A method for detecting the state of health of a super capacitor of an energy storage power station

Abstract

This application discloses a method for detecting the health status of supercapacitors in energy storage power stations, relating to the field of supercapacitor health status detection. The method includes establishing a supercapacitor switching system model, constructing a dual regressor parameter identification structure adapted to the switching system model, designing a cooperative switching recursive least squares algorithm based on restraint control, executing a cold start judgment and cascaded update mechanism, and reversing the supercapacitor physical parameters (equivalent capacitance, internal resistance, and equalization internal resistance) based on the identification coefficient vector. The health status of the supercapacitor is then quantitatively evaluated based on these physical parameters. By establishing a switching system model and a dual regressor identification structure, and employing a cooperative switching recursive least squares algorithm based on restraint control, this method solves the problems of insufficient transferability and generalization of data-driven methods, model mismatch in model parameter identification methods, and the inability of traditional parameter identification algorithms to adapt to switching systems in existing technologies. This achieves accurate, stable, and online estimation of the supercapacitor's health status.

Description

Technical Field

[0001] This application relates to the field of capacitor health status detection technology, and in particular to a method for detecting the health status of supercapacitors in energy storage power stations. Background Technology

[0002] Supercapacitors have become a key power source in industrial applications such as grid energy storage. Compared to lithium-ion batteries, they offer advantages such as high power density, high efficiency, and long lifespan, and can quickly respond to grid frequency regulation demands. As a fast-response unit in hybrid energy storage systems, they complement energy-type energy storage technologies. To ensure the safe and stable operation of energy storage systems and improve the overall safety and economy of supercapacitor systems, effective monitoring of the supercapacitor system's State of Health (SOH) is necessary.

[0003] However, existing technologies have the following drawbacks: 1) Data-driven methods have poor portability and generalization. Limited by data scale and quality, this method is mainly applicable to specific supercapacitor systems and is constrained by operating conditions.

[0004] 2) The model parameter identification method does not consider the impact of the balancing circuit operation, resulting in model mismatch. Supercapacitor modules for energy storage are typically connected in series to meet voltage requirements, making battery inconsistency unavoidable. To overcome this inconsistency, individual supercapacitor cells need to be connected in parallel with a balancing circuit, causing the original supercapacitor system model to transform from a static model to a dynamically switching model. Parameter identification must simultaneously track both numerical changes and model structural changes.

[0005] 3) Traditional parameter identification algorithms cannot adapt to structural changes in switching systems and cannot achieve synchronization of identified parameters across different subsystems. Equalization switching operations only affect the external current, while internal characteristics remain unchanged. Therefore, the evolution of physical parameters is slower than the dynamic process of switching. Their variation is negligible between two switching operations, exhibiting quasi-invariance. This characteristic generates numerical inertia, suggesting an inherent correlation between the parameters of the two subsystems. Summary of the Invention

[0006] The purpose of this application is to provide a method for detecting the health status of supercapacitors in energy storage power stations, thereby solving the aforementioned problems in the existing technology.

[0007] To achieve the above objectives, this application provides a method for detecting the health status of supercapacitors in an energy storage power station, comprising the following steps: S1: Construct a supercapacitor switching system model. Divide the switching system into a first subsystem and a second subsystem according to whether the equalization circuit is activated. The first subsystem corresponds to the equalization inactive mode, and the second subsystem corresponds to the equalization activated mode. Construct the state space expressions for the first subsystem and the second subsystem respectively. S2: Construct a dual regressor parameter identification structure adapted to the supercapacitor switching system model. Through discretization, make the first subsystem and the second subsystem each correspond to an independent regressor and coefficient vector, and the first subsystem and the second subsystem share the regression vector. S3: Based on the dual regressor parameter identification structure, a cooperative switching recursive least squares algorithm based on restraint control is adopted to achieve cooperative identification between the parameters of the first subsystem and the second subsystem. S4: Set up a cold start handling mechanism. Before the first subsystem completes its initial effective update, suspend the coordination between subsystems and allow each subsystem to update independently. After the first subsystem obtains a priori confidence update, the cascaded coordination control is restored. S5: Based on the coefficient vectors of the first and second subsystems obtained from the identification, solve the equivalent capacitance, equivalent internal resistance, and equalization internal resistance of the supercapacitor. S6: Based on the identified equivalent capacitance, quantitatively assess the health status of the supercapacitor.

[0008] Preferably, the supercapacitor switching system model is represented as follows: ; The coefficients of the state-space equations for the first subsystem are expressed as follows: ; The coefficients of the state-space equations for the second subsystem are expressed as follows: ; in, C This is the equivalent capacitance of a supercapacitor. r This is the equivalent internal resistance of the supercapacitor. R To passively balance internal resistance, s For equalization switch switching signal, This is the voltage across the equivalent capacitance of the supercapacitor. i This represents the total input current.

[0009] Preferably, the discretized system dynamic equations are expressed as follows: ; in, n For time variables, t For unit sampling time, express n Estimate the terminal voltage at all times. for n Input current at any given time for n The supercapacitor terminal voltage at time -1 σ = s +1, σ ∈{1,2}.

[0010] Preferably, the coefficient vector is represented as follows: ; The regression vector is represented as: ; in, For the first σ The first element of the subsystem coefficient vector.

[0011] Preferably, the total loss function for supercapacitor parameter identification is expressed as: ; in, λ Forgetting factor, satisfying λ ∈(0,1), For containing N Switching sequence of individual samples This is the total loss function of the least squares algorithm.

[0012] Preferably, the total loss function of the cooperative switching recursive least squares algorithm based on restraint control is expressed as: ; Collaborative update loss function Represented as: ; in, The least squares fitting loss function is used. ρ To switch control coefficients, For virtual observation samples, This is a virtual regression vector.

[0013] Preferably, the cold start processing mechanism in S4 uses a constraint control coefficient. α Implementation, specifically including: Independent update phase: Forced setting before the first subsystem completes its initial effective update. α =0, so that the first subsystem and the second subsystem can independently perform recursive least squares updates; Collaborative update phase: After the first subsystem obtains the prior confidence update, the restraint control coefficients are restored. α Configure and enter the cascaded restraint control structure.

[0014] Preferred, restraint control coefficient α Set as: ; in, For the first n Subsystems.

[0015] Preferably, the equivalent capacitance of the inverse supercapacitor is expressed as: ; The equivalent internal resistance of the inverse solution is expressed as: ; The equilibrium internal resistance of the inverse solution is expressed as: ; in, This is the first element of the coefficient vector of the second subsystem.

[0016] Preferably, the health status of a supercapacitor is expressed as follows: ; in, This is the nominal capacitance value.

[0017] Therefore, the above-mentioned method for detecting the health status of supercapacitors in energy storage power stations has the following beneficial effects: (1) This application overcomes the model structure mismatch problem. By establishing a switching model, the voltage-current relationship can be accurately described under both the active and inactive conditions of the equalization circuit, which solves the problem of the output error of the traditional single model increasing sharply when equalization is connected, and provides a correct model basis for subsequent parameter identification.

[0018] (2) This application avoids parameter divergence caused by data aliasing. The two regressors maintain coefficient vectors independently and only activate the regressor corresponding to the current subsystem, which effectively avoids parameter cross-contamination caused by mixing data from different model structures into the same regression model.

[0019] (3) This application demonstrates strong robustness to constant current weak excitation conditions and randomly switched signals. The restraint control provides a virtual continuous excitation for the subsystem, and can identify the equivalent internal resistance parameter even in sections where the actual current is constant. The restraint update also makes the parameter estimation smoother and without drastic jumps.

[0020] (4) The cold start mechanism of this application ensures initial stability. If the second subsystem corresponding to the balanced activation occurs before the first subsystem that is not activated, the algorithm temporarily suspends the restraint and activates it after the first subsystem converges, thus ensuring the correctness of the restraint control and parameter coordination.

[0021] (5) This application has high computational efficiency and is suitable for embedded implementation. The recursive update method does not require storing a large amount of historical data, and each update only requires the current sample value, which meets the needs of online applications.

[0022] The technical solution of this application will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0023] Figure 1 This is a flowchart illustrating a method for detecting the health status of a supercapacitor in an energy storage power station according to this application. Figure 2 This is a schematic diagram of the switching model of the supercapacitor equalization system in the embodiments of this application; Figure 3 This is a cascaded restraint control topology diagram in the embodiments of this application; Figure 4 This is a diagram illustrating the test conditions in the embodiments of this application; Figure 5 This is a diagram showing the equivalent capacitance identification results in the embodiments of this application; Figure 6 This is a diagram showing the equivalent internal resistance identification results in the embodiments of this application; Figure 7 This is a diagram showing the results of equalized internal resistance identification in the embodiments of this application; Figure 8 This is a graph showing the MSD (Parameter Identification and Evaluation) result of the embodiment of this application. Detailed Implementation

[0024] The following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0025] Unless otherwise defined, the technical or scientific terms used in this application shall have the ordinary meaning as understood by a person of ordinary skill in the art to which this application pertains.

[0026] The terms "comprising" or "including," as used in this application, mean that the element preceding the term encompasses the element listed after the term, and do not exclude the possibility of encompassing other elements as well. The terms "inner," "outer," "upper," and "lower," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application. When the absolute position of the described object changes, the relative positional relationship may also change accordingly. In this application, unless otherwise expressly specified and limited, the term "attached," etc., should be interpreted broadly. For example, it can refer to a fixed connection, a detachable connection, or an integral part; it can refer to a direct connection or an indirect connection through an intermediate medium; it can refer to the internal communication of two elements or the interaction relationship between two elements. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0027] Example 1: A method for detecting the health status of supercapacitors in an energy storage power station, such as Figure 1 As shown, it includes the following steps: S1: Construct a supercapacitor switching system model. Divide the switching system into a first subsystem and a second subsystem according to whether the equalization circuit is activated. The first subsystem corresponds to the equalization inactive mode, and the second subsystem corresponds to the equalization activated mode. Construct the state space expressions for the first subsystem and the second subsystem respectively. Specifically, in a supercapacitor series module, a typical passive equalization circuit adjusts the voltage of each unit by introducing the internal resistance of a switch. Figure 2 The equivalent circuit is shown, and the system's dynamic characteristics can be differentiated into two modes: Mode 1: The equalization circuit is inactive, and all input current flows through the individual cells; Mode 2: The equalization circuit is active, and part of the input current flows through the individual cells. Therefore, the individual cell current can be expressed as: ; in, i c This represents the current flowing through the individual cell.

[0028] The supercapacitor switching system model is represented as follows: ; Based on the general form of the state-space expression of the switching system: ; Among them, state variables x Set as equivalent capacitor voltage v c System input u Set as total input current i Observed variables y The terminal voltage of the supercapacitor v The subsystem characterization parameters satisfy... σ = s +1, σ ∈{1,2}. A σ , B σ , C σ , D σ For subsystem σ The corresponding state-space equation coefficients.

[0029] The coefficients of the state-space equations for the first subsystem are expressed as follows: ; The coefficients of the state-space equations for the second subsystem are expressed as follows: ; in, C This is the equivalent capacitance of a supercapacitor. r This is the equivalent internal resistance of the supercapacitor. R To passively balance internal resistance, s For equalization switch switching signal, This is the voltage across the equivalent capacitance of the supercapacitor. i This represents the total input current.

[0030] S2: Construct a dual regressor parameter identification structure adapted to the supercapacitor switching system model. Through discretization, make the first subsystem and the second subsystem each correspond to an independent regressor and coefficient vector, and the first subsystem and the second subsystem share the regression vector. Specifically, the dual regressor structure is obtained by discretizing the continuous state-space expression of the switching system, and the transfer function can be obtained by introducing a bilinear transformation: ; in, z Let Z be the Z-transform variable. The discretized system dynamic equations are expressed as follows: ; in, n For time variables, t For unit sampling time, express n Estimate the terminal voltage at all times. for n Input current at any given time for n The supercapacitor terminal voltage at time -1 σ = s +1, σ ∈{1,2}.

[0031] The above equations can be rearranged into vector form: ; The coefficient vector is represented as: ; The regression vector is represented as: ; in, For the first σ The first element of the subsystem coefficient vector.

[0032] The subsystem's dynamic characteristics are modeled within a mathematical framework of vector form of the discretized system dynamic equations, which allows for the application of shared regression vectors. Since the switching signal can be sampled, the subsystem can be configured with independent regressors, forming a dual-regressor structure adapted to the switching system.

[0033] S3: Based on the dual regressor parameter identification structure, a cooperative switching recursive least squares algorithm based on restraint control is adopted to achieve cooperative identification between the parameters of the first subsystem and the second subsystem. Switching operations only affect the external current, while the internal characteristics remain unchanged. Therefore, the evolution of physical parameters is slower than the switching dynamics. Parameter changes are negligible between two switching operations, exhibiting quasi-invariance. This characteristic generates numerical inertia, suggesting an inherent correlation between the parameters of the two subsystems.

[0034] This application introduces restraint control theory to achieve parameter coordination between the two subsystems. In the dual regressor structure, the topological relationship of the regression variables is transformed from a simultaneous form to a cascaded form, such as... Figure 3 As shown: When the leader subsystem 1 is activated, only negative feedback terms are executed, node interactions remain silent, and follower subsystem 2 maintains the latest updated state; when subsystem 2 is activated, only interaction terms are executed, the leader node maintains the latest state, and the subsystem reference value is calculated through the discretized system dynamic equations. Therefore, the update rate... It can be designed as: ; in, α To control the coefficient, θ * The identification coefficient vector under subsystem 1 condition The result of mapping the coefficient vector and regression vector formula to subsystem 2 is that the coefficient vector... The reference value for restraint control under subsystem 2 conditions. The updated gradient can be obtained by rearranging the virtual vectors into vector form. for: ; in, For subsystem σ Corresponding virtual sample set, For virtual regression vectors, These are virtual observations. The expression for the collaborative update loss function. J CU for: ; Joint least squares fitting The total loss function of the algorithm can be written as: ; in, ρ To switch control coefficients, the following must be satisfied: ; The total loss function for supercapacitor parameter identification is expressed as: ; in, λ Forgetting factor, satisfying λ ∈(0,1), For containing N The switching sequence of a sample.

[0035] The final recursive form of the algorithm is: Constructing an information regression information matrix : ; Update covariance matrix : ; Calculate the correction vector : ; Update coefficient vector : ; S4: Set up a cold start handling mechanism. Before the first subsystem completes its initial effective update, suspend the coordination between subsystems and allow each subsystem to update independently. After the first subsystem obtains a priori confidence update, the cascaded coordination control is restored. The cold start processing mechanism uses a constraint control coefficient. α Implementation, specifically including: Independent update phase: Forced setting before the first subsystem completes its initial effective update. α =0, so that the first subsystem and the second subsystem can independently perform recursive least squares updates; Collaborative update phase: After the first subsystem obtains the prior confidence update, the restraint control coefficients are restored. α Configure and enter the cascaded restraint control structure.

[0036] Control coefficient α Set as: ; in, For the first n Subsystems.

[0037] S5: Based on the coefficient vectors of the first and second subsystems obtained from the identification, solve the equivalent capacitance, equivalent internal resistance, and equalization internal resistance of the supercapacitor. The equivalent capacitance of the supercapacitor obtained by inverse kinematics is expressed as: ; The equivalent internal resistance of the inverse solution is expressed as: ; The equilibrium internal resistance of the inverse solution is expressed as: ; in, This is the first element of the coefficient vector of the second subsystem.

[0038] S6: Based on the identified equivalent capacitance, quantitatively assess the health status of the supercapacitor.

[0039] The health status of a supercapacitor is represented as follows: ; in, This is the nominal capacitance value.

[0040] Example 2: To further verify the effectiveness of the method in this application, a design was made as follows: Figure 4 The verification experiment shown illustrates the switching control signals corresponding to the multi-stage constant current charging condition and the collaborative equalization strategy. The experiment collected the terminal voltage of each supercapacitor in the series module, the total input current of the module, and the switching signals of the equalization circuit, with a sampling frequency of 10Hz. The proposed cooperative switching recursive least squares algorithm based on restraint control was applied to the equivalent capacitance... Equivalent internal resistance and balanced internal resistance By performing parameter identification, we can obtain Figure 5 , Figure 6 , Figure 7 The results are shown. Parameter identification results show that the algorithm effectively converges to the reference offline identification results. The restraint control provides a virtual continuous excitation for the subsystem, enabling the identification of equivalent physical parameters even in sections where the actual current is constant. The restraint update also makes parameter estimation smoother, allowing for rapid convergence after system switching, without drastic jumps or jitters. Figure 8 The figure shows the real-time mean square deviation (MSD) of the three parameters, with an average value of -23.17 dB, and the final SOH estimation absolute deviation is 0.034%.

[0041] Therefore, this application adopts the aforementioned method for detecting the health status of supercapacitors in energy storage power stations. By matching the switching system model of the supercapacitor passive balancing system, the system structure can be dynamically switched according to the switching state of the balancing resistor, accurately describing the electrical dynamic behavior under different operating modes. A dual regressor parameter identification structure adapted to the binary switching system is constructed. By independently maintaining the regressors and coefficient vectors of the two subsystems, model mismatch and estimation errors caused by data aliasing are avoided. Based on a cooperative switching recursive least squares algorithm using a restraint control strategy, the synchronous identification of physical parameters between subsystems is achieved by introducing a restraint update term and a cold start processing mechanism, ensuring stable convergence under low-excitation and frequent switching conditions.

[0042] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application and not to limit them. Although this application has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of this application, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of this application.

Claims

1. A method for detecting the state of health of a supercapacitor of an energy storage power plant, characterized in that, Includes the following steps: S1: Construct a supercapacitor switching system model, and divide the switching system into a first subsystem and a second subsystem based on whether the equalization circuit is activated; The first subsystem corresponds to the balanced inactive mode, and the second subsystem corresponds to the balanced active mode. Construct the state-space expressions for the first and second subsystems respectively; S2: Construct a dual regressor parameter identification structure adapted to the supercapacitor switching system model. Through discretization, make the first subsystem and the second subsystem each correspond to an independent regressor and coefficient vector, and the first subsystem and the second subsystem share the regression vector. S3: Based on the dual regressor parameter identification structure, a cooperative switching recursive least squares algorithm based on restraint control is adopted to achieve cooperative identification between the parameters of the first subsystem and the second subsystem. S4: Set up a cold start handling mechanism. Before the first subsystem completes its initial effective update, suspend the coordination between subsystems and allow each subsystem to update independently. After the first subsystem obtains a priori confidence update, the cascaded coordination control is restored. S5: Based on the coefficient vectors of the first and second subsystems obtained from the identification, solve the equivalent capacitance, equivalent internal resistance, and equalization internal resistance of the supercapacitor. S6: Based on the identified equivalent capacitance, quantitatively assess the health status of the supercapacitor.

2. The method for detecting the health status of a supercapacitor in an energy storage power station according to claim 1, characterized in that, The supercapacitor switching system model is represented as follows: ； The coefficients of the state-space equations for the first subsystem are expressed as follows: ； The coefficients of the state-space equations for the second subsystem are expressed as follows: ； in, C This is the equivalent capacitance of a supercapacitor. r This is the equivalent internal resistance of the supercapacitor. R To passively balance internal resistance, s For equalization switch switching signal, This is the voltage across the equivalent capacitance of the supercapacitor. i This represents the total input current.

3. The method for detecting the health status of a supercapacitor in an energy storage power station according to claim 2, characterized in that, The discretized system dynamic equations are expressed as follows: ； in, n For time variables, t For unit sampling time, express n Estimate the terminal voltage at all times. for n Input current at any given time for n The supercapacitor terminal voltage at time -1 σ = s +1, σ ∈{1,2}.

4. The method for detecting the health status of a supercapacitor in an energy storage power station according to claim 3, characterized in that, The coefficient vector is represented as: ； The regression vector is represented as: ； in, For the first σ The first element of the subsystem coefficient vector.

5. The method for detecting the health status of a supercapacitor in an energy storage power station according to claim 4, characterized in that, The total loss function for supercapacitor parameter identification is expressed as: ； in, λ Forgetting factor, satisfying λ ∈(0,1), For containing N Switching sequence of individual samples This is the total loss function of the least squares algorithm.

6. The method for detecting the health status of a supercapacitor in an energy storage power station according to claim 5, characterized in that, The total loss function of the cooperative switching recursive least squares algorithm based on restraint control is expressed as: ； Collaborative update loss function Represented as: ； in, The least squares fitting loss function is used. ρ To switch control coefficients, For virtual observation samples, This is a virtual regression vector.

7. The method for detecting the health status of a supercapacitor in an energy storage power station according to claim 6, characterized in that, The cold start processing mechanism in S4 uses a constraint control coefficient. α Implementation, specifically including: Independent update phase: Forced setting before the first subsystem completes its initial effective update. α =0, so that the first subsystem and the second subsystem can independently perform recursive least squares updates; Collaborative update phase: After the first subsystem obtains the prior confidence update, the restraint control coefficients are restored. α Configure and enter the cascaded restraint control structure.

8. The method for detecting the health status of a supercapacitor in an energy storage power station according to claim 7, characterized in that, Control coefficient α Set as: ； in, For the first n Subsystems.

9. The method for detecting the health status of a supercapacitor in an energy storage power station according to claim 8, characterized in that, The equivalent capacitance of the supercapacitor obtained by inverse kinematics is expressed as: ； The equivalent internal resistance of the inverse solution is expressed as: ； The equilibrium internal resistance of the inverse solution is expressed as: ； in, This is the first element of the coefficient vector of the second subsystem.

10. The method for detecting the health status of a supercapacitor in an energy storage power station according to claim 9, characterized in that, The health status of a supercapacitor is represented as follows: ； in, This is the nominal capacitance value.

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