Electrochemical energy storage system model parameter quantitative characterization method and related system

Abstract

The invention belongs to the field of electrochemical energy storage, and discloses an electrochemical energy storage system model parameter quantitative characterization method and a related system.The method comprises the steps that joint identification is conducted on characterization parameters of an alternating current side and a direct current side, and a unified parameter framework capable of reflecting multi-domain interaction characteristics of an electrochemical energy storage system is established; and the physical significance of the parameters is not lost in the recursive updating process. According to the method, the subjective weighting method is combined with expert experience and mechanism knowledge, the objective weighting method utilizes data distribution and information entropy, the reliability and objectivity of actual operation data can be considered while theoretical consistency is guaranteed through fusion of the subjective weighting method and the objective weighting method, and the rationality and robustness of a parameter representation result are remarkably improved. According to the method, subjective and objective weights are integrated through the game theory thought, and the obtained final parameter weight can be adaptively adjusted under different operation conditions, so that the model has higher precision and generalization ability when reflecting the nonlinear and time-varying characteristics of the energy storage system.

Description

Technical Field

[0001] This invention belongs to the field of electrochemical energy storage, specifically relating to a method for quantitative characterization of model parameters of electrochemical energy storage systems and related systems. Background Technology

[0002] Electrochemical energy storage, as a crucial component of new power systems, has been widely applied in various scenarios such as peak shaving and valley filling, frequency regulation, and renewable energy grid integration, thanks to its advantages including fast response, high regulation accuracy, high energy density, and flexible installation. Especially with the increasing proportion of intermittent energy sources like wind and solar power, electrochemical energy storage is considered a key supporting technology for achieving source-grid-load coordination and stable power system operation. However, in complex grid environments, the AC and DC characteristics of electrochemical energy storage systems (including PCS and battery systems) significantly interact. When transient disturbances or faults occur on the AC side, the PCS's power output to the grid drops sharply or even stops, while the battery continues to output energy, leading to energy retention and a sharp voltage rise on the DC side, which can easily trigger protection actions or equipment damage. During this process, the PCS control system needs to complete response adjustments within milliseconds, and its dynamic characteristics are strongly coupled with the internal electrochemical behavior of the battery and the BMS control strategy. Because electrochemical energy storage systems involve multi-domain coupling of electrical, control, and electrochemical domains, existing research lacks a unified parameter quantification model, and the AC / DC coupling mechanism remains unclear, making it difficult to accurately characterize and regulate the system's dynamic characteristics and stability. Summary of the Invention

[0003] The purpose of this invention is to overcome the problem that when the above-mentioned energy storage power station encounters faults during grid connection testing or actual operation, it is impossible to carry out correct theoretical analysis and propose effective solutions. This invention provides a quantitative characterization method for model parameters of electrochemical energy storage systems and related systems.

[0004] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a method for quantitative characterization of model parameters of an electrochemical energy storage system, comprising the following steps: Obtain the AC-side and DC-side characterization parameters of the electrochemical energy storage system model; Parameter identification is performed on the AC side characterization parameters and DC side characterization parameters to obtain the coupling parameters whose physical meaning still holds during the recursive process; The subjective weighting method is used to assign weights to the coupling parameters to obtain the subjective weight coupling parameters; The coupling parameters are assigned weights using an objective weighting method to obtain objective weighted coupling parameters; By assigning comprehensive weights to the subjective weight coupling parameters and the objective weight coupling parameters, the required electrochemical energy storage system model parameters are obtained.

[0005] A further improvement of the present invention is that the AC side characterization parameters include filter impedance, line impedance before and after grid connection point, converter output voltage, and AC line current. DC-side characterization parameters include battery impedance, output voltage, and output current.

[0006] A further improvement of this invention lies in the following method for identifying the AC-side and DC-side characterization parameters to obtain the coupling parameters whose physical meaning still holds during the recursive process: Set the initial parameter vector for the least squares method as required, and initialize the covariance matrix; preset the forgetting factor, and construct the regression vector; The AC side characterization parameters and DC side characterization parameters are obtained, and the least squares method is used to identify the AC side characterization parameters and DC side characterization parameters. The AC side characterization parameters and DC side characterization parameters are then updated recursively by combining the forgetting factor and regression vector. The updated AC-side and DC-side characterization parameters are subjected to physical constraints and consistency checks. After passing the checks, the coupling parameters are output.

[0007] A further improvement of this invention lies in the use of a subjective weighting method to assign weights to the coupling parameters. The specific method for obtaining the subjective weighted coupling parameters is as follows: Based on the coupling structure of the electrochemical energy storage system, the factors affecting the coupling parameters are decomposed hierarchically, and a hierarchical analysis model consisting of a target layer, a criterion layer, and an index layer is established. The questionnaire was designed using a pairwise comparison approach, and the factors at the same level were compared in pairs to obtain the expert survey results. Based on the expert survey results, construct judgment matrices for each level and calculate the weight vectors of the judgment matrices. The weight vector of the judgment matrix is ​​subjected to a consistency check. After passing the check, the subjective weight vectors of the AC side characterization parameters and the DC side characterization parameters are output as subjective weight coupling parameters.

[0008] A further improvement of this invention lies in the use of an objective weighting method to assign weights to the coupling parameters. The specific method for obtaining the objective weighted coupling parameters is as follows: Construct an index matrix consisting of several samples and parameter indicators based on the coupling parameters; The index matrix is ​​standardized to obtain a dimensionless matrix; Calculate the index weight matrix based on the dimensionless matrix; Calculate the information entropy of each indicator based on its information entropy. Calculate the difference coefficient of each indicator based on the information entropy of each indicator; Calculate the objective weight of each indicator based on the difference coefficient of each indicator; By assigning objective weights to the coupling parameters, we obtain objective weighted coupling parameters.

[0009] A further improvement of this invention lies in assigning comprehensive weights to the subjective weight coupling parameters and the objective weight coupling parameters, and the specific method for obtaining the required electrochemical energy storage system model parameters is as follows: Obtain the subjective weight coupling parameters and the objective weight coupling parameters, and construct a comprehensive weight vector based on the subjective weight coupling parameters and the objective weight coupling parameters; A loss function is established with the goal of minimizing the deviation between subjective and objective weights; Minimize the loss function to obtain the optimal combination coefficients; The final comprehensive weight is obtained by combining the comprehensive weight vector with the optimal combination coefficients; The subjective weight coupling parameters and the objective weight coupling parameters are assigned a final comprehensive weight to obtain the required electrochemical energy storage system model parameters.

[0010] Secondly, the present invention provides a quantitative characterization system for model parameters of an electrochemical energy storage system, comprising the following steps: The parameter acquisition module is used to acquire the AC-side and DC-side characterization parameters of the electrochemical energy storage system model. The parameter identification module is used to identify the AC side characterization parameters and DC side characterization parameters to obtain the coupling parameters whose physical meaning still holds during the recursive process. The subjective weight coupling module is used to assign weights to the coupling parameters using a subjective weighting method to obtain the subjective weight coupling parameters. The objective weight coupling module is used to assign weights to the coupling parameters using an objective weighting method to obtain the objective weight coupling parameters. The integrated weight coupling module is used to assign integrated weights to the subjective weight coupling parameters and the objective weight coupling parameters to obtain the required electrochemical energy storage system model parameters.

[0011] A further improvement of the present invention is that the parameter acquisition module is used to collect the filter impedance, the line impedance before and after the grid connection point, the converter output voltage and the AC line current as AC side characterization parameters; and to collect the battery impedance, output voltage and output current as DC side characterization parameters.

[0012] A further improvement of this invention is that the function of the parameter recognition module is implemented through the following method: Set the initial parameter vector for the least squares method as required, and initialize the covariance matrix; preset the forgetting factor, and construct the regression vector; The AC side characterization parameters and DC side characterization parameters are obtained, and the least squares method is used to identify the AC side characterization parameters and DC side characterization parameters. The AC side characterization parameters and DC side characterization parameters are then updated recursively by combining the forgetting factor and regression vector. The updated AC-side and DC-side characterization parameters are subjected to physical constraints and consistency checks. After passing the checks, the coupling parameters are output.

[0013] A further improvement of this invention is that the function of the subjective weight coupling module is implemented through the following method: Based on the coupling structure of the electrochemical energy storage system, the factors affecting the coupling parameters are decomposed hierarchically, and a hierarchical analysis model consisting of a target layer, a criterion layer, and an index layer is established. The questionnaire was designed using a pairwise comparison approach, and the factors at the same level were compared in pairs to obtain the expert survey results. Based on the expert survey results, construct judgment matrices for each level and calculate the weight vectors of the judgment matrices. The weight vector of the judgment matrix is ​​subjected to a consistency check. After passing the check, the subjective weight vectors of the AC side characterization parameters and the DC side characterization parameters are output as subjective weight coupling parameters.

[0014] A further improvement of this invention is that the function of the objective weight coupling module is implemented through the following method: Construct an index matrix consisting of several samples and parameter indicators based on the coupling parameters; The index matrix is ​​standardized to obtain a dimensionless matrix; Calculate the index weight matrix based on the dimensionless matrix; Calculate the information entropy of each indicator based on its information entropy. Calculate the difference coefficient of each indicator based on the information entropy of each indicator; Calculate the objective weight of each indicator based on the difference coefficient of each indicator; By assigning objective weights to the coupling parameters, we obtain objective weighted coupling parameters.

[0015] A further improvement of this invention is that the function of the integrated weight coupling module is implemented through the following method: Obtain the subjective weight coupling parameters and the objective weight coupling parameters, and construct a comprehensive weight vector based on the subjective weight coupling parameters and the objective weight coupling parameters; A loss function is established with the goal of minimizing the deviation between subjective and objective weights; Minimize the loss function to obtain the optimal combination coefficients; The final comprehensive weight is obtained by combining the comprehensive weight vector with the optimal combination coefficients; The subjective weight coupling parameters and the objective weight coupling parameters are assigned a final comprehensive weight to obtain the required electrochemical energy storage system model parameters.

[0016] Thirdly, the present invention provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of a method for quantitative characterization of model parameters of an electrochemical energy storage system.

[0017] Fourthly, the present invention provides a storage medium storing a computer program thereon, wherein the computer program, when executed by a processor, implements the steps of a method for quantitative characterization of model parameters of an electrochemical energy storage system.

[0018] Compared with the prior art, the present invention has the following beneficial effects: This invention establishes a unified parameter framework that reflects the multi-domain interaction characteristics of electrochemical energy storage systems by jointly identifying AC and DC side characterization parameters, ensuring that the physical meaning of parameters is not lost during the recursive update process. The subjective weighting method combines expert experience and mechanistic knowledge, while the objective weighting method utilizes data distribution and information entropy. This fusion ensures theoretical consistency while also considering the reliability and objectivity of actual operating data, significantly improving the rationality and robustness of parameter characterization results. Furthermore, this invention integrates subjective and objective weights using game theory, resulting in final parameter weights that can adaptively adjust under different operating conditions, enabling the model to have higher accuracy and generalization ability when reflecting the nonlinear and time-varying characteristics of energy storage systems. In conclusion, this invention can quickly quantify the deviation and coupling change patterns of key parameters when faults occur during grid-connected testing or operation of energy storage power stations, providing accurate theoretical basis for fault diagnosis, operation optimization, and control strategy adjustment. This avoids misjudgments and ineffective regulation caused by model distortion, thereby improving the safety and reliability of energy storage systems under complex grid conditions. Attached Figure Description

[0019] Figure 1 This is a flowchart of the present invention; Figure 2 This is a system diagram of the present invention; Figure 3 Architecture diagram of the electrochemical energy storage system model in this invention; Figure 4 This is a system diagram for Example 9. Detailed Implementation

[0020] To further understand the content of this invention, the invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments are merely illustrative and not limiting of the invention.

[0021] Example 1: See Figure 1 A method for quantitative characterization of model parameters of an electrochemical energy storage system includes the following steps: S1, obtain the AC-side characterization parameters and DC-side characterization parameters of the electrochemical energy storage system model.

[0022] S2, perform parameter identification on AC side and DC side characterization parameters to obtain coupling parameters whose physical meaning still holds during the recursive process.

[0023] S3. The subjective weighting method is used to assign weights to the coupling parameters to obtain the subjective weight coupling parameters.

[0024] S4. The objective weighting method is used to assign weights to the coupling parameters to obtain the objective weighted coupling parameters.

[0025] S5 assigns comprehensive weights to the subjective weight coupling parameters and the objective weight coupling parameters to obtain the required electrochemical energy storage system model parameters.

[0026] Example 2: See Figure 2 A quantitative characterization system for model parameters of an electrochemical energy storage system includes the following steps: The parameter acquisition module is used to acquire the AC-side characterization parameters and DC-side characterization parameters of the electrochemical energy storage system model.

[0027] The parameter identification module is used to identify the AC side characterization parameters and DC side characterization parameters to obtain the coupling parameters whose physical meaning still holds during the recursive process.

[0028] The subjective weight coupling module is used to assign weights to the coupling parameters using a subjective weighting method to obtain the subjective weight coupling parameters.

[0029] The objective weight coupling module is used to assign weights to the coupling parameters using an objective weighting method to obtain objective weight coupling parameters.

[0030] The integrated weight coupling module is used to assign integrated weights to the subjective weight coupling parameters and the objective weight coupling parameters to obtain the required electrochemical energy storage system model parameters.

[0031] Example 3: This embodiment further explains the functions of step S1 and the parameter acquisition module as follows: The AC side characterization parameters are filter impedance, line impedance before and after the grid connection point, converter output voltage, and AC line current. The DC side characterization parameters are battery impedance, output voltage, and output current.

[0032] Filter impedance This refers to the electrical impedance presented at a specific frequency by the filter configured between the inverter output terminal and the grid interface of an energy storage system. It is primarily used to suppress high-frequency switching harmonics generated by the inverter, ensuring the quality of the grid-connected current. The magnitude and frequency characteristics of the filter impedance directly affect the AC side voltage and current waveforms, as well as the dynamic stability of the system, and are important parameters characterizing the grid-connected safety of energy storage systems.

[0033] Filter impedance The measurement method is as follows: when the power grid is not short-circuited, the frequency sweep injection method can be used, or the injection current can be measured and calculated under a known voltage. The calculation formula is:

[0034] Line impedance before grid connection point and line impedance after grid connection point In this context, the line impedance before and after the grid connection point refers to the equivalent impedance of the power lines before and after the point of common coupling (PCC) of the energy storage system. Its magnitude depends on the grid structure, line length, conductor parameters, and load distribution. The commonly used equivalent form for each line segment is:

[0035] In the formula, Indicates the line resistance; This represents the line inductance. Line impedance affects voltage distribution, current shunting, and system short-circuit capacity, significantly impacting the grid-connected stability, fault ride-through capability, and power regulation performance of energy storage devices. The method for measuring line impedance before and after the grid point involves using voltage / current pulse measurements and least-squares fitting to obtain the line resistance and inductance.

[0036] Converter output voltage This refers to the three-phase voltage output on the AC side of the energy storage system inverter through a control strategy. It is the driving source for the DC-side energy, after conversion, applied to the filter / line, determining the phase and amplitude of the output current, and reflecting the transmission characteristics of DC-side energy storage state to AC-side power regulation. Converter output voltage. The measurement method is to directly measure the phase-to-neutral point or phase-to-phase voltage, and then extract the fundamental amplitude phase through Fourier transform after sampling.

[0037] AC line current measurement This refers to the actual phase current flowing from the AC output terminal of the energy storage system to the grid connection point. It reflects the power transmission status, power quality level, and dynamic response characteristics of the energy storage system during grid-connected operation, and is one of the key indicators characterizing AC / DC coupling. AC line current measurement. The measurement method is to directly measure using a current transformer / Hall sensor, and then perform fundamental / harmonic decomposition after sampling.

[0038] DC side voltage This represents the terminal voltage of the battery cluster at the current SOC, temperature, and operating current. DC side voltage. The measurement method is high-frequency sampling by a voltage sensor.

[0039] DC side current The DC-side current flowing into / out of the converter can cause fluctuations in the DC-side bus voltage due to battery impedance, affecting converter control performance and power quality. DC-side current The measurement method is to use a current sensor for high-frequency sampling.

[0040] Battery impedance It is the complex impedance of the battery to small-signal AC disturbances, which is frequency-dependent and includes electrochemical processes such as ohmic internal resistance and interface / diffusion polarization. The magnitude of the battery impedance determines the amplitude-frequency response of the battery to AC and DC disturbances, and is obtained through parameter identification methods.

[0041] Example 4: This embodiment further explains the functions of step S2 and the parameter identification module as follows: Step 1: Set the initial parameter vector for the least squares method as required, and initialize the covariance matrix; preset the forgetting factor, and construct the regression vector.

[0042] Step 2: Obtain the AC side characterization parameters and DC side characterization parameters. Use the least squares method to identify the AC side characterization parameters and DC side characterization parameters. Combine the forgetting factor and regression vector to recursively update the AC side characterization parameters and DC side characterization parameters.

[0043] Step 3: Perform physical constraint and consistency checks on the recursively updated AC-side and DC-side characterization parameters, and output the coupling parameters after passing the checks.

[0044] Specifically: Recursive least squares is a recursive version of least squares, suitable for online parameter identification. It reduces computational burden by successively updating parameter estimates. The recursive formula is:

[0045] in, It is the gain matrix. It is a regression vector.

[0046] This embodiment is suitable for real-time applications and has high computational efficiency, but a forgetting factor needs to be selected to prevent outdated data from affecting the estimation.

[0047] Step 1: Based on the offline identification results of the least squares method, set the initial parameter vector:

[0048] And initialize the covariance matrix.

[0049] It is used to reflect the uncertainty of the initial parameters.

[0050] Step 2: Collect operational data from the energy storage system, including: DC-side input voltage sequence ; Energy storage system output current sequence ; Battery open-circuit voltage sequence .

[0051] These data serve as the input and regression basis for the recursive least squares algorithm.

[0052] Step 3: Select the forgetting factor This is used to balance the weights of historical and new data, enabling the model to gradually forget the old data in order to track the dynamic changes of parameters during operation.

[0053] Step 4: Construct the regression vector based on the system equations after bilinear transformation. This allows it to simultaneously reflect the coupling relationship between AC and DC side parameters, providing an input basis for parameter updates.

[0054] Step 5, at each sampling time Perform the following recursive steps: Calculate prediction error

[0055] Calculate the gain matrix

[0056] Update parameter estimation

[0057] Update covariance matrix

[0058] Step 6: After each parameter update, verify the physical meaning of the identified AC side parameters (such as equivalent impedance and transformation gain) and DC side parameters (such as battery internal resistance and voltage constant) to ensure that their value range conforms to the system characteristics and energy conservation relationship.

[0059] Step 7: After multiple recursive iterations, output the converged parameter set, which is the AC-DC coupling parameter with consistent physical meaning during the recursive process. It can be used for subsequent system state estimation and control strategy optimization.

[0060] Example 5: This embodiment further explains the functions of step S3 and the subjective weight coupling module as follows: Step 1: Based on the coupling structure of the electrochemical energy storage system, the factors affecting the coupling parameters are decomposed hierarchically, and a hierarchical analysis model consisting of a target layer, a criterion layer, and an index layer is established.

[0061] Step two: Design a questionnaire using a pairwise comparison method to compare each factor in the same level in pairs and obtain the expert survey results.

[0062] Step 3: Based on the expert survey results, construct the judgment matrix for each level and calculate the weight vector of the judgment matrix.

[0063] Step four: Perform a consistency check on the weight vector of the judgment matrix. If the check passes, output the subjective weight vectors of the AC side characterization parameters and the DC side characterization parameters as subjective weight coupling parameters.

[0064] Specifically: Step one: Based on the structure of the electrochemical energy storage system coupling model, the important factors affecting the coupling parameters are decomposed hierarchically, constructing a hierarchical analysis structure consisting of a target layer, a criterion layer, and an index layer. For example: Target layer: Determine the overall importance of coupling parameters; Criterion layer: Divide the influencing factors into two categories: AC side parameters and DC side parameters; The index layer contains specific parameters, such as AC impedance, conversion gain, DC internal resistance, and electrochemical time constant.

[0065] Step two involves designing a pairwise comparison questionnaire and inviting multiple domain experts (such as power electronics, energy storage control, and modeling and simulation experts) to compare each factor in the same level pairwise. A 1–9 scale is used to quantify relative importance; the scale's meaning is as follows:

[0066] Step 3: Based on the results of the expert questionnaire, construct a judgment matrix for each level of factors:

[0067] in Indicates the first The factor relative to the first The importance of each factor. The matrices satisfy a reciprocal relationship. .

[0068] Step 4: Analyze the judgment matrix. Calculate the geometric mean for each row:

[0069] The results are normalized to obtain the weights of each factor:

[0070] in, This refers to the subjective weights of each coupling parameter.

[0071] Step 5: To ensure consistency in expert judgments, calculate the largest eigenvalue of the judgment matrix. And based on this, a consistency index is derived:

[0072] Random consistency index obtained by table lookup (See the table below) Calculate the consistency ratio:

[0073] when If the condition is met, the judgment matrix passes the consistency test; otherwise, the expert judgment results need to be readjusted or the matrix needs to be corrected.

[0074]

[0075] Step six: After passing the consistency check, obtain the subjective weight vector of each AC-side and DC-side coupling parameter. .

[0076] Example 6: This embodiment further explains the functions of step S4 and the objective weight coupling module as follows: Step 1: Construct an index matrix consisting of several samples and parameter indices based on the coupling parameters.

[0077] Step two: Standardize the index matrix to obtain a dimensionless matrix.

[0078] Step 3: Calculate the index weight matrix based on the dimensionless matrix.

[0079] Step 4: Calculate the information entropy of each indicator based on its information entropy.

[0080] Step 5: Calculate the difference coefficient of each indicator based on the information entropy of each indicator.

[0081] Step 6: Calculate the objective weight of each indicator based on the difference coefficient of each indicator.

[0082] Step 7: Assign objective weights to the coupling parameters to obtain objective weighted coupling parameters.

[0083] Specifically: Step 1: Based on the AC and DC coupling parameters identified by the recursive least squares method, construct a system... Each sample (or working status) and An indicator matrix composed of several parameters

[0084] in, Indicates the first The sample at the th The matrix represents the values ​​of each coupling parameter under different operating conditions.

[0085] Step two: To eliminate the influence of the dimensions of different coupling parameters, the matrix is... Normalize.

[0086] Positive indicators (higher values ​​are better) are calculated using the following formula:

[0087] The negative indicator (the smaller the value, the better) is calculated using the following formula:

[0088] Interval-based indicators (optimal within a specific interval) use the following formula:

[0089] in, and Indicators The ideal interval is defined by the lower and upper bounds. Standardization yields a dimensionless matrix. .

[0090] Step 3, calculate the first... The first indicator The proportion of each sample:

[0091] This proportion reflects the sample In terms of indicators The relative performance below.

[0092] Step four, based on the principle of information entropy, calculate the first... Information entropy value of each indicator:

[0093] in, The larger the value, the more dispersed the information and the lower the distinguishability of the indicator.

[0094] Step 5, calculate the... Coefficient of variation for each indicator:

[0095] The larger the difference coefficient, the more obvious the data difference and the richer the information of the indicator.

[0096] Step six, normalize the difference coefficients to obtain the first... Objective weights of each indicator:

[0097] Weight This reflects the relative importance of each coupling parameter in terms of its information contribution, and satisfies... .

[0098] Step 7: Input the obtained objective weight vector Parameters are assigned to each AC and DC side to obtain objective weighted coupling parameters. These weighting results can be used to perform weighted fusion with subjective weights (AHP results) to form a comprehensive weighted coupling model, thereby reflecting the system's parameter sensitivity and information contribution under actual operating conditions.

[0099] Example 7: This embodiment further explains the functions of step S5 and the comprehensive weight coupling module as follows: Step 1: Obtain the subjective weight coupling parameters and the objective weight coupling parameters, and construct the comprehensive weight vector based on the subjective weight coupling parameters and the objective weight coupling parameters.

[0100] Step two: Establish a loss function with the goal of minimizing the deviation between subjective weights and objective weights.

[0101] Step 3: Minimize the loss function to obtain the optimal combination coefficients.

[0102] Step four: Combine the weight vector with the optimal combination coefficients to obtain the final comprehensive weight.

[0103] Step 5: Assign final comprehensive weights to the subjective weight coupling parameters and the objective weight coupling parameters to obtain the required electrochemical energy storage system model parameters.

[0104] Specifically: Step one: Denote the subjective weight vector obtained through the Analytic Hierarchy Process (AHP) as:

[0105] The objective weight vector obtained through the entropy weight method is denoted as:

[0106] Both reflect the relative importance of coupling parameters at the expert cognitive level and the data discrete level.

[0107] Step 2, let the overall weight vector be:

[0108] in, This is a combination coefficient used to balance the relative contributions of subjective and objective weights in the overall weight.

[0109] Step 3: To minimize the deviation between the overall weight and the subjective and objective weights, define the loss function:

[0110] By minimizing this loss function, the optimal combination coefficients can be obtained. This allows the overall weighting to take into account both subjective judgment and objective data distribution characteristics.

[0111] Step 4, adjust the loss function. Taking the partial derivative and setting it to zero, we get:

[0112] Therefore, the optimal combination coefficients can be obtained:

[0113] Its calculation expression can be written as:

[0114] Step 5: Calculate the optimal combination coefficients. Substituting into the combination model, we obtain the final comprehensive weight:

[0115] This comprehensive weight vector It incorporates both expert experience information on subjective weights and statistical distribution information on objective weights, making the weighting results more balanced and robust.

[0116] Step 6: Calculate the overall weight. Assign coupling parameters to the AC and DC sides to form a comprehensive weighted parameter vector:

[0117] in, This indicates element-wise multiplication. This is the original parameter vector identified by the recursive least squares method. The updated... As the final model parameters for the electrochemical energy storage system, they comprehensively reflect both data-driven and expert-perceived information while maintaining physical consistency.

[0118] Example 8: See Figure 3The grid-connected operation of an electrochemical energy storage system is essentially the seamless, safe, and controllable connection of an independent, variable-energy DC power source to a large, stable, and synchronized AC power grid. This complex energy interaction process relies on a power conversion system (PCS). The system features are as follows: (1) Controllable power flow according to commands: The system must be able to respond accurately and quickly to power commands from the energy management system (EMS) or the grid dispatch center, realize independent decoupling control of active and reactive power, and complete charging (absorbing energy from the grid) or discharging (injecting energy into the grid). (2) Strict synchronization between AC output and the grid: The AC power generated by the PCS must be highly consistent with the grid in terms of frequency, phase, and voltage amplitude to ensure that the grid-connected current is smooth and stable and will not cause impact on the grid or form an islanding effect. Its AC / DC side coupling parameters are introduced as follows: 1. Power Coupling: DC side power P dc :

[0119] The output voltage of the electrochemical cell system V dc and battery system output current I dc They were generated together.

[0120] AC power P ac : Including active power transmitted to the power grid P ac and reactive power Q ac Active power P ac :

[0121] The coupling relationship is represented by active power balance (ignoring losses):

[0122] in P loss It refers to the losses of equipment such as PCS and transformers.

[0123] Control logic: The power grid dispatching or local control system issues active power commands. P ref The instruction is sent to the PCS. Upon receiving the instruction, the PCS adjusts its internal control strategy (such as adjusting the IGBT's switching duty cycle) to control the current drawn from the DC side. I dc Size, thus changing Pdc To satisfy P ac The output requirements.

[0124] 2. Voltage coupling: The DC and AC voltages are interconnected through the modulation process of the PCS.

[0125] DC side voltage V dc Determined by the battery's state of charge (SOC) and operating status, it is a DC voltage that changes relatively slowly.

[0126] AC side voltage V ac The voltage amplitude and frequency of the power grid are usually determined by the power grid itself, and the PCS must be synchronized with it.

[0127] The coupling relationship can be represented as the PCS using techniques such as sinusoidal pulse width modulation (SPWM) or space vector pulse width modulation (SVPWM) to convert the DC voltage V... dc It is converted into a three-phase AC voltage. The fundamental amplitude of its output AC line voltage is... V acll With DC voltage V dc The following approximation relationship is satisfied:

[0128] in m It is the modulation ratio (0 < m ≤1).

[0129] 3. Frequency coupling / synchronization: This represents a strong constraint from the AC side to the DC side. AC side frequency. f ac The power grid frequency (e.g., 50Hz) is the cornerstone of the stable operation of the power grid.

[0130] The coupling relationship is represented by the PCS having a phase-locked loop (PLL) circuit inside, which monitors the phase and frequency of the grid voltage in real time; the frequency of the SPWM / SVPWM modulated wave generated by the PCS must strictly track the grid frequency locked by the PLL. f ac The switching frequency of the PCS (usually several thousand to tens of thousands of Hz) is much higher than the grid frequency. Its purpose is to efficiently complete the DC / AC conversion and filter out high-frequency harmonics. The energy on the DC side must be injected into the grid at a frequency and phase that is completely synchronized with the grid, without any deviation.

[0131] 4. Coupling of control commands: All changes in operating modes are achieved through control commands, which directly affect the coupling of parameters on both sides.

[0132] Run mode commands: Constant power / constant current charge / discharge mode: Given a reference value for active power. P ref (or current reference value) I ref PCS controls DC side current I dc To achieve this.

[0133] Constant voltage control mode (less commonly used for grid connection): Given a DC voltage reference value V dcref PCS regulates the active power absorbed from or supplied to the grid. P ac To maintain V dc Stablize.

[0134] Reactive power control mode: Given a reactive power reference value Q ref PCS outputs or absorbs reactive power by adjusting the phase of the modulated wave, without directly affecting DC-side parameters (except for a slight increase in device losses), only affecting AC-side current. I ac and voltage V ac .

[0135] 5. Protection command coupling: When DC side parameters are abnormal (e.g.) V dc Overvoltage / undervoltage I dc In the event of an overcurrent, the PCS will immediately activate (e.g., block the IGBT pulse), cutting off energy exchange with the grid, resulting in... P ac and I ac It plummeted to zero.

[0136] Conversely, when there is a fault on the grid side (such as...) V ac abnormal, f ac In case of an anomaly, the PCS will also immediately disconnect from the grid protectively, stopping energy extraction from the DC side, resulting in... I dc A sudden drop.

[0137] The following equations together constitute the AC / DC parameter weighted coupling control system described in this invention. The optimal values ​​of the unknown coefficients (A, B, C, D, α, β, γ, δ, κ, λ, η, ξ, μ, ν) can be determined by using optimization algorithms such as particle swarm optimization (PSO) and genetic algorithm (GA) based on a large amount of system simulation data.

[0138] 1. Generalized power flow equation: This equation describes the power flow and loss relationship from the DC side to the AC grid connection point. The equation is in the following form:

[0139] The total loss can be expanded as follows:

[0140] The unified equation after integration:

[0141] in, The active power (W) output at the grid connection point, DC-side input power (W), To determine the overall system conversion efficiency, the total power loss during the conversion process is quantified, ideally slightly less than 1. It is the battery internal resistance loss coefficient, which quantifies the power loss on the battery internal resistance. It is usually proportional to the square of the battery current and the internal resistance. The battery circuit current (A) is... The equivalent internal resistance of the battery (Ω) It is the grid-side line loss factor, which quantifies the power loss on the line impedance between the inverter output and the grid connection point. The AC side output current (A); The equivalent resistance (Ω) of the grid-side line; Fixed losses in the system include constant losses (W) that are independent of power level, such as switching losses, core losses, and control power consumption.

[0142] 2. Dynamic voltage modulation equation: This equation determines the inverter's modulation voltage command to ensure stable grid-connected operation under various conditions. The equation is in the form of:

[0143] Each component can be expanded as follows:

[0144]

[0145]

[0146] The unified equation after integration:

[0147] in, The inverter modulation voltage reference command (V); This is the DC voltage gain coefficient, which is the base value for converting the DC bus voltage into the AC modulation voltage; This represents the DC bus voltage (V). It is the impedance voltage drop compensation coefficient, used to actively compensate for the voltage drop caused by the output filter and line impedance, so as to maintain the stability of the grid connection point voltage; The AC side output current (A); The output filter impedance (Ω); The adaptive coefficient for grid impedance is a core parameter used to sense and adaptively compensate for the influence of grid impedance. The effect of this coefficient is weakened when the grid is strong (low impedance) and strengthened when the grid is weak (high impedance), thereby improving the robustness of the system. The line impedance (Ω) from the grid connection point to the power grid; This is the grid voltage synchronization coefficient, ensuring that the inverter output voltage is synchronized with the grid voltage, thus achieving seamless grid connection.

[0148] 3. Frequency-power coordinated synchronization equation: This equation defines the inverter output frequency. f inv It not only synchronizes with the grid frequency but can also be fine-tuned according to power interaction to provide support functions similar to primary frequency regulation.

[0149]

[0150] This is the power-frequency droop factor. When there is a deviation between the actual power and the commanded power, the output frequency is fine-tuned to promote power balance.

[0151] This is the power grid frequency change rate response coefficient. It enables proactive regulation (providing inertial response) to rapid changes in power grid frequency.

[0152] By using power deviation (Pcmd-Pac) and frequency change rate (dfgrid / dt) as inputs for frequency control, and by tuning coefficients κ and λ, electrochemical energy storage power stations can participate more intelligently in the regulation of grid frequency, rather than simply passively synchronizing.

[0153] 4. Equation for generating integrated control commands: This equation integrates all the above coupling relationships to generate the final current command sent to the PCS inner loop controller. Iref :

[0154] Example 9: See Figure 4 The present invention also provides an electronic device 100 for a method of quantitative characterization of model parameters of an electrochemical energy storage system; the electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on the at least one processor 102, and at least one communication bus 104.

[0155] The memory 101 can be used to store the computer program 103. The processor 102 implements the steps of the electrochemical energy storage system model parameter quantification characterization method described in Embodiment 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101. The memory 101 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the electronic device 100 (such as audio data), etc. In addition, the memory 101 may include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other non-volatile solid-state storage device.

[0156] The at least one processor 102 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 102 may be a microprocessor or any conventional processor. The processor 102 is the control center of the electronic device 100, connecting various parts of the electronic device 100 via various interfaces and lines.

[0157] The memory 101 in the electronic device 100 stores multiple instructions to implement a method for quantitative characterization of model parameters of an electrochemical energy storage system, and the processor 102 can execute the multiple instructions to achieve the following: Obtain the AC-side and DC-side characterization parameters of the electrochemical energy storage system model; Parameter identification is performed on the AC side characterization parameters and DC side characterization parameters to obtain the coupling parameters whose physical meaning still holds during the recursive process; The subjective weighting method is used to assign weights to the coupling parameters to obtain the subjective weight coupling parameters; The coupling parameters are assigned weights using an objective weighting method to obtain objective weighted coupling parameters; By assigning comprehensive weights to the subjective weight coupling parameters and the objective weight coupling parameters, the required electrochemical energy storage system model parameters are obtained.

[0158] Example 10: If the modules / units integrated in the electronic device 100 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0159] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0160] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0161] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0162] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0163] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for quantitative characterization of model parameters of an electrochemical energy storage system, characterized in that, Includes the following steps: Obtain the AC-side and DC-side characterization parameters of the electrochemical energy storage system model; Parameter identification is performed on the AC side characterization parameters and DC side characterization parameters to obtain the coupling parameters whose physical meaning still holds during the recursive process; The subjective weighting method is used to assign weights to the coupling parameters to obtain the subjective weight coupling parameters; The coupling parameters are assigned weights using an objective weighting method to obtain objective weighted coupling parameters; By assigning comprehensive weights to the subjective weight coupling parameters and the objective weight coupling parameters, the required electrochemical energy storage system model parameters are obtained.

2. The method for quantitative characterization of model parameters of an electrochemical energy storage system according to claim 1, characterized in that, The AC side characterization parameters include filter impedance, line impedance before and after grid connection, converter output voltage, and AC line current. DC-side characterization parameters include battery impedance, output voltage, and output current.

3. The method for quantitative characterization of model parameters of an electrochemical energy storage system according to claim 1, characterized in that, The method for identifying the AC-side and DC-side characterization parameters to obtain the coupling parameters whose physical meaning still holds during the recursive process is as follows: Set the initial parameter vector for the least squares method as required, and initialize the covariance matrix; preset the forgetting factor, and construct the regression vector; The AC side characterization parameters and DC side characterization parameters are obtained, and the least squares method is used to identify the AC side characterization parameters and DC side characterization parameters. The AC side characterization parameters and DC side characterization parameters are then updated recursively by combining the forgetting factor and regression vector. The updated AC-side and DC-side characterization parameters are subjected to physical constraints and consistency checks. After passing the checks, the coupling parameters are output.

4. The method for quantitative characterization of model parameters of an electrochemical energy storage system according to claim 1, characterized in that, The specific method for assigning weights to the coupling parameters using the subjective weighting method to obtain the subjective weighted coupling parameters is as follows: Based on the coupling structure of the electrochemical energy storage system, the factors affecting the coupling parameters are decomposed hierarchically, and a hierarchical analysis model consisting of a target layer, a criterion layer, and an index layer is established. The questionnaire was designed using a pairwise comparison approach, and the factors at the same level were compared in pairs to obtain the expert survey results. Based on the expert survey results, construct judgment matrices for each level and calculate the weight vectors of the judgment matrices. The weight vector of the judgment matrix is ​​subjected to a consistency check. After passing the check, the subjective weight vectors of the AC side characterization parameters and the DC side characterization parameters are output as subjective weight coupling parameters.

5. The method for quantitative characterization of model parameters of an electrochemical energy storage system according to claim 1, characterized in that, The specific method for assigning weights to coupling parameters using the objective weighting method to obtain objective weighted coupling parameters is as follows: Construct an index matrix consisting of several samples and parameter indicators based on the coupling parameters; The index matrix is ​​standardized to obtain a dimensionless matrix; Calculate the index weight matrix based on the dimensionless matrix; Calculate the information entropy of each indicator based on its information entropy. Calculate the difference coefficient of each indicator based on the information entropy of each indicator; Calculate the objective weight of each indicator based on the difference coefficient of each indicator; By assigning objective weights to the coupling parameters, we obtain objective weighted coupling parameters.

6. The method for quantitative characterization of model parameters of an electrochemical energy storage system according to claim 1, characterized in that, The specific method for assigning comprehensive weights to the subjective and objective weight coupling parameters to obtain the required electrochemical energy storage system model parameters is as follows: Obtain the subjective weight coupling parameters and the objective weight coupling parameters, and construct a comprehensive weight vector based on the subjective weight coupling parameters and the objective weight coupling parameters; A loss function is established with the goal of minimizing the deviation between subjective and objective weights; Minimize the loss function to obtain the optimal combination coefficients; The final comprehensive weight is obtained by combining the comprehensive weight vector with the optimal combination coefficients; The subjective weight coupling parameters and the objective weight coupling parameters are assigned a final comprehensive weight to obtain the required electrochemical energy storage system model parameters.

7. A quantitative characterization system for model parameters of an electrochemical energy storage system, characterized in that, Includes the following steps: The parameter acquisition module is used to acquire the AC-side and DC-side characterization parameters of the electrochemical energy storage system model. The parameter identification module is used to identify the AC side characterization parameters and DC side characterization parameters to obtain the coupling parameters whose physical meaning still holds during the recursive process. The subjective weight coupling module is used to assign weights to the coupling parameters using a subjective weighting method to obtain the subjective weight coupling parameters. The objective weight coupling module is used to assign weights to the coupling parameters using an objective weighting method to obtain the objective weight coupling parameters. The integrated weight coupling module is used to assign integrated weights to the subjective weight coupling parameters and the objective weight coupling parameters to obtain the required electrochemical energy storage system model parameters.

8. The electrochemical energy storage system model parameter quantification characterization system according to claim 7, characterized in that, The parameter acquisition module is used to collect filter impedance, line impedance before and after grid connection point, converter output voltage and AC line current as AC side characterization parameters; and to collect battery impedance, output voltage and output current as DC side characterization parameters.

9. The electrochemical energy storage system model parameter quantification characterization system according to claim 7, characterized in that, The parameter recognition module's functionality is implemented using the following methods: Set the initial parameter vector for the least squares method as required, and initialize the covariance matrix; preset the forgetting factor, and construct the regression vector; The AC side characterization parameters and DC side characterization parameters are obtained, and the least squares method is used to identify the AC side characterization parameters and DC side characterization parameters. The AC side characterization parameters and DC side characterization parameters are then updated recursively by combining the forgetting factor and regression vector. The updated AC-side and DC-side characterization parameters are subjected to physical constraints and consistency checks. After passing the checks, the coupling parameters are output.

10. The electrochemical energy storage system model parameter quantification characterization system according to claim 7, characterized in that, The functionality of the subjective weight coupling module is implemented through the following methods: Based on the coupling structure of the electrochemical energy storage system, the factors affecting the coupling parameters are decomposed hierarchically, and a hierarchical analysis model consisting of a target layer, a criterion layer, and an index layer is established. The questionnaire was designed using a pairwise comparison approach, and the factors at the same level were compared in pairs to obtain the expert survey results. Based on the expert survey results, construct judgment matrices for each level and calculate the weight vectors of the judgment matrices. The weight vector of the judgment matrix is ​​subjected to a consistency check. After passing the check, the subjective weight vectors of the AC side characterization parameters and the DC side characterization parameters are output as subjective weight coupling parameters.

11. The electrochemical energy storage system model parameter quantification characterization system according to claim 7, characterized in that, The objective weight coupling module's functionality is implemented through the following methods: Construct an index matrix consisting of several samples and parameter indicators based on the coupling parameters; The index matrix is ​​standardized to obtain a dimensionless matrix; Calculate the index weight matrix based on the dimensionless matrix; Calculate the information entropy of each indicator based on its information entropy. Calculate the difference coefficient of each indicator based on the information entropy of each indicator; Calculate the objective weight of each indicator based on the difference coefficient of each indicator; By assigning objective weights to the coupling parameters, we obtain objective weighted coupling parameters.

12. The electrochemical energy storage system model parameter quantification characterization system according to claim 7, characterized in that, The functionality of the integrated weight coupling module is implemented through the following methods: Obtain the subjective weight coupling parameters and the objective weight coupling parameters, and construct a comprehensive weight vector based on the subjective weight coupling parameters and the objective weight coupling parameters; A loss function is established with the goal of minimizing the deviation between subjective and objective weights; Minimize the loss function to obtain the optimal combination coefficients; The final comprehensive weight is obtained by combining the comprehensive weight vector with the optimal combination coefficients; The subjective weight coupling parameters and the objective weight coupling parameters are assigned a final comprehensive weight to obtain the required electrochemical energy storage system model parameters.

13. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method for quantitative characterization of model parameters of an electrochemical energy storage system according to any one of claims 1 to 6.

14. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method for quantitative characterization of model parameters of an electrochemical energy storage system as described in any one of claims 1 to 6.

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