery process.

[0078] In step S4, the optimization control algorithm adopts the particle swarm optimization algorithm and the genetic algorithm. The goal of the algorithm is to maximize the battery capacity recovery rate, and the function established for the goal is the ratio of the restored battery capacity to the initial capacity.

[0079] Specifically, in this embodiment, step S4 involves the application of an optimization control algorithm. By dynamically adjusting the reductant concentration, battery charging current, and electrolyte flow rate, the maximization of the full vanadium flow battery capacity recovery is achieved. This step is closely connected to the aforementioned step S3. By adjusting the operating conditions in real time, it ensures that the battery recovers its capacity in the best state. Through intelligent algorithm optimization control, the system can accurately predict and adjust the operating parameters to improve the efficiency of battery capacity recovery.

[0080] Generally, optimization control algorithms can be used to solve complex optimization problems. During the battery capacity recovery process, the changes in operating parameters have complex interrelationships. Traditional manual adjustment methods may lead to unstable recovery effects. To overcome this challenge, this embodiment introduces the Particle Swarm Optimization (PSO) algorithm and the Genetic Algorithm (GA). These algorithms can optimize the operating parameters through efficient global search, avoid falling into local optimal solutions, and thus achieve the best effect of battery capacity recovery.

[0081] As an option, the Particle Swarm Optimization (PSO) algorithm and the Genetic Algorithm (GA) have unique advantages in dealing with multi-variable optimization problems. The Particle Swarm Optimization can efficiently search for the optimal solution by simulating the collaborative search among groups. The Genetic Algorithm can gradually iterate and optimize the solution by simulating the process of natural selection. Specifically, in this embodiment, the objective function is the ratio of the recovered battery capacity to the initial capacity, expressed as:

[0082]

[0083] where C final is the final capacity of the battery after recovery; C initial is the initial capacity of the battery. This objective function can clearly reflect the effect of battery capacity recovery and provide a clear optimization direction for the optimization algorithm.

[0084] In a possible implementation, the goal of the optimization control algorithm is not only to maximize the battery capacity recovery rate but also to consider the stability and long-term life of battery operation. For example, the battery recovers faster at high charging currents, but it may have a negative impact on the battery life. Therefore, the optimization algorithm needs to balance the relationship between the recovery rate and the long-term benefits.

[0085] Specifically, the application of the algorithm is not limited to the adjustment of the reductant concentration but also includes the adjustment of the battery charging current and the electrolyte flow rate. By accurately calculating the optimized values of each parameter, the system can dynamically adjust the battery charging strategy and real-time control the battery operating conditions. For example, at the initial stage of battery capacity recovery, a higher charging current may be required to accelerate the recovery. During the recovery process, when the capacity approaches the target value, the charging current may need to be gradually reduced to reduce the risk of overcharging.

[0086] As an extension, the feedback control system will perform real-time monitoring based on the optimization control algorithm and adjust the operating parameters according to the real-time performance of the battery. This closed-loop control method ensures the real-time and accuracy of the operation. Each adjustment will be based on the data results after the previous round of operation to ensure the continuous optimization of the capacity recovery process.

[0087] In some embodiments, the optimization control algorithm can gradually find the best operating conditions through an iterative approach. As the algorithm progresses, parameters such as the charging current, reducing agent concentration, and flow rate will be continuously adjusted until the optimal state of capacity recovery is reached. This process can not only improve the capacity recovery effect but also reduce the battery loss through multiple adjustments, thereby extending the service life of the battery.

[0088] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for recovering the capacity of an all-vanadium liquid flow battery, characterized in that: The following steps are involved: S1. Establish an electrochemical kinetic model to describe the relationship between the oxidation reaction rate of divalent vanadium ions and the battery voltage and electrolyte concentration, wherein the model is based on the Butler-Volmer equation; S2. Conduct experiments, perform charge and discharge tests at different concentrations, and record changes in battery voltage, current, and capacity to obtain optimal operating conditions; S3, introduce reducing agent, adjust reducing agent concentration and battery operating parameters, reduce V 2+ The oxidation rate of S4. Use the optimization control algorithm to dynamically adjust the reducing agent concentration, battery charging current and electrolyte flow rate to maximize the battery capacity recovery.

2. A method for recovering the capacity of an all-vanadium redox flow battery according to claim 1, characterized in that: In step S1, the Butler-Volmer equation is expressed as: Among them, r ox is the oxidation reaction rate; k ox is the reaction rate constant; is the concentration of trivalent vanadium ions; α and β are electrochemical parameters; η is the electrochemical overpotential.

3. The method for recovering the capacity of an all-vanadium liquid flow battery according to claim 1, characterized in that: In step S3, the reducing agent includes but is not limited to polyols, sulfites or selenites, and the concentration of the reducing agent is 0.1M to 0.5M.

4. The method for recovering the capacity of an all-vanadium liquid flow battery according to claim 1, characterized in that: In step S4, the optimization control algorithm adopts a particle swarm optimization algorithm and a genetic algorithm. The goal of the algorithm is to maximize the battery capacity recovery rate. The function established by the goal is the ratio of the recovered battery capacity to the initial capacity.

5. The method for recovering the capacity of an all-vanadium redox flow battery according to claim 1, characterized in that: In step S3, the concentration of the reducing agent is adjusted in real time by a feedback control system, which monitors the V 2+ concentration, battery current and battery voltage, and adjusts the charging current, electrolyte flow rate and reducing agent concentration according to the difference between the target capacity and the actual capacity.

6. The method for recovering the capacity of an all-vanadium redox flow battery according to claim 1, characterized in that: In step S1, the electrochemical kinetic model further includes a diffusion-reaction coupling model, which is used to calculate the diffusion effect and reaction rate of divalent vanadium ions and reducing agents in the electrolyte.

7. The method for recovering the capacity of an all-vanadium redox flow battery according to claim 6, characterized in that: The diffusion-reaction coupling model is expressed as follows: in, is the concentration of divalent vanadium ions; is the diffusion coefficient of divalent vanadium ions; is the spatial diffusion term of concentration; r ox is the oxidation reaction rate; r Red is the reduction reaction rate.

8. The method for recovering the capacity of an all-vanadium redox flow battery according to claim 1, characterized in that: In step S2, the experiment includes the following steps: At the beginning of the experiment, the same all-vanadium flow battery with a known initial capacity was selected, and preliminary operating conditions such as initial electrolyte concentration, charging current, and electrolyte flow rate were set; Carry out charge and discharge tests at different reducing agent concentrations, and evaluate the effect of different concentrations on capacity recovery by recording the changes in battery voltage, current and capacity; After each charge and discharge cycle, record the battery capacity attenuation and capacity recovery, and analyze the impact of operating conditions on recovery efficiency; According to the experimental data, the operating parameters such as the reducing agent concentration, battery current and electrolyte flow rate were adjusted, and the charge and discharge tests were repeated until the capacity recovery reached the maximum value; At the end of the experiment, the final and initial capacities recorded during the experiment were evaluated and compared to determine the optimal operating conditions and reducing agent concentration.

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