Soc estimation method and device based on interactive multi-model, equipment and medium

Abstract

Embodiments of the present application disclose a SOC estimation method and device based on an interactive multi-model, equipment and a medium. The method comprises: obtaining actual operation parameters of an energy storage system, including real-time current data, voltage data and temperature data of a pole; determining a high-frequency equivalent model and a medium-low-frequency equivalent model of the energy storage system using the actual operation parameters and the charge-discharge hysteresis characteristics of the energy storage system; performing SOC estimation processing of the high-frequency equivalent model and the medium-low-frequency equivalent model using an unscented Kalman filter respectively, to determine a first SOC estimation result of the high-frequency equivalent model and a second SOC estimation result of the medium-low-frequency equivalent model at time k; and performing SOC fusion processing according to the first SOC estimation result, the second SOC estimation result and a preset interactive multi-model, to obtain a target SOC estimation result of the energy storage system at time k+1. In this way, the SOC estimation results of the equivalent models in two different frequency bands are fused to obtain the target SOC estimation result of the energy storage system, thereby improving the SOC estimation accuracy.

Description

TECHNICAL FIELD

[0001] The present application relates to the SOC estimation technology field of energy storage systems, and in particular to an SOC estimation method and device based on an interactive multi-model, equipment and a medium. BACKGROUND

[0002] With the continuous expansion of global wind power and photovoltaic and other new energy installations, new energy power generation is becoming higher and higher. However, the power output of new energy generation has defects such as uncertainty and volatility, and a large number of access will impact the operation of the power grid. Energy storage is one of the most effective and most economical ways to solve the randomness and volatility of new energy generation and maintain stable operation of the power grid.

[0003] In an energy storage system, electrochemical energy storage is widely used in new energy power generation bases due to its short construction period, flexible and convenient construction and other advantages. SOC, as the most important and basic parameter in the battery management system, is the control logic basis of the BMS. If the accurate estimation of SOC cannot be guaranteed, no matter how many protection functions are added to the BMS system, the stable operation of the battery cannot be guaranteed, and the purpose of prolonging the service life of the battery and maintaining the stability of the power grid cannot be achieved. Therefore, researching and finding an accurate and stable SOC estimation method is the primary task of daily maintenance and technical development of energy storage batteries.

[0004] In an energy storage system, high-frequency power electronic components are usually used to control energy flow, which will generate high-frequency noise and current ripple, causing the energy storage battery to be excited by high-frequency excitation when working. The existing SOC estimation method of the energy storage battery mainly uses a second-order RC equivalent circuit model. When high-frequency noise and current ripple exist, the second-order RC model cannot well reflect the internal reaction condition of the energy storage battery, thereby leading to inaccurate SOC estimation of the energy storage battery and causing serious consequences. The existing SOC estimation method of the energy storage battery also has an equivalent circuit model that considers high-frequency alone, but when the battery works under medium-low frequency excitation, the high-frequency equivalent circuit model cannot well adapt to medium-low frequency excitation, thereby leading to inaccurate SOC estimation of the energy storage battery and causing serious consequences.

[0005] Therefore, the existing SOC estimation method for the energy storage system has low estimation accuracy, and still needs to be improved. SUMMARY

[0006] The main purpose of the present application is to provide an SOC estimation method and device based on an interactive multi-model, equipment and a medium, which can solve the problem of low estimation accuracy of the SOC estimation method for the energy storage system in the prior art.

[0007] To achieve the above-mentioned purpose, the first aspect of the present application provides an SOC estimation method based on an interactive multi-model, which comprises:

[0008] acquire actual operation parameters of the energy storage system, the actual operation parameters at least including real-time current data, voltage data and temperature data of a pole;

[0009] establish an equivalent model of the energy storage system by using the current data, the voltage data, the temperature data and charge-discharge hysteresis characteristics of the energy storage system, determine a high-frequency equivalent model and a medium-low-frequency equivalent model of the energy storage system;

[0010] respectively perform SOC estimation processing of the high-frequency equivalent model and the medium-low-frequency equivalent model by using an unscented Kalman filter, determine a first SOC estimation result of the high-frequency equivalent model and a second SOC estimation result of the medium-low-frequency equivalent model at k time;

[0011] perform SOC fusion processing according to the first SOC estimation result, the second SOC estimation result and a preset interactive multiple model, obtain a target SOC estimation result of the energy storage system at k+1 time.

[0012] In a feasible implementation manner, the interactive multiple model includes an input interactive model, and the SOC fusion processing according to the first SOC estimation result, the second SOC estimation result and the preset interactive multiple model to obtain the target SOC estimation result of the energy storage system at k+1 time includes:

[0013] input the first SOC estimation result and the second SOC estimation result into the input interactive model, determine an input interactive result of the input interactive model, the input interactive result at least including a first model probability of the high-frequency equivalent model at k time, a third SOC estimation result after input interaction, a second model probability of the medium-low-frequency equivalent model at k time and a fourth SOC estimation result after input interaction;

[0014] obtain a fifth SOC estimation result of the high-frequency equivalent model at k+1 time, a sixth SOC estimation result of the medium-low-frequency equivalent model at k+1 time, a k+1 time residual and a k+1 time residual covariance by using the current data, the voltage data, the temperature data, the third SOC estimation result and the fourth SOC estimation result;

[0015] obtain the target SOC estimation result of the energy storage system at k+1 time according to the fifth SOC estimation result, the sixth SOC estimation result, the first model probability, the second model probability, the k+1 time residual and the k+1 time residual covariance.

[0016] In an implementation, the interactive multi-model further comprises an output interaction model, and the determining the target SOC estimation result of the energy storage system at the k+1 moment based on the fifth SOC estimation result, the sixth SOC estimation result, the first model probability, the second model probability, the k+1 moment residual and the k+1 moment residual covariance comprises:

[0017] determining the first likelihood function of the high-frequency equivalent model and the second likelihood function of the medium-low-frequency equivalent model at the k+1 moment based on the k+1 moment residual and the k+1 moment residual covariance;

[0018] determining the model mixing probability at the k+1 moment based on the first likelihood function and the second likelihood function, the first model probability, the second model probability and a preset mixing probability algorithm;

[0019] determining the third model probability of the high-frequency equivalent model and the fourth model probability of the medium-low-frequency equivalent model at the k+1 moment based on the first model probability, the second model probability, the first likelihood function, the second likelihood function and the model mixing probability at the k+1 moment;

[0020] determining the target SOC estimation result of the energy storage system at the k+1 moment based on the fifth SOC estimation result, the sixth SOC estimation result, the third model probability, the fourth model probability and the output interaction model.

[0021] In an implementation, the determining the target SOC estimation result of the energy storage system at the k+1 moment based on the fifth SOC estimation result, the sixth SOC estimation result, the third model probability, the fourth model probability and the output interaction model comprises:

[0022] inputting the fifth SOC estimation result, the sixth SOC estimation result, the third model probability and the fourth model probability into the output interaction model to determine the output interaction result at the k+1 moment;

[0023] determining the target SOC estimation result of the energy storage system at the k+1 moment based on the output interaction result at the k+1 moment and a preset SOC extraction algorithm.

[0024] In an implementation, the SOC estimation processing of performing the unscented Kalman filtering on the high-frequency equivalent model and the medium-low-frequency equivalent model respectively to determine the first SOC estimation result of the high-frequency equivalent model and the second SOC estimation result of the medium-low-frequency equivalent model comprises:

[0025] The high-frequency equivalent model and the low-frequency equivalent model are identified in real time by using a forgetting factor recursive least square, so as to determine first model parameters of the high-frequency equivalent model and second model parameters of the low-frequency equivalent model.

[0026] The first model parameters and the second model parameters are used to perform SOC estimation by using a Kalman filter, so as to determine a first SOC estimation result of the high-frequency equivalent model and a second SOC estimation result of the low-frequency equivalent model.

[0027] In a feasible implementation manner, the input interaction model comprises the following mathematical expression:

[0028]

[0029]

[0030]

[0031]

[0032] In the formula, n∈N, N represents a total number of state points x at the k+1 time, n is a state point identifier, m is a total number of equivalent models, is a SOC estimation result of the model j after input interaction at the k time, is a SOC estimation result of the equivalent model j at the k time; is a model probability of the equivalent model l at the k time, is a model probability of the equivalent model j at the k time, m is a total number of equivalent models, n∈N, N is a total number of state points x at the k time, n is a state point identifier, π lj is a probability of the model j switching to the model l, is a covariance of the SOC estimation result of the model j after input interaction at the k time, is a SOC estimation result of the equivalent model l at the k time, is a covariance of the SOC estimation result of the model l after input interaction at the k time.

[0033] In a feasible implementation manner, the output interaction model comprises the following mathematical expression:

[0034]

[0035]

[0036] In the formula, n∈N, N represents a total number of state points x at the k+1 time, n is a state point identifier, m is a total number of equivalent models, and P n (k+1|k+1) is an output interaction result at the k+1 time, SOC estimation result of the energy storage system at k+1 moment, P n (k+1|k+1) is the covariance of the state vector of the energy storage system at k+1 moment; is the SOC estimation result of the equivalent model j at k+1 moment, is the model probability of the equivalent model j at k+1 moment.

[0037] To achieve the above object, the second aspect of the present application provides a SOC estimation device based on interactive multiple models, which comprises:

[0038] A data acquisition module is configured to acquire actual operation parameters of the energy storage system, wherein the actual operation parameters at least include real-time current data, voltage data and temperature data of the pole;

[0039] A model determination module is configured to perform equivalent model establishment processing of the energy storage system by using the current data, voltage data, temperature data and charge-discharge hysteresis characteristics of the energy storage system, and determine high-frequency equivalent models and medium-low-frequency equivalent models of the energy storage system;

[0040] An SOC estimation module is configured to perform SOC estimation processing of the high-frequency equivalent models and the medium-low-frequency equivalent models by using unscented Kalman filtering respectively, and determine a first SOC estimation result of the high-frequency equivalent models and a second SOC estimation result of the medium-low-frequency equivalent models at k moment;

[0041] A result fusion module is configured to perform SOC fusion processing according to the first SOC estimation result, the second SOC estimation result and a preset interactive multiple model, and obtain a target SOC estimation result of the energy storage system at k+1 moment.

[0042] To achieve the above object, the third aspect of the present application provides a computer readable storage medium, which stores a computer program, wherein the computer program is executed by a processor to make the processor execute the steps shown in the first aspect and any feasible implementation manner.

[0043] To achieve the above object, the fourth aspect of the present application provides a computer device, which comprises a memory and a processor, wherein the memory stores a computer program, and the computer program is executed by the processor to make the processor execute the steps shown in the first aspect and any feasible implementation manner.

[0044] By adopting the embodiments of the present application, the following beneficial effects are achieved:

[0045] The application provides an SOC estimation method based on an interactive multi-model, which comprises the following steps: obtaining actual operation parameters of an energy storage system, wherein the actual operation parameters at least include real-time current data, voltage data and temperature data of a pole; establishing an equivalent model of the energy storage system by using the current data, the voltage data, the temperature data and the charge-discharge hysteresis characteristics of the energy storage system, determining a high-frequency equivalent model and a medium-low-frequency equivalent model of the energy storage system; performing SOC estimation processing on the high-frequency equivalent model and the medium-low-frequency equivalent model by using an unscented Kalman filter respectively, determining a first SOC estimation result of the high-frequency equivalent model at time k and a second SOC estimation result of the medium-low-frequency equivalent model; and performing SOC fusion processing according to the first SOC estimation result, the second SOC estimation result and a preset interactive multi-model, obtaining a target SOC estimation result of the energy storage system at time k+1. In this way, the SOC estimation results of the high-frequency equivalent model and the medium-low-frequency equivalent model can be fused to obtain the target SOC estimation result of the energy storage system, the influence of the frequency band on the SOC estimation result is effectively reduced, and the SOC estimation accuracy is improved. BRIEF DESCRIPTION OF DRAWINGS

[0046] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0047] Among them:

[0048] Figure 1 The flow chart of the SOC estimation method based on the interactive multi-model in the embodiment of the present application;

[0049] Figure 2 The experimental test current and open circuit voltage signal waveform diagram in the embodiment of the present application;

[0050] Figure 3 The high-frequency equivalent circuit model in the embodiment of the present application;

[0051] Figure 4 The medium-low-frequency equivalent circuit model in the embodiment of the present application;

[0052] Figure 5 The hysteresis characteristic diagram of the energy storage system charge and discharge in the embodiment of the present application;

[0053] Figure 6 The medium-low-frequency equivalent circuit interactive model parameter identification result in the embodiment of the present application;

[0054] Figure 7The high-frequency equivalent circuit interactive model parameter identification result in the embodiment of the present application;

[0055] Figure 8 The UKF filtering SOC estimation graph and error graph for the medium-low frequency equivalent circuit interactive model in the embodiment of the present application;

[0056] Figure 9 The UKF filtering SOC estimation graph and error graph for the high-frequency equivalent circuit interactive model in the embodiment of the present application;

[0057] Figure 10 Another flow chart of the SOC estimation method based on the interactive multi-model in the embodiment of the present application

[0058] Figure 11 The IMM-UKF system block diagram in the embodiment of the present application;

[0059] Figure 12 The battery SOC graph and error graph using the IMM-UKF in the embodiment of the present application;

[0060] Figure 13 The structure block diagram of the SOC estimation device based on the interactive multi-model in the embodiment of the present application;

[0061] Figure 14 The structure block diagram of the computer device in the embodiment of the present application. DETAILED DESCRIPTION

[0062] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the present application.

[0063] Please refer to Figure 1 , Figure 1 The flow chart of the SOC estimation method based on the interactive multi-model in the embodiment of the present application, as shown in the method comprises the following steps: Figure 1

[0064] 101, obtaining the actual operation parameters of the energy storage system, the actual operation parameters at least including real-time current data, voltage data and temperature data of the pole;

[0065] 102, using the current data, voltage data, temperature data and charge-discharge hysteresis characteristics of the energy storage system to establish the equivalent model of the energy storage system, determining the high-frequency equivalent model and the medium-low frequency equivalent model of the energy storage system;

[0066] ​It should be noted that the SOC estimation method based on the interactive multi-model shown in the present application is to calculate the real-time SOC of the energy storage system so as to dynamically master the SOC state of the energy storage system. First, in order to calculate the SOC of the energy storage system, an equivalent model of the energy storage system needs to be established. In the present application, two interactive models of equivalent circuits are established for the energy storage system according to different frequency bands to improve the accuracy of modeling. Specifically, the actual operating parameters of the energy storage system are obtained, which are used to reflect the actual operating conditions of the energy storage battery. The actual operating parameters at least include real-time current data, voltage data and temperature data of the pole, and the equivalent model of the energy storage system is established by using the current data, voltage data and temperature data of the pole in combination with the charge-discharge hysteresis characteristics of the energy storage system, to determine the high-frequency equivalent model and the medium-low frequency equivalent model of the energy storage system, wherein the energy storage system can be an energy storage battery or the like. The current data, voltage data and temperature data of the pole can be collected by corresponding sensor devices.

[0067] For example, the voltage and current of the energy storage battery can be collected by voltage and current collection devices, and the pole temperature of the energy storage battery can be collected by a temperature sensor.

[0068] For example, a certain type of lead-carbon battery with a rated capacity of 660Ah is taken as an experimental object, the battery rated voltage is 2V, the discharge cut-off voltage is 1.8V, and the maximum charging current is 300A. 0.1C discharge is adopted, the discharge current is 66A, and after 5% SOC discharge each time, the battery is statically placed for 10min. The battery is tested by using a blue battery test system, and the charge-discharge voltage and current data are collected. The temperature is measured by using a DS18B20 adhesive temperature sensor. Further, the collected current and open circuit voltage signal waveforms are as shown in Figure 2 Figure 2 The current and open circuit voltage signal waveform diagram for experimental test in the embodiment of the present application is shown.

[0069] Further, the high-frequency equivalent circuit interactive model considering the hysteresis characteristics of the energy storage battery and the medium-low frequency equivalent circuit interactive model considering the hysteresis characteristics of the energy storage battery are established according to the collected voltage, current fluctuation frequency and hysteresis characteristics of the battery charge-discharge, wherein the high-frequency equivalent circuit interactive model is also called high-frequency equivalent model, and the medium-low frequency equivalent circuit interactive model is also called medium-low frequency equivalent model. Please refer to Figure 3 and Figure 4 .

[0070] Among them, Figure 3 is the high-frequency equivalent circuit model in the embodiment of the present application, Figure 3 The high-frequency equivalent circuit interactive model considering the hysteresis characteristics of the present application is composed of a battery balance potential EMF and a hysteresis voltage U H to replace the open circuit voltage U​OCV(SOC，H，T) The internal resistance of the energy storage battery is replaced by a resistor R0, the high-frequency part of the equivalent circuit is replaced by an inductor L, and the other parts of the equivalent circuit are replaced by a resistor R1 and a capacitor C1 connected in parallel. In the interaction model of the high-frequency equivalent circuit considering hysteresis characteristics, its open-circuit voltage can be expressed as:

[0071] U OCV(SOC,H,T) =IR0+U L +U1+U0

[0072] In the formula, the open-circuit voltage U OCV(SOC，H，T) It can be represented as:

[0073] U OCV(SOC,H,T) =EMF+U H

[0074] In the formula, the hysteresis voltage U H The formula for calculating the equilibrium potential (EMF) is as follows:

[0075]

[0076] In the formula, η takes values ​​from 0.5 to 1, U charge U is the equilibrium terminal voltage of the battery during charging. discharge This is the equilibrium terminal voltage of the battery during discharge.

[0077] For example, see [link / reference] Figure 5 , Figure 5 This diagram illustrates the hysteresis characteristics of the energy storage system during charging and discharging in this embodiment of the invention. Figure 5 The charge / discharge hysteresis voltage curves are shown in the figure.

[0078] in, Figure 4 This is the low-to-medium frequency equivalent circuit model in the embodiments of the present invention. Figure 4 The interaction model of the mid-to-low frequency equivalent circuit considering hysteresis characteristics consists of the battery equilibrium potential EMF and the hysteresis voltage U. H To form a voltage source to replace the open-circuit voltage U of the battery OCV(SOC，H，T) The ohmic internal resistance of the energy storage battery is replaced by a resistor R0, and the low-to-mid-frequency portion of the equivalent circuit is replaced by a second-order RC circuit. In the interaction model of the low-to-mid-frequency equivalent circuit considering hysteresis characteristics, its open-circuit voltage can be expressed as:

[0079] U OCV(SOC,H,T) =IR0+U1+U2+U0

[0080] In the formula, the open-circuit voltage U OCV(SOC，H，T) It can be represented as:

[0081] U OCV(SOC,H,T) =EMF+U H

[0082] Wherein, the discrete state space equation can be established based on the mid-low frequency equivalent circuit interactive model considering the hysteresis characteristic as follows:

[0083]

[0084] τ1=R1C1

[0085] τ2=R2C2

[0086]

[0087] It should be noted that the above is only as an example to illustrate a possible establishment process of the high-frequency equivalent model and the low-frequency equivalent model of a certain type of battery, and does not limit the present application. In actual situations, the establishment of the high-frequency equivalent model and the low-frequency equivalent model of different types of batteries can be performed.

[0088] 103, respectively, the high-frequency equivalent model and the mid-low frequency equivalent model are subjected to unscented Kalman filtering SOC estimation processing, and the first SOC estimation result of the high-frequency equivalent model and the second SOC estimation result of the mid-low frequency equivalent model at time k are determined;

[0089] It should be noted that after the high-frequency equivalent model and the mid-low frequency equivalent model are established, SOC estimation processing needs to be performed. Specifically, the present application first performs SOC estimation on the two models. Specifically, the high-frequency equivalent model and the mid-low frequency equivalent model are subjected to unscented Kalman filtering SOC estimation processing by unscented Kalman filtering (UKF), and the first SOC estimation result of the high-frequency equivalent model and the second SOC estimation result of the mid-low frequency equivalent model at time k are determined. Wherein, the first SOC estimation result is the SOC estimation result of the high-frequency equivalent model at time k after unscented Kalman filtering, and the second SOC estimation result is the SOC estimation result of the mid-low frequency equivalent model at time k after unscented Kalman filtering. Wherein, unscented Kalman filtering is a combination of unscented transformation (UT) and standard Kalman filtering system, which makes the nonlinear system equation applicable to the standard Kalman filtering system under the linear assumption.

[0090] In a feasible implementation manner, step 103 includes steps A1-A2:

[0091] A1, real-time parameter identification is performed on the high-frequency equivalent model and the mid-low frequency equivalent model respectively by using a forgetting factor recursive least square, and the first model parameter of the high-frequency equivalent model and the second model parameter of the mid-low frequency equivalent model are determined;

[0092] Firstly, the forgetting factor recursive least square (FFRLS) is used to perform real-time parameter identification on the high-frequency equivalent circuit interactive model considering hysteresis characteristics and the low-frequency equivalent circuit interactive model considering hysteresis characteristics, to determine the first model parameters of the high-frequency equivalent model and the second model parameters of the low-frequency equivalent model.

[0093] For example, the identification algorithm for real-time parameter identification can refer to the following mathematical expression:

[0094]

[0095] In the formula, λ is the forgetting factor, generally 0.95-1 in the energy storage battery, θ k+1 is the parameter identification result at the current time k+1, θ k is the input of parameter identification, is the observation value of the energy storage system at the current time k+1, y(k+1) is the real feedback value of the energy storage system at the current time k+1, K(k+1) is the gain of the energy storage system at the current time k+1, P(k+1) is the covariance matrix of the energy storage system at the current time k+1, and E is the unit matrix. θ k+1 , K(k+1) and P(k+1) can be regarded as parameter identification results.

[0096] For example, refer to 6, Figure 6 is the parameter identification result of the low-frequency equivalent circuit interactive model in the embodiment of the application, Figure 6 The second model parameters of the low-frequency equivalent circuit interactive model identified are as follows: the resistance R0 fluctuates between 0.05-0.055Ω, the resistance R1 fluctuates between 0.055-0.065Ω, the resistance R2 fluctuates between 0.11-0.12Ω, the capacitance C1 fluctuates between 1.8-2.0F, and the capacitance C2 fluctuates between 4.5-5F.

[0097] For example, refer to 7, Figure 7 is the parameter identification result of the high-frequency equivalent circuit interactive model in the embodiment of the application, Figure 7 The first model parameters of the high-frequency equivalent circuit interactive model identified are as follows: the resistance R0 fluctuates between 0.01-0.015Ω, the resistance R1 fluctuates between 0.001-0.0012Ω, the inductance L fluctuates between 0.01-0.015H, and the capacitance C1 fluctuates between 40-45F.

[0098] A2, using the first model parameters and the second model parameters to perform SOC estimation processing of unscented Kalman filtering, to determine the first SOC estimation result of the high-frequency equivalent model and the second SOC estimation result of the low-frequency equivalent model.

[0099] Further, after determining the first model parameters of the high-frequency equivalent model and the second model parameters of the low-frequency equivalent model, the SOC of each of the two models can be estimated. The first SOC estimation result of the high-frequency equivalent model and the second SOC estimation result of the low-frequency equivalent model are determined by performing the SOC estimation process of the unscented Kalman filter using the first model parameters and the second model parameters.

[0100] For example, the step A2 specifically includes using the model parameters identified in the step A1 to perform the SOC estimation of the high-frequency equivalent circuit interactive model considering the hysteresis characteristic and the low-frequency equivalent circuit interactive model considering the hysteresis characteristic by using the UKF filter, i.e., using the first model parameters to perform the following filtering process on the high-frequency equivalent model and using the second model parameters to perform the following filtering process on the high-frequency equivalent model. The specific filtering steps are as follows:

[0101] First, the state equation and the observation equation of the system are determined by using the identified model parameters, wherein the state equation and the observation equation of the system can refer to the following formulas:

[0102]

[0103] In the formulas, X k is the state vector of the energy storage system at the time k, and Z k is the observation vector of the energy storage system at the time k, which can be obtained by using the identified model parameters such as resistance, capacitance, voltage, etc.

[0104] First step: initialize the state quantity and the covariance matrix.

[0105]

[0106] In the formulas, X is the initial state vector obtained by taking the expectation of the initial state vector X0 of the equivalent model at k=0, wherein the equivalent model is the high-frequency equivalent model or the low-frequency equivalent model, E[] represents taking the expectation of X0, P0 is the initial covariance matrix, and T represents the transpose of the matrix.

[0107] Second step: calculate the 2n+1 sigma points at the time k-1, i.e., the sigma sampling points, by using and P k-1 .

[0108]

[0109] In the formulas, X represents the i-th column of the matrix , and the total length is n, X represents the i-n-th column of the matrix , and the total length is 2n.

[0110] Third step: Calculate k time based on the second step of sampling points and P k / k-1 One step prediction model value, complete and P k / k-1 Update process.

[0111]

[0112]

[0113] In the formula: Q k-1 Noise variance matrix; The expected weight, The weight of variance.

[0114] Fourth step: Calculate the further prediction of 2n+1 sigma points at k time using and P k / k-1

[0115]

[0116] Fifth step: Calculate the mean and covariance matrix by weighting the fourth step prediction sigma point.

[0117]

[0118]

[0119] Sixth step: Calculate the UKF gain matrix.

[0120]

[0121] Seventh step: Finally, update the state of the battery and update the estimation covariance.

[0122]

[0123] In the formula, The SOC estimation result at k time, when the high-frequency equivalent model is filtered by the above-mentioned way, It can represent the first SOC estimation result at k time, when the medium and low frequency equivalent model is filtered by the above-mentioned way, It can represent the second SOC estimation result at k time, without limitation, P k That is Covariance. Among them, X k The state vector at k time, The filtered state vector at k time, X k Is the state matrix including SOC state and voltage state. ​

[0124] For example, the low and medium frequency equivalent circuit interactive model adopts the second model parameter and the test obtained voltage and current, and the waveform diagram and SOC estimation error obtained by adopting the UKF for SOC estimation are as shown in Figure 8 , Figure 8 For the low and medium frequency equivalent circuit interactive model in the embodiment of the application, the UKF filtering SOC estimation diagram and error diagram are adopted, and from Figure 8 It can be seen from the error diagram that the error of the low and medium frequency equivalent circuit interactive model is less than that of the ampere-hour integration method.

[0125] The high frequency equivalent circuit interactive model adopts the first model parameter and the test obtained voltage and current, and the waveform diagram and SOC estimation error obtained by adopting the UKF for SOC estimation are as shown in Figure 9 , Figure 9 For the high frequency equivalent circuit interactive model in the embodiment of the application, the UKF filtering SOC estimation diagram and error diagram are adopted, and from Figure 9 It can be seen from the error diagram that the error of the low and medium frequency equivalent circuit interactive model is less than that of the ampere-hour integration method.

[0126] 104. Perform SOC fusion processing according to the first SOC estimation result, the second SOC estimation result, and a preset interactive multiple model, to obtain a target SOC estimation result of the energy storage system at k+1 time.

[0127] Finally, the target SOC estimation result of the energy storage system at k+1 time can be obtained by fusing the first SOC estimation result at k time of the high frequency equivalent model and the second SOC estimation result at k time of the low and medium frequency equivalent model. Specifically, the target SOC estimation result of the energy storage system at k+1 time is obtained by performing SOC fusion processing according to the first SOC estimation result, the second SOC estimation result, and a preset interactive multiple model. Through the above method, the estimated SOC of the two equivalent models can be fused in real time, and real-time calculation of the SOC of the energy storage system is realized. Further, the main idea of the control algorithm of the interactive multiple model (IMM) is the automatic identification and switching between models based on the Bayesian theory: at any tracking time, the model filter corresponding to the possible number of target models is set to perform real-time maneuvering model detection, the weight coefficient and model update probability are set for each filter, and finally the current optimal estimation state is calculated by weighting, so as to achieve the purpose of adaptive tracking of the model. Therefore, the real-time SOC of the energy storage battery is calculated by fusing the two interactive models through the above interactive multiple model (IMM), which has higher calculation accuracy, better prolongs the service life of the battery, and better maintains the safe and stable operation of the power grid.

[0128] The application provides an SOC estimation method based on an interactive multi-model, which comprises the following steps: obtaining actual operation parameters of an energy storage system, wherein the actual operation parameters at least include real-time current data, voltage data and temperature data of a pole; establishing an equivalent model of the energy storage system by using the current data, the voltage data, the temperature data and charge-discharge hysteresis characteristics of the energy storage system, determining a high-frequency equivalent model and a medium-low-frequency equivalent model of the energy storage system; performing SOC estimation processing on the high-frequency equivalent model and the medium-low-frequency equivalent model by using an unscented Kalman filter respectively, determining a first SOC estimation result of the high-frequency equivalent model at a k moment and a second SOC estimation result of the medium-low-frequency equivalent model; and performing SOC fusion processing according to the first SOC estimation result, the second SOC estimation result and a preset interactive multi-model, obtaining a target SOC estimation result of the energy storage system at a k+1 moment. In this way, the SOC estimation results of the high-frequency equivalent model and the medium-low-frequency equivalent model can be fused to obtain the target SOC estimation result of the energy storage system, the influence of frequency bands on the SOC estimation result is effectively reduced, and the SOC estimation accuracy is improved.

[0129] Please refer to Figure 10 , Figure 10 Another flowchart of the SOC estimation method based on the interactive multi-model in the embodiment of the application is shown in Figure 10 The method comprises the following steps:

[0130] 1001, obtaining actual operation parameters of the energy storage system, wherein the actual operation parameters at least include real-time current data, voltage data and temperature data of a pole;

[0131] 1002, establishing an equivalent model of the energy storage system by using the current data, the voltage data, the temperature data and charge-discharge hysteresis characteristics of the energy storage system, determining a high-frequency equivalent model and a medium-low-frequency equivalent model of the energy storage system;

[0132] 1003, performing SOC estimation processing on the high-frequency equivalent model and the medium-low-frequency equivalent model by using an unscented Kalman filter respectively, determining a first SOC estimation result of the high-frequency equivalent model at a k moment and a second SOC estimation result of the medium-low-frequency equivalent model;

[0133] It should be noted that the steps 1001-1003 are similar to the contents of the steps 101-103 shown in Figure 1 To avoid repetition, the specific contents can be referred to the contents of the steps 101-103 shown in the foregoing Figure 1 .

[0134] It should be noted that the interactive multi-model includes an input interaction model and an output interaction model, and the first SOC estimation result and the second SOC estimation result at the k moment are fused and interacted through the input interaction model and the output interaction model, and finally the target SOC estimation result of the energy storage system at the k+1 moment is obtained.

[0135] Further, the SOC fusion processing according to the first SOC estimation result, the second SOC estimation result and the preset interactive multi-model to obtain the target SOC estimation result of the energy storage system at the k+1 moment can include the processing process of the input interaction model and the processing process of the output interaction model, and then can include steps 1004-1006, please refer to the following contents.

[0136] 1004, input the first SOC estimation result and the second SOC estimation result into the input interaction model, determine the input interaction result of the input interaction model, and the input interaction result at least includes the first model probability of the high-frequency equivalent model at the k moment, the third SOC estimation result after input interaction, the second model probability of the medium-low frequency equivalent model at the k moment, and the fourth SOC estimation result after input interaction;

[0137] Firstly, the first SOC estimation result of the high-frequency equivalent model at the k moment and the second SOC estimation result of the medium-low frequency equivalent model at the k moment are input interacted through the input interaction model to obtain the input interaction result. The input interaction model is to update the first SOC estimation result and the second SOC estimation result again to obtain the third SOC estimation result of the high-frequency equivalent model at the k moment after input interaction and the fourth SOC estimation result of the medium-low frequency equivalent model at the k moment after input interaction. Specifically, the first SOC estimation result and the second SOC estimation result are input into the input interaction model to determine the input interaction result of the input interaction model, and the input interaction result at least includes the first model probability of the high-frequency equivalent model at the k moment, the third SOC estimation result after input interaction, the second model probability of the medium-low frequency equivalent model at the k moment, and the fourth SOC estimation result after input interaction. The model probability is the probability of the equivalent model of the energy storage system at the k moment.

[0138] For example, the input interaction model includes the following mathematical expression:

[0139]

[0140]

[0141]

[0142]

[0143] is the SOC estimation result of the equivalent model j at time k after the input interaction of model j, is the SOC estimation result of the equivalent model j at time k after the input interaction of model j, is the SOC estimation result of the equivalent model j at time k after the input interaction of model j, is the model probability of the equivalent model j at time k, is the model probability of the equivalent model j at time k, lj is the model probability of the equivalent model j at time k, is the covariance of the SOC estimation result of the equivalent model j at time k after the input interaction of model j, is the SOC estimation result of the equivalent model j at time k after the input interaction of model j, is the covariance of the SOC estimation result of the equivalent model j at time k after the input interaction of model j,

[0144] 1005、Utilize the current data, voltage data, temperature data, third SOC estimation result and fourth SOC estimation result, get the predicted fifth SOC estimation result of high frequency equivalent model at time k+1, the sixth SOC estimation result of the medium and low frequency equivalent model, k+1 time residual and k+1 time residual covariance;

[0145] Further, utilize the current data, voltage data, temperature data, third SOC estimation result and fourth SOC estimation result, get the predicted fifth SOC estimation result of high frequency equivalent model at time k+1, the sixth SOC estimation result of the medium and low frequency equivalent model, k+1 time residual and k+1 time residual covariance, specifically, for equivalent model j=1, …m with the voltage, current, pole temperature, third SOC estimation result and fourth SOC estimation result of energy storage battery And covariance As input, UKF filtering is carried out, and the state and covariance of the next period k+1 are obtained And The estimation of the next period k+1 is obtained And The residual and its covariance are

[0146] 1006、According to the fifth SOC estimation result, the sixth SOC estimation result, the first model probability, the second model probability, the k+1 time residual and the k+1 time residual covariance, the target SOC estimation result of the energy storage system at time k+1 is obtained.

[0147] It should be noted that after obtaining the SOC estimation result at the k+1 moment, the target SOC estimation result of the energy storage system at the k+1 moment can be obtained according to the fifth SOC estimation result, the sixth SOC estimation result, the first model probability, the second model probability, the k+1 moment residual and the k+1 moment residual covariance. Wherein, the interactive multi-model further includes an output interaction model, and step 1006 includes B1-B4:

[0148] B1, the first likelihood function of the high-frequency equivalent model and the second likelihood function of the medium-low-frequency equivalent model at the k+1 moment are determined by using the k+1 moment residual and the k+1 moment residual covariance;

[0149] By obtaining the k+1 moment residual and the k+1 moment residual covariance, the likelihood function of each equivalent model at the k+1 moment can be obtained, wherein the first likelihood function is the likelihood function of the high-frequency equivalent model at the k+1 moment, and the second likelihood function is the likelihood function of the medium-low-frequency equivalent model at the k+1 moment.

[0150] For example, the likelihood function includes the following mathematical expression:

[0151] The likelihood function of the jth model can be expressed as:

[0152]

[0153] In the formula, N[·] represents a density function subject to a Gaussian distribution, is the k+1 moment residual, is the k+1 moment residual covariance, is the likelihood function of the equivalent model j at the k+1 moment.

[0154] B2, the model mixing probability at the k+1 moment is determined according to the first likelihood function and the second likelihood function, the first model probability, the second model probability, and a preset mixing probability algorithm;

[0155] B3, the third model probability of the high-frequency equivalent model and the fourth model probability of the medium-low-frequency equivalent model at the k+1 moment are determined by using the first model probability, the second model probability, the first likelihood function, the second likelihood function and the model mixing probability at the k+1 moment;

[0156] Further, the model mixing probability at the k+1 moment is determined according to the first likelihood function and the second likelihood function, the first model probability, the second model probability, and a preset mixing probability algorithm; the third model probability of the high-frequency equivalent model and the fourth model probability of the medium-low-frequency equivalent model at the k+1 moment are determined by using the first model probability, the second model probability, the first likelihood function, the second likelihood function and the model mixing probability at the k+1 moment.

[0157] The mixed probability of the model at time k+1 is calculated as follows:

[0158]

[0159]

[0160] wherein, is the model probability of the equivalent model j at time k+1, C n (k+1) is the mixed probability at time k+1.

[0161] B4, determining the target SOC estimation result of the energy storage system at time k+1 according to the fifth SOC estimation result, the sixth SOC estimation result, the third model probability, the fourth model probability, and the output interaction model.

[0162] Further, the target SOC estimation result of the energy storage system at time k+1 is obtained by inputting the calculation value at time k+1 into the output interaction model for fusion. That is, the target SOC estimation result of the energy storage system at time k+1 is determined according to the fifth SOC estimation result, the sixth SOC estimation result, the third model probability, the fourth model probability, and the output interaction model.

[0163] For example, the output interaction model includes the following mathematical expression:

[0164]

[0165]

[0166] wherein, n∈N, N represents the total number of state points x at time k+1, n is a state point identifier, and m is the total number of equivalent models, and P n (k+1|k+1) is the output interaction result at time k+1, represents the SOC estimation result of the energy storage system at time k+1, P n (k+1|k+1) is the covariance of the state vector of the energy storage system at time k+1; is the SOC estimation result of the equivalent model j at time k+1, is the model probability of the equivalent model j at time k+1.

[0167] Further, step B4 can include the following steps C1-C2:

[0168] C1, inputting the fifth SOC estimation result, the sixth SOC estimation result, the third model probability, the fourth model probability into the output interaction model to determine the output interaction result at time k+1;

[0169] C2, determining the target SOC estimation result of the energy storage system at the k+1 moment by using the output interaction result at the k+1 moment and a preset SOC extraction algorithm.

[0170] It should be noted that the output interaction result at the k+1 moment is obtained through the output interaction model, and the output interaction result at the k+1 moment includes the state variable at the k+1 moment and the covariance. The SOC state needs to be extracted from the state variable at the k+1 moment, so the target SOC estimation result of the energy storage system at the k+1 moment is determined by using the output interaction result at the k+1 moment and the preset SOC extraction algorithm.

[0171] Exemplarily, the SOC extraction algorithm includes:

[0172]

[0173] In the formula, is the state vector in the output interaction result at the k+1 moment. The state vector is a three-parameter matrix, so the target SOC estimation result SOC in the output interaction result can be obtained by performing matrix operation on [1 0 0] T and ksum

[0174] Exemplarily, reference can be made to Figure 11 , Figure 11 is the IMM-UKF system block diagram in the embodiment of the application, Figure 11 The processing process of the IMM model is illustrated in the embodiment, and the specific process is as follows:

[0175] First step: input interaction, that is, model condition initialization or re-initialization, to obtain the state vector and covariance matrix of each UKF filter input of the model at the current moment. For model j, the period is k:

[0176]

[0177]

[0178]

[0179]

[0180] In the formula, is the SOC estimation result of the input interaction of the model j at the k moment, is the SOC estimation result of the equivalent model j at the k moment; is the model probability of the equivalent model l at the k moment, ​Let be the model probability of the equivalent model j at time k, m be the total number of equivalent models, n∈N, N be the total number of state points x at time k, n be the state point identifier, and π. lj Let the probability of switching from model j to model l be . Input the covariance of the SOC estimation result at time k after the interaction into model j. This is the SOC estimation result at time k for the equivalent model l. The covariance of the SOC estimation result at time k after the interaction is input to model l.

[0181] Step 2: For the model j=1, ...N in Step 2, the voltage, current, and electrode temperature of the energy storage battery are considered. as well as The input is used for UKF filtering, utilizing the period k. and Obtain the state of the next period k+1 and an estimate of its covariance. and residual and its covariance

[0182] Step 3: Model Probability Update: The likelihood function of the j-th model can be expressed as:

[0183]

[0184] In the formula, N[·] represents the density function that follows a Gaussian distribution. The residual at time k+1, Let the residual covariance be at time k+1. Let be the likelihood function of the equivalent model j at time k+1.

[0185] The mixture probability at time k+1 is calculated as follows:

[0186]

[0187]

[0188] Step 4: Output interaction: Fuse the model with its corresponding probabilities to calculate the state estimate and covariance at time k+1.

[0189]

[0190]

[0191] Finally, the fused SOC is output:

[0192]

[0193] In this embodiment, the covariance matrix, model probability and state equation of the medium and low frequency equivalent circuit interaction model and the high frequency equivalent circuit interaction model are interacted by adopting IMM, and finally more accurate battery SOC estimation value and error are calculated, as shown in Figure 12 , Figure 12 The IMM-UKF battery SOC diagram and error diagram in the embodiment of the application are used from Figure 12 It can be seen from the IMM filtering error diagram that the error of the battery SOC estimation using the IMM filtering is the smallest and tends to be within 0.001, and the estimation effect is much better than that of the separate filtering estimation model.

[0194] The application provides an SOC estimation method based on an interactive multiple model, which has the following beneficial effects: the application provides an SOC estimation method based on an IMM-UKF energy storage battery different frequency band, which firstly models high frequency equivalent circuit and medium and low frequency equivalent circuit for the frequency of the energy storage battery work, inputs voltage, current and temperature data, and then uses UKF to perform high frequency equivalent circuit and medium and low frequency equivalent circuit SOC estimation, and then calculates the fused SOC through an interactive multiple model algorithm, so that the SOC estimation is more accurate, the battery service life is better prolonged, and the power grid safe and stable operation is better maintained. When the interactive model is established, the hysteresis characteristics of the open circuit voltage, the high frequency fluctuation of the energy storage battery in the power system due to the power electronic equipment, and the high frequency equivalent circuit interaction model considering the hysteresis characteristics and the medium and low frequency equivalent circuit interaction model considering the hysteresis characteristics are used when the interactive model is established, so that the model is simpler, the SOC estimation calculation amount and calculation time of a single model are greatly reduced compared with considering high frequency and low frequency mixed as one model, the SOC fusion is performed through IMM, and the fused SOC is more accurate

[0195] Please refer to Figure 13 , Figure 13 The structure block diagram of the SOC estimation device based on the interactive multiple model in the embodiment of the application is shown in Figure 13 The device comprises:

[0196] The data acquisition module 1301 is used to acquire the actual operation parameters of the energy storage system, and the actual operation parameters at least include real-time current data, voltage data and temperature data of the pole;

[0197] The model determination module 1302 is used to perform energy storage system equivalent model establishment processing by using the current data, voltage data, temperature data and the charge and discharge hysteresis characteristics of the energy storage system, and determine the high frequency equivalent model and the medium and low frequency equivalent model of the energy storage system;

[0198] The SOC estimation module 1303 is configured to perform SOC estimation processing on the high-frequency equivalent model and the low-to-medium frequency equivalent model respectively by using the unscented Kalman filter, and determine the first SOC estimation result of the high-frequency equivalent model and the second SOC estimation result of the low-to-medium frequency equivalent model at the k th moment.

[0199] The result fusion module 1304 is configured to perform SOC fusion processing according to the first SOC estimation result, the second SOC estimation result and a preset interactive multiple model, and obtain the target SOC estimation result of the energy storage system at the k+1 th moment.

[0200] It should be noted that, Figure 13 the functions of the modules in the device are similar to Figure 1 the contents of the steps in the method are similar to the contents of the steps in the method, and thus details are not repeated here, and the specific contents can be referred to Figure 1 the contents of the steps in the method.

[0201] The present application provides an SOC estimation method based on an interactive multiple model, which comprises the following steps: a data acquisition module is configured to acquire actual operation parameters of an energy storage system, wherein the actual operation parameters at least include real-time current data, voltage data and temperature data of a pole; a model determination module is configured to establish an equivalent model of the energy storage system by using the current data, the voltage data, the temperature data and the charge-discharge hysteresis characteristics of the energy storage system, and determine a high-frequency equivalent model and a low-to-medium frequency equivalent model of the energy storage system; an SOC estimation module is configured to perform SOC estimation processing on the high-frequency equivalent model and the low-to-medium frequency equivalent model respectively by using the unscented Kalman filter, and determine the first SOC estimation result of the high-frequency equivalent model and the second SOC estimation result of the low-to-medium frequency equivalent model at the k th moment; and a result fusion module is configured to perform SOC fusion processing according to the first SOC estimation result, the second SOC estimation result and a preset interactive multiple model, and obtain the target SOC estimation result of the energy storage system at the k+1 th moment. In this way, the SOC estimation results of the high-frequency equivalent model and the low-to-medium frequency equivalent model can be fused to obtain the target SOC estimation result of the energy storage system, so that the influence of the frequency band on the SOC estimation result is effectively reduced, and the SOC estimation accuracy is improved.

[0202] Figure 14 An internal structure diagram of a computer device in an embodiment is shown. The computer device can be a terminal or a server. As shown in Figure 14As shown, the computer device includes a processor, a memory and a network interface connected through a system bus. Among them, the memory includes a non-volatile storage medium and an internal memory. The non-volatile storage medium of the computer device stores an operating system, and can also store a computer program, which, when executed by the processor, can enable the processor to implement the above method. The internal memory can also store a computer program, which, when executed by the processor, can enable the processor to execute the above method. Those skilled in the art can understand that Figure 14 The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different component arrangement.

[0203] In one embodiment, a computer device is provided, comprising a memory and a processor, the memory storing a computer program, the computer program being executed by the processor to enable the processor to execute the steps of the method as Figure 1 or Figure 10 The method shown.

[0204] In one embodiment, a computer readable storage medium is provided, storing a computer program, the computer program being executed by a processor to enable the processor to execute the steps of the method as Figure 1 or Figure 10 The method shown.

[0205] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The program can be stored in a non-volatile computer readable storage medium, and when the program is executed, the processes of the above-mentioned embodiment methods can be included. Any reference to memory, storage, database, or other medium used in the embodiments provided in the present application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0206] Any combination of the technical features of the above embodiments can be made. In order to make the description simple, all possible combinations of the technical features in the above embodiments are not described, however, as long as the combination of the technical features does not exist, it should be considered as the scope of the present application.

[0207] The above embodiments only express several implementation manners of the present application, and the description is more specific and detailed, but it should not be understood as a limitation on the scope of the patent of the present application. It should be pointed out that for ordinary skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are all within the protection scope of the present application. Therefore, the protection scope of the patent of the present application should be subject to the appended claims.

Claims

1. A SOC estimation method based on an interactive multiple model, characterized in that, The method comprises: acquiring actual operation parameters of the energy storage system, the actual operation parameters at least including real-time current data, voltage data and temperature data of a pole; establishing an equivalent model of the energy storage system by using the current data, voltage data, temperature data and charge-discharge hysteresis characteristics of the energy storage system, and determining a high-frequency equivalent model and a medium-low-frequency equivalent model of the energy storage system; respectively, the high-frequency equivalent model and the low-frequency equivalent model are subjected to SOC estimation processing of the unscented Kalman filter to determine k the first SOC estimation result of the high-frequency equivalent model and the second SOC estimation result of the low-frequency equivalent model at the time According to the first SOC estimation result, the second SOC estimation result, and a preset interactive multiple model, SOC fusion processing is performed to obtain k a target SOC estimation result of the energy storage system at the first moment wherein the interactive multi-model includes an input interactive model, and the SOC fusion processing according to the first SOC estimation result, the second SOC estimation result and the preset interactive multi-model to obtain a target SOC estimation result of the energy storage system at k+1 time comprises: input the first SOC estimation result and the second SOC estimation result into the input interaction model to determine an input interaction result of the input interaction model, the input interaction result at least including the high-frequency equivalent model k the first model probability at the moment, the third SOC estimation result after input interaction, and the medium-low-frequency equivalent model k the second model probability at the moment, and the fourth SOC estimation result after input interaction The predicted SOC is obtained by using the current data, voltage data, temperature data, third SOC estimation result and fourth SOC estimation result k +1 time high-frequency equivalent model fifth SOC estimation result, the low-frequency equivalent model sixth SOC estimation result, k +1 time residual error and k +1 time residual error covariance; Based on the fifth SOC estimation result, the sixth SOC estimation result, the first model probability, and the second model probability, k +1 time residual and k +1 time residual covariance, we get k The target SOC estimation result of the energy storage system at time +1.

2. The method of claim 1, wherein, The interactive multi-model further comprises an output interaction model, and the target SOC estimation result of the energy storage system at the t+1 moment is obtained according to the fifth SOC estimation result, the sixth SOC estimation result, the first model probability, the second model probability, k the t+1 moment residual error and k the t+1 moment residual error covariance, and the target SOC estimation result of the energy storage system at the t+1 moment is obtained. k the t+1 moment residual error and k the t+1 moment residual error covariance, and the target SOC estimation result of the energy storage system at the t+1 moment is obtained. k the t+1 moment residual error and k the t+1 moment residual error covariance, and the target SOC estimation result of the energy storage system at the t+1 determined using the k +1 time residual and k +1 time residual covariance, determine k +1 time first likelihood function of the high frequency equivalent model and second likelihood function of the mid-low frequency equivalent model determining a first model probability and a second model probability according to the first likelihood function and the second likelihood function, and a preset mixing probability algorithm k the model mixing probability at the time point of +1; determining a third model probability of the high-frequency equivalent model and a fourth model probability of the mid-low-frequency equivalent model at time t+1 using the first model probability, the second model probability, the first likelihood function, the second likelihood function, and the k +1time model mixture probability k +1time the third model probability of the high-frequency equivalent model and the fourth model probability of the mid-low-frequency equivalent model According to the fifth SOC estimation result, the sixth SOC estimation result, the third model probability, the fourth model probability, and the output interaction model, determine k a target SOC estimation result of the energy storage system at the time point of +1.

3. The method of claim 2, wherein, The output interaction model is determined according to the fifth SOC estimation result, the sixth SOC estimation result, the third model probability, the fourth model probability and the output interaction model. k The target SOC estimation result of the energy storage system at the time t+1 includes: The fifth SOC estimation result, the sixth SOC estimation result, the third model probability, and the fourth model probability are input into the output interaction model to determine k the output interaction result at the time t+1; Utilize the output interaction result at the +1 moment and the preset SOC extraction algorithm to determine the target SOC estimation result of the energy storage system at the +1 moment. k +1 moment and the preset SOC extraction algorithm to determine the target SOC estimation result of the energy storage system at the +1 moment. k +1 moment and the preset SOC extraction algorithm to determine the target SOC estimation result of the energy storage system at the +1 moment.

4. The method of claim 1, wherein, the SOC estimation processing of the high-frequency equivalent model and the medium-low-frequency equivalent model by using the unscented Kalman filter to determine a first SOC estimation result of the high-frequency equivalent model and a second SOC estimation result of the medium-low-frequency equivalent model comprises: real-time parameter identification of the high-frequency equivalent model and the medium-low-frequency equivalent model by using the forgetting factor recursive least square to determine a first model parameter of the high-frequency equivalent model and a second model parameter of the medium-low-frequency equivalent model; the SOC estimation processing of the high-frequency equivalent model and the medium-low-frequency equivalent model by using the unscented Kalman filter to determine a first SOC estimation result of the high-frequency equivalent model and a second SOC estimation result of the medium-low-frequency equivalent model.

5. The method of claim 1, wherein, The input interactive model comprises the following mathematical expression: In the formula, For the model j Input interaction k SOC estimation results at time 10:00 For equivalent model i of k SOC estimation results at time step; for k Time-equivalent model l The model probability, for k Time-equivalent model j The model probability, m The total number of equivalent models. n ∈ N , N for k State point at time x The total number, n For status point identification, For the model j Switch to model l The probability, = , For the model j Input interaction k The covariance of the SOC estimation results at time t. For equivalent model l of k Moment SOC Estimation results For the model l Input interaction k The covariance of the SOC estimation results at time t.

6. The method of claim 2, wherein, The output interactive model comprises the following mathematical expression: In the formula, n ∈ N , N represent k State point at time +1 x The total number, n For status point identification, m The total number of equivalent models, as well as for k The output interaction result at time +1 represent k The SOC estimation result of the energy storage system at time +1 for k The covariance of the state vector of the energy storage system at time +1; For equivalent model j of k SOC estimation results at time +1 for k Equivalent model at time +1 j The model probability.

7. An interactive multiple model based SOC estimation apparatus, characterized by, The device comprises: a data acquisition module for acquiring actual operation parameters of the energy storage system, the actual operation parameters at least including real-time current data, voltage data and temperature data of a pole; a model determination module for establishing an equivalent model of the energy storage system by using the current data, voltage data, temperature data and charge-discharge hysteresis characteristics of the energy storage system, and determining a high-frequency equivalent model and a medium-low-frequency equivalent model of the energy storage system; The SOC estimation module is configured to perform the SOC estimation process of the high-frequency equivalent model and the low-frequency equivalent model respectively by using the unscented Kalman filter, and determine the first SOC estimation result of the high-frequency equivalent model and the second SOC estimation result of the low-frequency equivalent model at the moment t. k The SOC estimation module is configured to perform the SOC estimation process of the high-frequency equivalent model and the low-frequency equivalent model respectively by using the unscented Kalman filter, and determine the first SOC estimation result of the high-frequency equivalent model and the second SOC estimation result of the low-frequency equivalent model at the moment t. The result fusion module is used to perform SOC fusion processing based on the first SOC estimation result, the second SOC estimation result, and a preset interactive multi-model to obtain... k The target SOC estimation result of the energy storage system at time +1; The interactive multi-model includes an input interaction model, and the result fusion module is specifically configured to: input the first SOC estimation result and the second SOC estimation result into the input interaction model, determine an input interaction result of the input interaction model, and the input interaction result at least includes the high-frequency equivalent model k the first model probability at the moment, the third SOC estimation result after input interaction, the medium-low-frequency equivalent model k the second model probability at the moment, the fourth SOC estimation result after input interaction; and obtain the predicted k the fifth SOC estimation result of the high-frequency equivalent model at the moment +1, the sixth SOC estimation result of the medium-low-frequency equivalent model, k the residual error at the moment +1, and k the residual error covariance at the moment +1; and obtain k the target SOC estimation result of the energy storage system at the moment +1 according to the fifth SOC estimation result, the sixth SOC estimation result, the first model probability, the second model probability, k the residual error at the moment +1, and k the residual error covariance at the moment +1.

8. A computer readable storage medium storing a computer program, characterized in that, The computer program is executed by the processor to make the processor execute the steps of the method of any one of claims 1 to 6. 9.A computer device, comprising a memory and a processor, and characterized in that, The memory stores a computer program, and the computer program is executed by the processor to make the processor execute the steps of the method of any one of claims 1 to 6.

Images