Parameter estimation method and device of super capacitor, and equalization method and device

Abstract

The application discloses a super capacitor parameter estimation method and device, and a super capacitor equalization method and device. The super capacitor parameter estimation method comprises the following steps: establishing an equivalent circuit model of the super capacitor; wherein the equivalent circuit model comprises parallel instantaneous RC branches, short-time RC branches, long-time RC branches and self-discharge resistance branches, and the instantaneous RC branches, the short-time RC branches and the long-time RC branches have different time constants. A state space expression of the super capacitor is determined according to the equivalent circuit model, wherein the output quantity of the state space expression is the value of the terminal voltage, and the state variables in the state space expression comprise the capacitance values of the capacitors of the branches of the equivalent circuit model, the resistance values of the resistors of the branches and the voltage values of the capacitors in the branches. The state variables in the state space expression are estimated by using an unscented Kalman algorithm. The parameters of the super capacitor are calculated according to the estimated values of the state variables. The application can improve the parameter estimation accuracy of the super capacitor.

Description

Technical Field

[0001] The embodiments of the present invention relate to energy storage technology, and more particularly to a method and apparatus for estimating parameters of a supercapacitor, as well as a method and apparatus for equalization. Background Technology

[0002] Compared with traditional energy storage technologies, supercapacitors have advantages such as high power density, high charge and discharge efficiency, ultra-long lifespan, good performance at high and low temperatures, and strong high-current discharge capability. Supercapacitor energy storage is widely used in new power systems. In supercapacitor energy storage systems, the role of supercapacitor management is particularly significant, playing a crucial role in the coordinated control of supercapacitors and reducing operating costs.

[0003] However, the rationality of supercapacitor management depends on the accuracy of the obtained supercapacitor parameters. Currently, research and applications of supercapacitor management strategies and systems are limited to providing power for automobiles and ships, and the accuracy of estimating supercapacitor parameters remains relatively low. Summary of the Invention

[0004] This invention provides a method and apparatus for estimating parameters of a supercapacitor, as well as an equalization method and apparatus, to improve the accuracy of parameter estimation for supercapacitors.

[0005] In a first aspect, embodiments of the present invention provide a method for estimating the parameters of a supercapacitor, the method comprising: An equivalent circuit model of the supercapacitor is established; wherein, the equivalent circuit model includes a parallel instantaneous RC branch, a short-time RC branch, a long-time RC branch, and a self-discharge resistance branch, and the instantaneous RC branch, the short-time RC branch, and the long-time RC branch have different time constants; The state-space expression of the supercapacitor is determined based on the equivalent circuit model, wherein the output quantity of the state-space expression is the value of the terminal voltage, and the state variables in the state-space expression include the capacitance value of each branch of the equivalent circuit model, the resistance value of each branch, and the voltage value of the capacitor in each branch. The state variables in the state-space expression are estimated using the unscented Kalman algorithm; The parameters of the supercapacitor are calculated based on the estimated values ​​of the state variables, including SOE and SOH.

[0006] Optionally, the instantaneous RC branch includes a variable capacitor, a fixed capacitor, and an equivalent series resistance, wherein the equivalent series resistance and the fixed capacitor are connected in series, and the variable capacitor and the fixed capacitor are connected in parallel; the short-time RC branch includes a short-time equivalent resistance and a short-time capacitor connected in series; the long-time RC branch includes a long-time capacitor and a long-time equivalent resistance connected in series; and the self-discharge resistance branch includes a self-discharge resistor.

[0007] Optionally, the state variables in the state-space expression include: the capacitance value of the variable capacitor in the instantaneous RC branch, the equivalent capacitance value of the branch with the fixed capacitor and the variable capacitor in parallel, the resistance value of the equivalent series resistor, and the voltage value of the variable capacitor; the capacitance value of the short-time capacitor in the short-time RC branch, the resistance value of the short-time equivalent resistor, and the voltage value of the short-time capacitor; the capacitance value of the long-time capacitor in the long-time RC branch, the resistance value of the long-time equivalent resistor, and the voltage value of the long-time capacitor; and the resistance value of the self-discharge resistor in the self-discharge branch.

[0008] Optionally, the state variables in the state-space expression are estimated using the unscented Kalman algorithm, including: Based on the unscented Kalman algorithm, the state-space expression of the supercapacitor is set as a general expression; Initialize the covariance of the algorithm error and the state variables in the state-space expression; Perform a nonlinear transformation on the initial Sigma point set related to the state variable in the state space expression after initialization to obtain a preset number of first Sigma point sets; Based on the preset number of first Sigma point sets after nonlinear transformation, determine the covariance of the algorithm error and the predicted value of the state variable corresponding to time; Perform a nonlinear transformation on the set of second Sigma points related to the state variables in the state space expression after initialization to obtain the preset number of second Sigma point sets; Update the state-space expression and the covariance of the algorithm error based on the second Sigma point set; The estimated value of the state variable is determined based on the updated state-space expression.

[0009] Optionally, calculating the parameters of the supercapacitor based on the estimated values ​​of the state variables includes: The SOE of the supercapacitor is calculated based on the estimated values ​​of the state variables and the first calculation formula, wherein the first calculation formula is used to characterize the relationship between the estimated values ​​of the capacitance and voltage of each branch capacitor in the state variables and the SOE.

[0010] Optionally, determining the state-space expression of the supercapacitor based on the equivalent circuit model includes: Based on the equivalent circuit model, determine the current expression and the corresponding voltage expression for each branch; The state-space expression is determined based on the current expression and the corresponding voltage expression for each branch.

[0011] Optionally, based on the equivalent circuit model, the current expression and corresponding voltage expression for each branch are determined, including: The current expression for each branch is determined based on the equivalent circuit model. By applying a linear approximation to the current expression, the voltage expression for the corresponding branch is determined.

[0012] Optionally, the preset quantity is 2n+1, where n is the number of state variables.

[0013] Optionally, calculating the parameters of the supercapacitor based on the estimated values ​​of the state variables includes: The SOH of the supercapacitor is calculated based on the estimated value of the state variable and the second calculation formula, wherein the second calculation formula is used to characterize the relationship between the factory rated resistance and the estimated actual resistance of the equivalent series resistance in the instantaneous RC branch of the state variable and the SOH.

[0014] Secondly, embodiments of the present invention also provide an equalization method for a supercapacitor bank, wherein the supercapacitor bank includes multiple supercapacitors; the equalization method includes: According to the parameter estimation method of any of the supercapacitors described in the first aspect, the parameters of each supercapacitor in the supercapacitor group are obtained, wherein the parameters include SOE and SOH; Determine whether the supercapacitor bank needs balancing based on its voltage data. When the supercapacitor bank needs to be balanced, the supercapacitor bank is balanced according to the SOH and SOE of each supercapacitor in the supercapacitor bank.

[0015] Optionally, determining whether the supercapacitor bank needs balancing based on its voltage data includes: If the total system voltage of the supercapacitor bank is greater than a preset voltage value and the maximum voltage difference between the individual supercapacitors in the supercapacitor bank is greater than a preset voltage difference, it is determined that the supercapacitor bank needs to be balanced.

[0016] Thirdly, embodiments of the present invention also provide a parameter estimation device for a supercapacitor. The device includes: an equivalent model establishment module, a state-space expression determination module, an estimation module, and a calculation module. The equivalent model establishment module is used to establish an equivalent circuit model of the supercapacitor; wherein the equivalent circuit model includes parallel instantaneous RC branches, short-time RC branches, long-time RC branches, and self-discharge resistance branches, and the instantaneous RC branches, short-time RC branches, and long-time RC branches have different time constants. The state-space expression determination module is used to determine the state-space expression of the supercapacitor based on the equivalent circuit model, wherein the output quantity of the state-space expression is the terminal voltage, and the state variables in the state-space expression include the capacitance, resistance, and voltage of each branch of the equivalent circuit model. The estimation module is used to estimate the state variables in the state-space expression using an unscented Kalman algorithm. The calculation module is used to calculate the parameters of the supercapacitor based on the estimated values ​​of the state variables, the parameters including SOE and SOH.

[0017] Fourthly, embodiments of the present invention also provide an equalization device for a supercapacitor bank. The active equalization control device for the supercapacitor bank includes: a parameter acquisition module, an equalization judgment module, and an equalization module. The parameter acquisition module is used to acquire the parameters of each supercapacitor in the supercapacitor group according to the parameter estimation method of any of the supercapacitors described in the first aspect, wherein the parameters include SOE and SOH; The equalization judgment module is used to determine whether the supercapacitor bank needs equalization based on the voltage data of the supercapacitor bank. The equalization module is used to equalize the supercapacitor group according to the SOH and SOE of each supercapacitor in the supercapacitor group when equalization is required.

[0018] The present invention provides a parameter estimation method, parameter estimation device, equalization method, and equalization device for supercapacitors. Considering self-discharge and capacitance variation effects, a three-branch equivalent circuit model of the supercapacitor is established. The self-discharge branch in the equivalent circuit model can simulate the self-discharge phenomenon of the supercapacitor, improving the model's ability to simulate the charge redistribution process in the supercapacitor and thus increasing the accuracy of parameter estimation. An unscented Kalman algorithm is employed in the parameter estimation process, reducing the computational complexity. Attached Figure Description

[0019] Figure 1 A flowchart illustrating a parameter estimation method for a supercapacitor provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of an equivalent circuit model provided in an embodiment of the present invention; Figure 3 A flowchart illustrating another method for estimating the parameters of a supercapacitor provided in an embodiment of the present invention; Figure 4 A flowchart illustrating another method for estimating the parameters of a supercapacitor provided in an embodiment of the present invention; Figure 5 A schematic diagram of a parameter estimation device for a supercapacitor provided in an embodiment of the present invention; Figure 6 A flowchart illustrating an equalization method for a supercapacitor bank provided in an embodiment of the present invention. Figure 7 This is a schematic diagram of the structure of an equalization device for a supercapacitor bank provided in an embodiment of the present invention. Detailed Implementation

[0020] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the present invention, and not all of the structures.

[0021] This invention provides a method for estimating the parameters of a supercapacitor. Figure 1 This is a flowchart illustrating a parameter estimation method for a supercapacitor provided in an embodiment of the present invention. Figure 2 This is a schematic diagram of an equivalent circuit model provided in an embodiment of the present invention, combined with... Figure 1 and Figure 2 Methods for estimating the parameters of supercapacitors include: S101. Establish the equivalent circuit model of the supercapacitor.

[0022] The equivalent circuit model refers to the circuit model that simulates the function and effect of the actual circuit of the supercapacitor. The equivalent circuit model includes a parallel instantaneous RC branch 201, a short-time RC branch 202, a long-time RC branch 203, and a self-discharge resistance branch 204. The instantaneous RC branch 201, the short-time RC branch 202, and the long-time RC branch 203 have different time constants.

[0023] Specifically, considering the self-discharge phenomenon and capacitance change effect of the supercapacitor, an equivalent circuit model 200 of the supercapacitor is established. In the equivalent circuit model 200, the time constant of the instantaneous RC branch 201 is the shortest, the time constant of the short-time RC branch 202 is longer than the time constant of the instantaneous RC branch 201, and the time constant of the long-time RC branch 203 is longer than the time constants of both the instantaneous RC branch 201 and the short-time RC branch 202.

[0024] For example, the instantaneous RC branch 201 may include a variable capacitor, a fixed capacitor, and an equivalent series resistance, with the equivalent series resistance and the fixed capacitor connected in series, and the variable capacitor and the fixed capacitor connected in parallel. The short-time RC branch 202 may include a short-time equivalent resistance and a short-time capacitor connected in series. The long-time RC branch 203 may include a long-time capacitor and a long-time equivalent resistance connected in series. The self-discharge resistance branch 204 may include a self-discharge resistor. The self-discharge resistance branch 204 in the equivalent circuit model can simulate the self-discharge phenomenon of the supercapacitor, improving the simulation capability of the equivalent circuit model for the charge redistribution process in the supercapacitor.

[0025] S102. Determine the state-space expression of the supercapacitor based on the equivalent circuit model.

[0026] In a state-space expression, state equations and output equations are combined. A state-space expression is a formula that fully describes the relationships between state variables in the equivalent circuit model. The state equations in a state-space expression are the expressions for the state variables. The output equation of a state-space expression is the terminal voltage. The output variable in the state-space expression is the terminal voltage. The state variables in the state-space expression include the capacitance value of each branch, the resistance value of each branch, and the voltage value of each branch's capacitor in the equivalent circuit model.

[0027] Specifically, based on the components of each branch in the equivalent circuit model, the current and voltage expressions for each branch can be determined using state variables. Then, based on the current and voltage expressions for each branch, the state-space expression of the equivalent circuit model can be determined.

[0028] For example, the current expression for each branch is derived from the state variables of each branch in the equivalent circuit model. By applying a linear approximation to the current expression of the branch, the voltage expression for the voltage across the capacitor in the corresponding branch can be determined. This voltage expression is represented by the other state variables of the branch and the current value of the branch. Furthermore, the first-order differential equation for the capacitor voltage in the branch can be determined based on the voltage expression. Based on the current expression, voltage expression, and the first-order differential equation for the capacitor voltage, the first-order differential equations for the capacitance values ​​in the branch and / or the first-order differential equation for the equivalent capacitance of the branch are determined.

[0029] Using the terminal voltage of the supercapacitor as the output quantity of the state space, and taking the output quantity of the state space as the last state variable, the state variable expressions are written according to the relationship between the state variables in each branch and the terminal voltage in the equivalent circuit model. Based on the degree of change of each state variable over time, the first-order differential of the state variables that are constant or change slowly over time is set to 0, which is the first state equation. Based on at least one of the following: the voltage expression of the capacitor, the first-order differential equation of the capacitor voltage, the first-order differential equation of each capacitor value, and the first-order differential equation of the equivalent capacitance of the branch, the input quantity is set as the instantaneous current value of the RC branch. The expressions for the first-order differentials of other state variables whose first-order differentials are not 0 are determined, which are the second state equations. Based on the state variable expressions, the first-order differential of the terminal voltage is expressed in terms of the state variables, which is the output equation.

[0030] S103. Use the unscented Kalman algorithm to estimate the state variables in the state-space expression.

[0031] Among them, the unscented Kalman algorithm refers to the Kalman filtering algorithm that uses unscented transformation to sample state variables.

[0032] Specifically, the state-space expression of the equivalent circuit model of a supercapacitor can be converted into a general expression of the discrete state space in the unscented Kalman filter algorithm. Initial values ​​are then assigned to the state variables in the state-space expression to initialize the unscented Kalman algorithm. An unscented transformation is performed on the Sigma point set in the state space, and a one-step prediction calculation is performed on the transformed Sigma point set. Then, a new Sigma point set is selected and an unscented transformation is performed on the new Sigma point set. Finally, the results of the previous prediction calculation are updated based on the transformed Sigma point set to determine the Kalman gain matrix, thereby obtaining the final estimated values ​​of the state variables.

[0033] S104. Calculate the parameters of the supercapacitor based on the estimated values ​​of the state variables.

[0034] The parameters include SOE and SOH. SOE refers to the remaining percentage of charge in the supercapacitor. SOH refers to the health status of the supercapacitor, which can be the ratio of the current capacity to the original factory capacity.

[0035] Specifically, based on the relationship between the capacitance and voltage values ​​of each branch in the state variables and the State of Emergency (SOH), the SOH of the supercapacitor is calculated using the predicted capacitance and voltage values ​​of each branch. Similarly, based on the relationship between the factory rated resistance and actual resistance values ​​of the resistors in the instantaneous RC branches in the state variables and the State of Emergency (SOE), the SOE of the supercapacitor is calculated using the factory rated resistance and predicted resistance values ​​of the resistors in the instantaneous RC branches.

[0036] The supercapacitor parameter estimation method provided in this embodiment considers self-discharge and capacitance variation effects, and establishes a three-branch equivalent circuit model of the supercapacitor. The self-discharge branch in the equivalent circuit model can simulate the self-discharge phenomenon of the supercapacitor, improving the simulation capability of the equivalent circuit model for the charge redistribution process in the supercapacitor and thus improving the accuracy of parameter estimation. An unscented Kalman algorithm is used in the parameter estimation process to reduce the computational complexity.

[0037] Figure 3 This is a flowchart illustrating another method for estimating the parameters of a supercapacitor provided in an embodiment of the present invention, combined with... Figure 2 and Figure 3 Methods for estimating the parameters of supercapacitors include: S301. Establish the equivalent circuit model of the supercapacitor.

[0038] Step S301 is the same as step S101, and will not be repeated here.

[0039] S302. Based on the equivalent circuit model, determine the current expression and the corresponding voltage expression for each branch.

[0040] The current expression refers to the expression for the current value flowing through each branch, which can be represented by the state variables in the equivalent circuit model of the supercapacitor. The voltage expression refers to the expression for the voltage value across the capacitor in each branch, which can be represented by the current value in the corresponding branch and state variables other than the voltage value of the capacitor.

[0041] Specifically, the state variables in the state space of the equivalent circuit model include: the capacitance value of the variable capacitor in the instantaneous RC branch. The capacitance value of a fixed capacitor The resistance value of the equivalent series resistance and the voltage value of the variable capacitor The capacitance value of the short-time capacitor in the short-time RC branch The resistance value of the short-time equivalent resistance and the voltage value of the short-time capacitor The capacitance value of the long-time capacitor in the long-time RC branch The resistance value of the long-term equivalent resistance and the voltage value of the long-term capacitor The resistance value of the self-discharge resistor in the self-discharge branch The terminal voltage of a supercapacitor The value is used as the output quantity of the state space.

[0042] The current expressions for each branch are determined based on the equivalent circuit model. For example, according to the composition of each branch, the current expressions for the instantaneous RC branch, short-time RC branch, long-time RC branch, and self-discharge branch can be sequentially determined as the first current expression, the second current expression, the third current expression, and the fourth current expression. The first current expression is... ,in, This represents the instantaneous current value of the RC branch. This is the capacitance value of the fixed capacitor in the instantaneous RC branch. This represents the capacitance value of the variable capacitor in the instantaneous RC branch. This is the instantaneous voltage value of the capacitors in the RC branch (the voltage values ​​of the two capacitors are the same). The expression for the second current is: ,in, This represents the current value of the short-time RC branch. This represents the capacitance value of the short-time capacitor in the short-time RC branch. This represents the voltage across the short-time capacitor in the short-time RC branch. The expression for the third current is: ,in, This represents the current value of the long-term RC branch. This represents the capacitance value of the long-time capacitor in the long-time RC branch. This represents the voltage value of the long-time and short-time capacitors in the long-time RC branch. The expression for the fourth current is: ,in, This represents the current value of the self-discharge branch. This is the terminal voltage value of the supercapacitor. This is the resistance value of the self-discharge resistor in the self-discharge branch.

[0043] Then, a linear approximation is applied to the current expression to determine the voltage expression for the corresponding branch. For example, a linear approximation is applied to the first current expression to determine the voltage expression of the capacitor in the instantaneous RC branch of the supercapacitor, which is the first voltage expression. The first voltage expression is: ,in, This represents the instantaneous voltage across the capacitor in the RC branch. This is the equivalent capacitance value of the instantaneous RC branch. .

[0044] S303. Determine the state-space expression based on the current expression and the corresponding voltage expression of each branch.

[0045] Specifically, based on the voltage expression, the corresponding first-order differential equation for the voltage can be obtained. For example, differentiating the first voltage expression yields the first-order differential equation determining the voltage value of the capacitor in the instantaneous RC branch, which serves as the first voltage differential equation. The first voltage differential equation is: .

[0046] Then, based on the current expression, voltage expression, and the first-order differential equation of the corresponding voltage for the branch, the first-order differential equation for the capacitance value in the corresponding branch is determined. For example, based on the first current expression, first voltage expression, and first voltage differential equation, the first-order differential equation for the reciprocal of the equivalent capacitance of the instantaneous RC branch can be obtained. The first-order differential equation for the reciprocal of the equivalent capacitance of the instantaneous RC branch is: .

[0047] If we consider the terminal voltage of the supercapacitor as the output quantity y of the supercapacitor system, and treat the output quantity y as the last state variable, then the state variable expression of the system can be: ,and ,in, The value of the equivalent series resistance in the instantaneous RC branch is given. This represents the short-time equivalent resistance value in the short-time RC branch. This represents the long-term equivalent resistance value in the long-term RC branch. This represents the resistance value of the self-discharge resistor in the self-discharge branch. Let represent the total current of the supercapacitor. Based on the system's state variable expression, the system's state variable matrix can be determined as follows: Among them, state variables include , , , , , , , , , and There are 11 in total. The equivalent capacitance value of the instantaneous RC branch can be represented by other state variables in the instantaneous RC branch, and the output quantity is 1. .

[0048] The state-space representation includes state equations and output equations, where the state equations include a first state equation and a second state equation. Because... , , , , , and Since all of these conditions remain constant or change relatively slowly over time, the first state equation can be obtained: = = = = = = =0. From the voltage expression, the voltage differential equation, and the first-order differential equation for the capacitance value in the corresponding branch, and setting the system input u of the equivalent circuit model as the instantaneous RC branch current value in the state variable expression, the second state equation can be obtained: and To ensure the observability of the equivalent circuit model, the system output is set as y in the state variable expression, resulting in the output equation: .

[0049] S304. Use the unscented Kalman algorithm to estimate the state variables in the state-space representation.

[0050] S305. Calculate the parameters of the supercapacitor based on the estimated values ​​of the state variables.

[0051] Steps S304 and S305 correspond to the same content as steps S103 and S104, respectively, and will not be described again here.

[0052] The supercapacitor parameter estimation method provided in this embodiment considers self-discharge and capacitance variation effects, and establishes a three-branch equivalent circuit model of the supercapacitor. The self-discharge branch in the equivalent circuit model can simulate the self-discharge phenomenon of the supercapacitor, improving the simulation capability of the equivalent circuit model for the charge redistribution process in the supercapacitor and thus improving the accuracy of parameter estimation. An unscented Kalman algorithm is used in the parameter estimation process to reduce the computational complexity.

[0053] Figure 4 A flowchart illustrating another method for estimating the parameters of a supercapacitor provided in this embodiment of the invention is shown below. Figure 4 Methods for estimating the parameters of supercapacitors include: S401. Establish the equivalent circuit model of the supercapacitor.

[0054] S402. Determine the state-space expression of the supercapacitor based on the equivalent circuit model.

[0055] Steps S401 and S402 correspond one-to-one with the contents of steps S101 and S102, respectively, and will not be repeated here.

[0056] S403. Based on the unscented Kalman algorithm, the state-space expression of the supercapacitor is set as a general expression.

[0057] Specifically, combining the state-space expression, the general expression for the discrete state space of a supercapacitor system based on the unscented Kalman algorithm is as follows: ,in, Let be the k-th sampled value of the spatial vector corresponding to the state variable in the state space. It is the input quantity (i.e., branch current) of the supercapacitor system. Sampling time, This is the prediction error matrix of the algorithm. For the measurement error matrix, Let be the covariance matrix of the prediction error in the algorithm. Let be the covariance matrix of the measurement error. is the state-space equation in the state-space expression. This refers to the output expression in the output equation of the state-space representation. This represents the output (i.e., the terminal voltage) of the supercapacitor system corresponding to k sample values.

[0058] S404. Perform initialization operations on the covariance of the algorithm error and the state variables in the state-space expression.

[0059] The initialization operation refers to the process of assigning initial values ​​to the covariance of the state variables and algorithm error in the state-space expression before the first iteration of the Kalman algorithm. The first iteration refers to the process of obtaining the predicted value based on the first Sigma point set.

[0060] Specifically, the unscented Kalman algorithm is initialized by assigning initial values ​​to the state table variables in the state-space expression. For example, initial values ​​are assigned to the state variables of a supercapacitor system. Assign an error covariance matrix to the prediction process error of the system. The initialization process is completed before the first iteration.

[0061] S405. Perform a nonlinear transformation on the initial Sigma point set related to the state variables in the state space expression after initialization to obtain a preset number of first Sigma point sets.

[0062] Nonlinear transformation (i.e., unscented transformation) refers to the process of calculating a certain number of Sigma points (also known as key points), then transforming these Sigma points through a nonlinear function, and calculating the Gaussian distribution based on the transformation result and the corresponding weights.

[0063] Specifically, a certain number of initial Sigma points can be determined through random sampling. Then, the unscented Kalman algorithm is used to perform a first nonlinear transformation on the certain number of initial Sigma points in the state space to obtain the first Sigma point. The probability density function of the first Sigma point is equal to the true density function of the state vector. Since there are 11 state variables in the state space, the preset number of the first Sigma point is 2*11+1=23. Here, i is the index of the Sigma point. It is the state vector form of the (k-1)th sampled sigma point. It is the first i The middle value of the second sampling It is the value of the system state variable in the (k-1)th iteration. It is the process error of the (k-1)th prediction.

[0064] S406. Based on the preset number of first Sigma point sets after nonlinear transformation, determine the covariance of the algorithm error and the predicted value of the state variable corresponding to time.

[0065] Specifically, the state-space equations in the state-space expression The computational model is converted into a state vector form. Then, in the time update phase, the covariance matrix of the predicted values ​​of the state variables corresponding to time and the algorithm error can be expressed as: , It is the prior value of the state vector sampled k times. It is the prior value of the state vector sampled in k-1 samplings; It is the state vector form of the k-th sampled sigma point; It is the state vector of the error.

[0066] S407. Perform a nonlinear transformation on the quadratic Sigma point set related to the state variable in the state space expression after initialization to obtain the preset number of second Sigma point sets.

[0067] Specifically, similar to step S405, in the measurement update phase, a certain number of quadratic Sigma point sets can be determined through random sampling. Then, the unscented Kalman algorithm is used to perform a second nonlinear transformation on the certain number of quadratic Sigma point sets in the state space to obtain a preset number of second Sigma point sets. In subsequent calculations, the prediction results of data points in the second Sigma point sets that are identical to data points in the first Sigma point set can be directly used to improve prediction efficiency.

[0068] S408. Update the state-space expression and the covariance of the algorithm error based on the predicted values ​​of the state variables corresponding to time and the second Sigma point set after nonlinear transformation.

[0069] Specifically, the output quantity expression in the output equation of the state-space expression. Converting to state vector form, the output equation is: Therefore, the predicted output value in the state space and its algorithm error covariance matrix can be expressed as: ,in, To account for measurement error, It is the state vector of the output value sigma point; It is the state vector of the estimated output quantity in the state space; This is the covariance matrix of the predicted output values. Therefore, the predicted output values ​​in the state space are determined. Sampling prior values ​​of the state vector sampled at k times The cross covariance matrix can be expressed as Finally, based on the predicted output values, predicted state variables, algorithm error covariance matrix, and cross-covariance matrix of the state space, the Kalman filter gain can be obtained. Final state variable estimates and final algorithm error , can be represented as follows: .

[0070] S409. Determine the estimated values ​​of the state variables based on the updated state-space expression.

[0071] Specifically, based on the final state variable estimates The state variable matrix of the system with state variables is used to extract the estimated values ​​of state variables related to SOH and determine them as state indicators of SOH, and the estimated values ​​of state variables related to SOE and determine them as state indicators of SOE.

[0072] S410. Calculate the parameters of the supercapacitor based on the estimated values ​​of the state variables.

[0073] Specifically, the SOE of the supercapacitor is calculated based on the estimated values ​​of the state variables and the first calculation formula. The first calculation formula characterizes the relationship between the estimated capacitance and voltage values ​​of each branch capacitor in the state variables and the SOE. For example, the first calculation formula can be: ,in, This is the estimated capacitance of the fixed capacitor in the instantaneous RC branch. This is the estimated voltage of the variable capacitor in the instantaneous RC branch. This is the estimated capacitance value of the variable capacitor in the instantaneous RC branch. This is the estimated capacitance value of the short-time capacitor in the short-time RC branch. This is the estimated voltage of the short-time capacitor in the short-time RC branch. This is the estimated capacitance value of the long-time capacitor in the long-time RC branch. This is the voltage estimate of the long-time capacitor in the long-time RC branch. All estimates used in calculating SOE can be obtained from the SOE status indicator in step S408.

[0074] The state of equilibrium (SOH) of the supercapacitor is calculated based on the estimated values ​​of the state variables and the second calculation formula. The second calculation formula characterizes the relationship between the factory-rated and actual estimated values ​​of the equivalent series resistance in the instantaneous RC branch of the state variables and the SOH. For example, the second calculation formula can be... ,in, This is the factory-set resistance value of the equivalent series resistance in the instantaneous RC branch of the supercapacitor. This is an estimated value for the effective series resistance in the instantaneous RC branch of the supercapacitor. All estimates used in calculating SOH can be obtained from the SOH status indicator in step S408. The supercapacitor parameter estimation method provided in this embodiment considers self-discharge and capacitance variation effects, and establishes a three-branch equivalent circuit model of the supercapacitor. The self-discharge branch in the equivalent circuit model can simulate the self-discharge phenomenon of the supercapacitor, improving the simulation capability of the equivalent circuit model for the charge redistribution process in the supercapacitor and thus improving the accuracy of parameter estimation. In the parameter estimation process, an unscented Kalman algorithm is employed, using two unscented transformations to estimate the Kalman gain matrix, increasing the number of iterations and improving the algorithm's accuracy, thereby enhancing the parameter estimation accuracy of the supercapacitor.

[0075] This invention also provides a parameter estimation device for supercapacitors. Figure 5 This is a schematic diagram of a parameter estimation device for a supercapacitor provided in an embodiment of the present invention, with reference to... Figure 5 The supercapacitor parameter estimation device 500 includes: an equivalent model establishment module 501, a state-space expression determination module 502, an estimation module 503, and a calculation module 504. The equivalent model establishment module 501 is used to establish an equivalent circuit model of the supercapacitor; wherein, the equivalent circuit model includes parallel instantaneous RC branches, short-time RC branches, long-time RC branches, and self-discharge resistance branches, and the instantaneous RC branches, short-time RC branches, and long-time RC branches have different time constants; the state-space expression determination module 502 is used to determine the state-space expression of the supercapacitor based on the equivalent circuit model, wherein, the output quantity of the state-space expression is the terminal voltage, and the state variables in the state-space expression include the capacitance, resistance, and voltage of each branch of the equivalent circuit model; the estimation module 503 is used to estimate the state variables in the state-space expression using an unscented Kalman algorithm; the calculation module 504 is used to calculate the parameters of the supercapacitor based on the estimated values ​​of the state variables, and the parameters include SOE and SOH.

[0076] Optionally, based on the foregoing embodiments, the estimation module includes an initialization unit, a first nonlinear transformation unit, a prediction value determination unit, a second nonlinear transformation unit, an update unit, and an estimate value determination unit. The initialization unit is used to perform initialization operations on the covariance of the algorithm error and the state variables in the state space expression. The first nonlinear transformation unit is used to perform a nonlinear transformation on a preset number of first Sigma point sets related to the state variables in the state space expression after initialization. The prediction value determination unit is used to determine the covariance of the algorithm error and the predicted values ​​of the state variables corresponding to time based on the preset number of first Sigma point sets after nonlinear transformation. The second nonlinear transformation unit is used to perform a nonlinear transformation on a preset number of second Sigma point sets related to the state variables in the state space expression after initialization. The update unit is used to update the state space expression and the covariance of the algorithm error based on the predicted values ​​of the state variables corresponding to time and the second Sigma point sets after nonlinear transformation. The estimate value determination unit is used to determine the estimated values ​​of the state variables based on the updated state space expression. Optionally, based on the foregoing embodiments, the calculation module includes an SOE calculation unit, which is used to calculate the SOE of the supercapacitor based on the estimated values ​​of the state variables and a first calculation formula, wherein the first calculation formula is: ,in, This is the estimated capacitance of the fixed capacitor in the instantaneous RC branch. This is the estimated voltage of the variable capacitor in the instantaneous RC branch. This is the estimated capacitance value of the variable capacitor in the instantaneous RC branch. This is the estimated capacitance value of the short-time capacitor in the short-time RC branch. This is the estimated voltage of the short-time capacitor in the short-time RC branch. This is the estimated capacitance value of the long-time capacitor in the long-time RC branch. This is the estimated voltage of the long-time capacitor in the long-time RC branch.

[0077] Optionally, based on the foregoing embodiments, the calculation module further includes a State of Emergency (SOH) calculation unit. The SOH calculation unit is used to calculate the SOH of the supercapacitor based on the estimated value of the state variable and a second calculation formula. The second calculation formula is... ,in, This is the factory-set resistance value of the equivalent series resistance in the instantaneous RC branch of the supercapacitor. This is an estimated value for the equivalent series resistance in the instantaneous RC branch of the supercapacitor.

[0078] Optionally, based on the foregoing embodiments, the state-space expression determination module includes an expression determination unit and a state-space expression determination unit. The expression determination unit is used to determine the current expression and the corresponding voltage expression of each branch according to the equivalent circuit model; the state-space expression determination unit is used to determine the state-space expression based on the current expression and the corresponding voltage expression of each branch. The expression determination unit includes a current determination element and a voltage determination element. The current determination element is used to determine the current expression of each branch according to the equivalent circuit model; the voltage determination element is used to perform a linear approximation on the current expression to determine the voltage expression of the corresponding branch.

[0079] This invention also provides an active balancing method for supercapacitor banks, which is implemented by a control module in an active balancing control device for supercapacitor banks. Figure 6 This is a flowchart illustrating an equalization method for a supercapacitor bank provided in an embodiment of the present invention, referring to... Figure 6 A method for equalizing supercapacitor banks includes: S601. Obtain the parameters of each supercapacitor in the supercapacitor group.

[0080] Specifically, the parameter estimation method for supercapacitors proposed in any embodiment of the present invention is used to obtain the parameters of each supercapacitor in the supercapacitor group, including SOE and SOH.

[0081] S602. Determine whether the supercapacitor bank needs balancing based on its voltage data.

[0082] Specifically, since low voltage has a relatively small impact on the state of equilibrium (SOH) of supercapacitors, even if the voltages of the supercapacitors are inconsistent, the impact on the SOH of each supercapacitor is very small when the voltage of the supercapacitor bank is low. Based on experience, when the voltage of a single supercapacitor is 2.1V or below, the voltage has a small impact on the SOH of the supercapacitor, and equalization is not required. Therefore, for example, when determining whether equalization is needed, one can simultaneously determine whether the maximum voltage difference between the individual supercapacitors in the supercapacitor bank is greater than a preset voltage difference, and whether the total system voltage of the supercapacitor bank is greater than a preset voltage value. The preset voltage difference and preset voltage value can be obtained from supercapacitor bank charge-discharge experiments or empirical values. The preset voltage value can be Ue = M * 2.1V, where M is the number of supercapacitors in the supercapacitor bank. If the total system voltage of the supercapacitor bank is greater than the preset voltage value and the maximum voltage difference between the individual supercapacitors in the supercapacitor bank is greater than the preset voltage difference, it can be determined that the supercapacitor bank needs active equalization.

[0083] S603. When balancing is required, the supercapacitor bank is balanced according to the SOH and SOE of each supercapacitor in the supercapacitor bank.

[0084] Specifically, the voltage limits of the supercapacitors in the supercapacitor bank can be obtained. These voltage limits include high-voltage and low-voltage limits, which can be calculated based on the real-time state variables of the supercapacitors or read from a database to prevent overcharging or over-discharging during the equalization process. The equalization time can be determined based on the parameters of the individual supercapacitors. Next, the supercapacitor bank is equalized based on the State of Harm (SOH) and State of Energy (SOE) of each supercapacitor. The equalization method can be active equalization, which involves mutual charging and discharging between the supercapacitors in the bank to maintain the voltage values ​​of each individual supercapacitor within a preset range, thereby reducing the differences in SOE between the individual supercapacitors. For example, based on historical application data, if the voltages of the individual supercapacitors in the supercapacitor bank reach the equalization condition before discharge, and the voltage of the individual supercapacitor with the lower SOH is lower than the average value after discharge, then it is not necessary to activate other devices to passively charge and discharge the low-voltage individual supercapacitors for equalization. During the application intervals of the supercapacitor bank, active equalization is performed between the individual supercapacitors in the bank. Therefore, before the next use, the voltage of the individual supercapacitor with the lower SOH will return to the normal range. During the active balancing process, the voltage value of the supercapacitor is monitored in real time to prevent overcharging and over-discharging.

[0085] This invention also provides an active equalization control device for supercapacitor banks. Figure 7 This is a schematic diagram of the structure of an equalization device for a supercapacitor bank provided in an embodiment of the present invention, with reference to... Figure 7 The equalization device 700 for a supercapacitor bank includes a parameter acquisition module 701, an equalization judgment module 702, and an equalization module 703. The parameter acquisition module 701 is used to acquire the parameters of each supercapacitor in the supercapacitor bank according to the parameter estimation method of any supercapacitor according to any embodiment of the present invention. The parameters include SOE and SOH. The parameter acquisition module 701 may include a supercapacitor parameter estimation device. The equalization judgment module 702 is used to determine whether the supercapacitor bank needs equalization based on the voltage data of the supercapacitor bank. The equalization judgment module 702 includes a determination unit, which determines that the supercapacitor bank needs equalization when the total system voltage of the supercapacitor bank is greater than a preset voltage value and the maximum voltage difference between the individual supercapacitors in the supercapacitor bank is greater than a preset voltage difference. The equalization module 703 is used to equalize the supercapacitor bank based on the SOH and SOE of each supercapacitor in the supercapacitor bank when equalization is required.

[0086] This invention provides a parameter estimation method, parameter estimation device, equalization method, and equalization device for supercapacitors. Considering self-discharge and capacitance variation effects, a three-branch equivalent circuit model of the supercapacitor is established. The self-discharge branch in the equivalent circuit model can simulate the self-discharge phenomenon of the supercapacitor, improving the model's ability to simulate the charge redistribution process within the supercapacitor and thus increasing the accuracy of parameter estimation. An unscented Kalman algorithm is employed in the parameter estimation process, using two unscented transformations to estimate the Kalman gain matrix, increasing the number of iterations and improving the algorithm's accuracy, thereby enhancing the parameter estimation precision of the supercapacitor. The active equalization control device can perform active equalization within the supercapacitor group using the parameter estimates of each capacitor. This solves the problem of poor equalization performance caused by inaccurate consideration of the supercapacitor's health state in traditional equalization strategies, providing differentiated control for individual supercapacitors within the supercapacitor group and improving the overall consistency and reliability of the supercapacitor group.

[0087] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0088] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0089] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0090] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0091] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0092] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0093] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0094] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for estimating the parameters of a supercapacitor, characterized in that, include: An equivalent circuit model of the supercapacitor is established; wherein the equivalent circuit model includes a parallel instantaneous RC branch, a short-time RC branch, a long-time RC branch, and a self-discharge resistance branch, the instantaneous RC branch, the short-time RC branch, and the long-time RC branch having different time constants; the instantaneous RC branch includes a variable capacitor, a fixed capacitor, and an equivalent series resistance, the equivalent series resistance and the fixed capacitor being connected in series, and the variable capacitor and the fixed capacitor being connected in parallel; the short-time RC branch includes a short-time equivalent resistance and a short-time capacitor connected in series; the long-time RC branch includes a long-time capacitor and a long-time equivalent resistance connected in series; the self-discharge resistance branch includes a self-discharge resistor; The state-space expression of the supercapacitor is determined based on the equivalent circuit model, wherein the output quantity of the state-space expression is the value of the terminal voltage, and the state variables in the state-space expression include the capacitance value of each branch of the equivalent circuit model, the resistance value of each branch, and the voltage value of the capacitor in each branch. The state variables in the state-space expression are estimated using the unscented Kalman algorithm; The parameters of the supercapacitor are calculated based on the estimated values ​​of the state variables, including SOE and SOH.

2. The parameter estimation method for supercapacitors according to claim 1, characterized in that, The state variables in the state-space expression include: the capacitance value of the variable capacitor in the instantaneous RC branch, the equivalent capacitance value of the branch with the fixed capacitor and the variable capacitor in parallel, the resistance value of the equivalent series resistor, and the voltage value of the variable capacitor; the capacitance value of the short-time capacitor in the short-time RC branch, the resistance value of the short-time equivalent resistor, and the voltage value of the short-time capacitor; the capacitance value of the long-time capacitor in the long-time RC branch, the resistance value of the long-time equivalent resistor, and the voltage value of the long-time capacitor; and the resistance value of the self-discharge resistor in the self-discharge resistor branch.

3. The parameter estimation method for supercapacitors according to claim 1, characterized in that, The state variables in the state-space expression are estimated using the unscented Kalman algorithm, including: Based on the unscented Kalman algorithm, the state-space expression of the supercapacitor is set as a general expression; Initialize the covariance of the algorithm error and the state variables in the state-space expression; Perform a nonlinear transformation on the initial Sigma point set related to the state variable in the state space expression after initialization to obtain a preset number of first Sigma point sets; Based on the preset number of first Sigma point sets after nonlinear transformation, determine the covariance of the algorithm error and the predicted value of the state variable corresponding to time; Perform a nonlinear transformation on the set of second Sigma points related to the state variables in the state space expression after initialization to obtain the preset number of second Sigma point sets; Update the state-space expression and the covariance of the algorithm error based on the second Sigma point set; The estimated value of the state variable is determined based on the updated state-space expression.

4. The parameter estimation method for supercapacitors according to claim 2, characterized in that, The parameters of the supercapacitor are calculated based on the estimated values ​​of the state variables, including: The SOE of the supercapacitor is calculated based on the estimated values ​​of the state variables and the first calculation formula, wherein the first calculation formula is used to characterize the relationship between the estimated values ​​of the capacitance and voltage of each branch capacitor in the state variables and the SOE.

5. The parameter estimation method for supercapacitors according to claim 2, characterized in that, Determining the state-space expression of the supercapacitor based on the equivalent circuit model includes: Based on the equivalent circuit model, determine the current expression and the corresponding voltage expression for each branch; The state-space expression is determined based on the current expression and the corresponding voltage expression for each branch.

6. The parameter estimation method for a supercapacitor according to claim 5, characterized in that, Based on the equivalent circuit model, determine the current expression and corresponding voltage expression for each branch, including: The current expression for each branch is determined based on the equivalent circuit model. By applying a linear approximation to the current expression, the voltage expression for the corresponding branch is determined.

7. The parameter estimation method for a supercapacitor according to claim 3, characterized in that, The preset quantity is 2n+1, where n is the number of state variables.

8. The parameter estimation method for supercapacitors according to claim 2, characterized in that, The parameters of the supercapacitor are calculated based on the estimated values ​​of the state variables, including: The SOH of the supercapacitor is calculated based on the estimated value of the state variable and the second calculation formula, wherein the second calculation formula is used to characterize the relationship between the factory rated resistance and the estimated actual resistance of the equivalent series resistance in the instantaneous RC branch of the state variable and the SOH.

9. A method for equalizing a supercapacitor bank, characterized in that, The supercapacitor bank comprises multiple supercapacitors; the equalization method includes: According to any one of the supercapacitor parameter estimation methods of claims 1-8, the parameters of each supercapacitor in the supercapacitor group are obtained, wherein the parameters include SOE and SOH; Determine whether the supercapacitor bank needs balancing based on its voltage data. When the supercapacitor bank needs to be balanced, the supercapacitor bank is balanced according to the SOH and SOE of each supercapacitor in the supercapacitor bank.

10. The equalization method for a supercapacitor bank according to claim 9, characterized in that, Determining whether the supercapacitor bank needs balancing based on its voltage data includes: If the total system voltage of the supercapacitor bank is greater than a preset voltage value and the maximum voltage difference between the supercapacitors in the supercapacitor bank is greater than a preset voltage difference, it is determined that the supercapacitor bank needs to be balanced.

11. A parameter estimation device for a supercapacitor, characterized in that, include: An equivalent model establishment module is used to establish an equivalent circuit model of the supercapacitor. The equivalent circuit model includes parallel instantaneous RC branches, short-time RC branches, long-time RC branches, and a self-discharge resistance branch. The instantaneous RC branches, short-time RC branches, and long-time RC branches have different time constants. The instantaneous RC branch includes a variable capacitor, a fixed capacitor, and an equivalent series resistance. The equivalent series resistance and the fixed capacitor are connected in series, and the variable capacitor is connected in parallel with the fixed capacitor. The short-time RC branch includes a short-time equivalent resistance and a short-time capacitor connected in series. The long-time RC branch includes a long-time capacitor and a long-time equivalent resistance connected in series. The self-discharge resistance branch includes a self-discharge resistor. The state-space expression determination module is used to determine the state-space expression of the supercapacitor based on the equivalent circuit model, wherein the output quantity of the state-space expression is the terminal voltage, and the state variables in the state-space expression include the capacitance, resistance and voltage of each branch of the equivalent circuit model. An estimation module is used to estimate the state variables in the state-space expression using the unscented Kalman algorithm; The calculation module is used to calculate the parameters of the supercapacitor based on the estimated values ​​of the state variables, the parameters including SOE and SOH.

12. An equalization device for a supercapacitor bank, characterized in that, include: The parameter acquisition module is used to acquire the parameters of each supercapacitor in the supercapacitor group according to the parameter estimation method of any one of claims 1-8, wherein the parameters include SOE and SOH; The equalization judgment module is used to determine whether the supercapacitor group needs equalization based on the voltage data of the supercapacitor group. The equalization module is used to equalize the supercapacitor group according to the SOH and SOE of each supercapacitor in the supercapacitor group when equalization is required.

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