CN115935206A - SOC estimation method and device of energy storage system, equipment and storage medium - Google Patents
SOC estimation method and device of energy storage system, equipment and storage medium Download PDFInfo
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Abstract
The embodiment of the invention discloses a method, a device, equipment and a storage medium for estimating the SOC of an energy storage system, wherein the method comprises the following steps: determining an interactive model of a high-frequency equivalent circuit and an interactive model of a medium-low frequency equivalent circuit of the energy storage system based on the charging and discharging characteristics of the energy storage system and the collected current data, voltage data and temperature data of the energy storage system, wherein the charging and discharging characteristics at least comprise the charging and discharging hysteresis characteristics of the energy storage system; respectively carrying out particle filtering processing on the interaction models, and determining the estimation SOC of each interaction model; and performing SOC fusion processing by using the estimated SOC of each interactive model and a preset interactive multi-model to determine the target SOC of the energy storage system. By the method, the interaction models of the high-frequency equivalent circuit and the medium-low frequency equivalent circuit of the energy storage system are respectively established, the estimated SOC of each interaction model is respectively obtained by utilizing particle filtering, and the estimated SOC is fused through the interactive multi-model, so that the obtained target SOC of the energy storage system is more accurate.
Description
Technical Field
The invention relates to the technical field of battery SOC, in particular to a method, a device, equipment and a storage medium for estimating the SOC of an energy storage system.
Background
High frequency power electronics, which are found anywhere in the power system, can generate high frequency noise and current ripples. However, the energy storage electrochemical cell used in the power system will also be affected by high frequency noise and current ripple, which will result in inaccurate State of charge (SOC) estimation of the energy storage cell. Inaccurate Battery SOC estimation will cause errors in the control logic of a Battery Management System (BMS), which may seriously cause instability of a power grid System, large-area power failure, and huge economic loss.
The existing SOC estimation method of the energy storage battery mainly takes a second-order RC equivalent circuit model as a main part. When high-frequency noise and current ripples exist, the internal reaction condition of the energy storage battery cannot be well reflected by the second-order RC model, so that the SOC estimation of the energy storage battery is inaccurate, and serious consequences are caused. The existing energy storage battery SOC estimation also has an equivalent circuit model for dealing with high frequency, but when the battery works in low-medium frequency excitation, the high-frequency equivalent circuit model cannot well adapt to the low-medium frequency excitation, so that the energy storage battery SOC estimation is inaccurate, and serious consequences are caused.
Moreover, when the SOC of the battery is estimated, the traditional Kalman filtering has good adaptability to a linear system, and the adaptability to a nonlinear system for estimating the SOC of the battery is poor.
Therefore, the prior art still lacks a way to improve the accuracy of SOC estimation.
Disclosure of Invention
The invention mainly aims to provide a method and a device for estimating the SOC of an energy storage system, equipment and a storage medium, which can solve the problem of low SOC estimation accuracy in the prior art.
To achieve the above object, a first aspect of the present invention provides a method for estimating SOC of an energy storage system, the method including:
determining an interaction model of a high-frequency equivalent circuit and an interaction model of a medium-low frequency equivalent circuit of the energy storage system based on the charging and discharging characteristics of the energy storage system and the collected current data, voltage data and temperature data of the energy storage system, wherein the charging and discharging characteristics at least comprise the charging and discharging hysteresis characteristics of the energy storage system;
respectively carrying out particle filtering processing on the interaction models, and determining the estimation SOC of each interaction model;
and performing SOC fusion processing by using the estimated SOC of each interactive model and a preset interactive multi-model to determine the target SOC of the energy storage system.
In a possible implementation manner, the performing particle filtering processing on the interaction models respectively to determine an estimated SOC of each interaction model includes:
respectively carrying out parameter identification on the interactive models by utilizing the current data, the voltage data, the temperature data and a preset forgetting factor recursive least square algorithm, and determining a parameter identification result of each interactive model, wherein the parameter identification result at least comprises model parameters of the interactive models;
and respectively carrying out particle filtering processing on the interaction models by using the model parameters of the interaction models, and determining the estimated SOC of each interaction model.
In a possible implementation manner, the performing, by using the model parameters of the interaction models, particle filtering on the interaction models respectively to determine the estimated SOC of each interaction model includes:
determining a state equation and an observation equation of the energy storage system by using the model parameters of the interaction model;
for each interaction model, at an initialization time k =0, performing particle sampling based on prior probability of a state equation of the energy storage system, and determining N state particles at the initialization time, wherein the weight of the N state particles at each initialization time is 1/N; let k = k +1, enter the iterative process of particle filtering;
taking the system state transition probability density at the moment k as the importance probability density at the moment k, and sampling particles at the moment k by using the importance probability density at the moment k to obtain N state particles at the moment k;
determining the update weight values of the N state particles at the k moment by using the observed values of the observation equations of the N state particles at the k moment and a preset weight value update algorithm;
resampling at the k moment by using the updated weight and a preset resampling threshold, and determining filtered state particles at the k moment;
and determining a state estimation value updated by the interactive model at the k moment according to the filtered state particles at the k moment and a preset state updating algorithm, wherein the state estimation value updated by the k moment at least comprises an estimated SOC of the interactive model at the k moment, enabling k = k +1, returning to execute the step of taking the system state transition probability density at the k moment as the importance probability density at the k moment, and performing particle sampling at the k moment by using the importance probability density at the k moment to obtain N state particles at the k moment until particle filtering at all moments is completed.
In a possible implementation manner, the determining a target SOC state of the energy storage system by performing SOC fusion processing using the estimated SOC state of each interactive model and a preset interactive multi-model includes:
inputting the updated state estimation value and covariance estimation value of each interactive model at the moment k into a preset interactive multi-model, and determining the state estimation value and covariance estimation value of the energy storage system at the moment k;
respectively carrying out particle filtering on each interactive model by using the current data, the voltage data, the temperature data, the state estimation value and the covariance estimation value of the energy storage system at the moment k, and estimating the state estimation value, the covariance estimation value, the residual error estimation value and the residual covariance estimation value of each interactive model at the moment k + 1;
determining the likelihood function of each interactive model at the k +1 moment by using the residual estimation value, the residual covariance estimation value and a preset density function which obeys Gaussian distribution;
determining the model probability of each interactive model at the time k +1 by using the likelihood function at the time k +1 and a preset model probability algorithm;
performing fusion processing according to the model probability and the state estimation value and covariance estimation value at the moment k +1 to determine a target state estimation value of the energy storage system at the moment k + 1;
and determining the target SOC of the energy storage system by using the target state estimation value and a preset SOC extraction algorithm.
In one possible implementation, the parameter identification result includes the following mathematical expression:
wherein λ is forgetting factor, θ k+1 Is the parameter identification result of the current time k +1, theta k For the purpose of inputting the parameter identification,
the measured value is an observed value of the energy storage system at the current moment K +1, y (K + 1) is a real feedback value of the energy storage system at the current moment K +1, K (K + 1) is a gain of the energy storage system at the current moment K +1, P (K + 1) is a covariance matrix of the energy storage system at the current moment K +1, and E is an identity matrix.
In one possible implementation, the state updating algorithm includes the following mathematical expression:
in the formula (I), the compound is shown in the specification,
for an updated state estimate at time k>
A covariance estimate updated for time k; n is the total number of state particles; />
The ith state particle at the moment k; />
The weight of the ith state particle at time k.
In a possible implementation manner, the target state estimation value and the target covariance estimation value of the energy storage system at the time k +1 include the following mathematical expressions:
in the formula (I), the compound is shown in the specification,
the target state estimation value of the energy storage system at the moment k +1 is obtained; p n (k + 1) is a target covariance estimation value of the energy storage system at the moment of k + 1; m is the total number of interaction models, in>
The model probability of the interaction model j at the moment k + 1; n is the number of particles of the particle filter; />
The state estimation value of the model j at the moment k + 1;
the SOC extraction algorithm includes the following mathematical expression:
in the formula, SOC ksum To be a target SOC for the energy storage system,
and the estimated value of the target state of the energy storage system at the moment k +1 is obtained.
To achieve the above object, a second aspect of the present invention provides an SOC estimation apparatus of an energy storage system, the apparatus including:
a model determination module: the energy storage system interaction model determining method comprises the steps that an interaction model of a high-frequency equivalent circuit and an interaction model of a medium-low frequency equivalent circuit of the energy storage system are determined based on the charging and discharging characteristics of the energy storage system and collected current data, voltage data and temperature data of the energy storage system, and the charging and discharging characteristics at least comprise the charging and discharging hysteresis characteristics of the energy storage system;
an SOC estimation module: the device is used for respectively carrying out particle filtering processing on the interaction models and determining the estimation SOC of each interaction model;
an SOC fusion module: and the system is used for performing SOC fusion processing by utilizing the estimated SOC of each interactive model and a preset interactive multi-model to determine the target SOC of the energy storage system.
To achieve the above object, a third aspect of the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps as shown in the first aspect or any possible implementation manner.
To achieve the above object, a fourth aspect of the present invention provides a computer device, comprising a memory and a processor, the memory storing a computer program, the computer program, when executed by the processor, causing the processor to perform the steps as shown in the first aspect or any possible implementation manner.
By adopting the embodiment of the invention, the following beneficial effects are achieved:
the invention provides an SOC estimation method of an energy storage system, which comprises the following steps: determining an interaction model of a high-frequency equivalent circuit and an interaction model of a medium-low frequency equivalent circuit of the energy storage system based on the charging and discharging characteristics of the energy storage system and the acquired current data, voltage data and temperature data of the energy storage system, wherein the charging and discharging characteristics at least comprise the hysteresis characteristics of the charging and discharging of the energy storage system; respectively carrying out particle filter processing on the interactive models to determine the estimated SOC of each interactive model; and performing SOC fusion processing by using the estimated SOC of each interactive model and the preset interactive multi-model to determine the target SOC of the energy storage system.
According to the mode, firstly, high-frequency equivalent circuit modeling and middle-low frequency equivalent circuit modeling are respectively carried out according to the working frequency of the energy storage battery, so that the established model accords with the real charging and discharging characteristics of the battery, SOC calculation errors caused by frequency band changes of high frequency and middle-low frequency are reduced, then, the particle filtering is adopted to carry out SOC estimation on the high-frequency equivalent circuit and the middle-low frequency equivalent circuit, and the integrated SOC is calculated through an interactive multi-model algorithm.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Wherein:
FIG. 1 is a flow chart of a method for estimating SOC of an energy storage system according to an embodiment of the present invention;
FIG. 2 is a waveform diagram of an experimental test current and open circuit voltage signal in an embodiment of the present invention;
FIG. 3 (a) is a diagram of a high frequency equivalent circuit model particle filter according to an embodiment of the present invention;
FIG. 3 (b) is a particle filter diagram of a low frequency equivalent circuit model according to an embodiment of the present invention;
FIG. 4 is a graph illustrating hysteresis characteristics of charging and discharging an energy storage battery according to an embodiment of the present invention;
FIG. 5 is another flow chart of a method for estimating SOC of an energy storage system according to an embodiment of the invention;
FIG. 6 (a) is a parameter identification result of an interaction model of a low frequency equivalent circuit according to an embodiment of the present invention;
FIG. 6 (b) is a parameter identification result of an interaction model of a high frequency equivalent circuit according to an embodiment of the present invention;
fig. 7 (a) is a diagram illustrating a low frequency equivalent circuit interaction model using a Particle Filter (PF) SOC estimation diagram and an error diagram according to an embodiment of the present invention;
FIG. 7 (b) is a diagram illustrating a Particle Filter (PF) SOC estimation diagram and an error diagram of a high frequency equivalent circuit interaction model according to an embodiment of the present invention;
FIG. 8 is a block diagram of an interactive multi-model particle filter system according to an embodiment of the present invention;
FIG. 9 is a SOC diagram and an error diagram of a battery using an interactive multi-model particle filter according to an embodiment of the present invention;
fig. 10 is a block diagram of an SOC estimation apparatus of an energy storage system according to an embodiment of the present invention;
fig. 11 is a block diagram of a computer device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, fig. 1 is a flowchart illustrating a method for estimating SOC of an energy storage system according to an embodiment of the present invention, where the method shown in fig. 1 includes the following steps:
101. determining an interaction model of a high-frequency equivalent circuit and an interaction model of a medium-low frequency equivalent circuit of the energy storage system based on the charging and discharging characteristics of the energy storage system and the acquired current data, voltage data and temperature data of the energy storage system, wherein the charging and discharging characteristics at least comprise the hysteresis characteristics of the charging and discharging of the energy storage system;
it should be noted that the SOC estimation method of the energy storage system disclosed in the present application is to perform real-time SOC calculation of the energy storage system, so as to dynamically grasp the SOC state of the energy storage system. Firstly, in order to calculate the SOC of the energy storage system, an equivalent model of the energy storage system needs to be established, and the interactive model of two equivalent circuits is established for the energy storage system according to different frequency bands so as to improve the modeling accuracy. Specifically, an interaction model of a high-frequency equivalent circuit and an interaction model of a medium-low frequency equivalent circuit of the energy storage system are determined based on the charging and discharging characteristics of the energy storage system and the collected current data, voltage data and temperature data of the energy storage system, the charging and discharging characteristics at least comprise the hysteresis characteristics of charging and discharging of the energy storage system, and the energy storage system can be an energy storage battery and the like.
Taking the energy storage system as an energy storage battery as an example, step 101 may include the following steps step1.1 and step1.2:
step1.1: the voltage and the current of the energy storage battery are collected through the voltage and current collecting equipment, and the pole temperature of the energy storage battery is collected through the temperature sensor.
Referring to fig. 2, fig. 2 is a waveform diagram of experimental test current and open-circuit voltage signals according to an embodiment of the present invention, in this embodiment, a certain type of lead-carbon battery with a rated capacity of 660Ah is used as an experimental object, the rated voltage of the battery is 2V, the discharge cut-off voltage is 1.8V, and the maximum charging current is 300A. Discharging at 0.1C, discharging current of 66A, discharging 5% SOC each time, standing for 10min, testing the battery with blue battery test system, collecting charging and discharging voltage and current data, measuring temperature with DS18B20 bonded temperature sensor, and collecting current and open-circuit voltage signal waveforms as shown in FIG. 2.
Step1.2: respectively establishing a high-frequency equivalent circuit interaction model considering the hysteresis characteristic of the energy storage battery and a medium-low frequency equivalent circuit interaction model considering the hysteresis characteristic according to the acquired voltage and current fluctuation frequency of the energy storage battery and the hysteresis characteristic of the battery during charging and discharging.
Based on electrochemical impedance spectrum, a high-frequency equivalent circuit interaction model taking hysteresis characteristics into consideration consists of a battery balance potential EMF and a hysteresis voltage U H Constituting a voltage source to replace the open circuit voltage U of the battery OCV(SOC,H,T) By a resistance R 0 The ohmic internal resistance of the energy storage battery is replaced by an inductor L to replace the high-frequency part of the equivalent circuit, and a resistor R 1 And a capacitor C 1 The equivalent circuit is replaced by a parallel connection, and reference may be specifically made to fig. 3 (a), where fig. 3 (a) is a high-frequency equivalent circuit model in an embodiment of the present invention. The open circuit voltage of the high-frequency equivalent circuit interaction model considering the hysteresis characteristic can be expressed as follows:
U OCV(SOC,H,T) ＝IR 0 +U L +U 1 +U 0 ；
wherein the open circuit voltage U OCV(SOC,H,T) Can be expressed as:
U OCV(SOC,H,T) ＝EMF+U H ；
its hysteresis voltage U H And the equilibrium potential EMF calculation formula is as follows:
wherein eta is 0.5-1, U charge To the equilibrium terminal voltage, U, of the battery during charging discharge Is the equilibrium terminal voltage of the cell at discharge.
Referring to fig. 4, a charging and discharging hysteresis voltage curve can be seen, and fig. 4 is a hysteresis characteristic diagram of charging and discharging of an energy storage battery according to an embodiment of the present invention.
Further, based on the electrochemical impedance spectrum, the medium-low frequency equivalent circuit interaction model considering the hysteresis characteristic consists of a battery balance potential EMF and a hysteresis voltage U H Constituting a voltage source to replace the open circuit voltage U of the battery OCV(SOC,H,T) By a resistance R 0 The ohmic internal resistance of the energy storage battery is replaced by a second-order RC circuit instead of the middle-low frequency part of the equivalent circuit, specifically referring to fig. 3 (b), where fig. 3 (b) is a low-frequency equivalent circuit model in the embodiment of the present invention. The open-circuit voltage of the medium-low frequency equivalent circuit interaction model considering the hysteresis characteristic can be expressed as follows:
U OCV(SOC,H,T) ＝IR 0 +U 1 +U 2 +U 0 ；
wherein the open circuit voltage U OCV(SOC,H,T) Can be expressed as:
U OCV(SOC,H,T) ＝EMF+U H ；
based on a middle-low frequency equivalent circuit interaction model considering hysteresis characteristics, a discrete state space equation can be established as follows:
wherein, tau 1 ＝R 1 C 1 ，τ 2 ＝R 2 C 2 ，
102. Respectively carrying out particle filtering processing on the interaction models, and determining the estimation SOC of each interaction model;
after the interactive model is obtained, particle filter processing may be performed on the interactive model by using Particle Filter (PF), so as to determine an estimated SOC of each interactive model. Specifically, the SOC estimation is performed by using the acquired voltage, current and temperature data, the established high-frequency equivalent circuit interaction model considering the hysteresis characteristic, and the established medium-low frequency equivalent circuit interaction model considering the hysteresis characteristic, and by using Particle Filter (PF).
The particle filter is as follows: the method is characterized in that a group of random samples which are propagated in a state space are searched to approximately represent a probability density function, the mean value of the samples is used for replacing integral operation, and then the minimum variance estimation process of the system state is obtained.
In contrast to Kalman filtering (Kalman Filter), the idea of particle filtering is based on the Monte Carlo method (Monte Carlo methods), which uses a set of particles to represent the probability, which can be used on any form of state space model. The core idea is to express the distribution of random state particles by extracting the random state particles from the posterior probability, and the method is a Sequential Importance Sampling method (Sequential import Sampling). Briefly, the particle filtering method is a process of approximating a probability density function by searching a group of random samples propagating in a state space, and substituting an integral operation with a sample mean value to obtain a state minimum variance distribution. The samples herein refer to particles, and any form of probability density distribution can be approximated when the number of samples N → ∞ is.
Although the probability distribution in the algorithm is only an approximation of the real distribution, due to the non-parametric characteristic, the method gets rid of the restriction that the random quantity must meet the Gaussian distribution when solving the nonlinear filtering problem, can express the distribution which is wider than that of a Gaussian model, and has stronger modeling capability on the nonlinear characteristic of variable parameters. Therefore, the particle filter can express posterior probability distribution based on the observed quantity and the controlled quantity more accurately, and can be used for solving the SLAM problem.
103. And performing SOC fusion processing by using the estimated SOC of each interactive model and a preset interactive multi-model to determine the target SOC of the energy storage system.
After the estimated SOC of each interactive Model is obtained, SOC fusion processing may be performed by using the estimated SOC of each interactive Model and a preset Interactive Multiple Model (IMM), so as to determine a target SOC of the energy storage system, that is, an SOC state of the energy storage system. By the mode, the estimated SOC of the two equivalent models can be fused in real time, and the real-time calculation of the SOC of the energy storage system is realized.
The main idea of the interactive multi-model control algorithm is automatic identification and switching among models based on Bayesian theory: at any tracking moment, model filters corresponding to the number of possible models of a target are set to detect the maneuvering model in real time, a weight coefficient and the probability of model updating are set for each filter, and finally the current optimal estimation state is obtained through weighting calculation, so that the purpose of model self-adaptive tracking is achieved.
Therefore, the two interactive models are fused through the interactive multi-model (IMM) to calculate the real-time SOC of the energy storage battery, the calculation accuracy is higher, the service life of the battery is better prolonged, and the safe and stable operation of a power grid is better maintained.
The invention provides an SOC estimation method of an energy storage system, which comprises the following steps: determining an interaction model of a high-frequency equivalent circuit and an interaction model of a medium-low frequency equivalent circuit of the energy storage system based on the charging and discharging characteristics of the energy storage system and the acquired current data, voltage data and temperature data of the energy storage system, wherein the charging and discharging characteristics at least comprise the hysteresis characteristics of the charging and discharging of the energy storage system; respectively carrying out particle filtering processing on the interaction models, and determining the estimation SOC of each interaction model; and performing SOC fusion processing by using the estimated SOC of each interactive model and a preset interactive multi-model to determine the target SOC of the energy storage system. According to the mode, the energy storage battery SOC estimation method based on the interactive multi-model particle filter is provided, firstly, high-frequency equivalent circuit and middle-low frequency equivalent circuit modeling is carried out respectively according to the working frequency of the energy storage battery, the established model accords with the real charging and discharging characteristics of the battery, SOC calculation errors caused by frequency band changes of high frequency and middle-low frequency are reduced, then, the particle filter is used for carrying out SOC estimation on the high-frequency equivalent circuit and the middle-low frequency equivalent circuit, and then, the integrated SOC is calculated through an interactive multi-model algorithm.
Referring to fig. 5, fig. 5 is another flowchart of a method for estimating SOC of an energy storage system according to an embodiment of the present invention, where the method shown in fig. 5 includes the following steps:
501. determining an interaction model of a high-frequency equivalent circuit and an interaction model of a medium-low frequency equivalent circuit of the energy storage system based on the charging and discharging characteristics of the energy storage system and the collected current data, voltage data and temperature data of the energy storage system, wherein the charging and discharging characteristics at least comprise the charging and discharging hysteresis characteristics of the energy storage system;
it should be noted that the content of step 501 is similar to that of step 101 shown in fig. 1, and for avoiding repetition, details of step 101 may be referred to specifically.
502. Respectively carrying out parameter identification on the interactive models by utilizing the current data, the voltage data, the temperature data and a preset forgetting factor recursive least square algorithm, and determining a parameter identification result of each interactive model, wherein the parameter identification result at least comprises model parameters of the interactive models;
it should be noted that, when determining the estimated SOC of each interactive model by using particle filtering, firstly, parameter identification is performed on the interactive model by using forgetting factor recursive least square method ((FFRLS), and secondly, SOC estimation is performed by using the identification result, so for each interactive model, parameter identification is performed in step 502, that is, acquired voltage, current, and temperature data are used to perform real-time parameter identification on the established high-frequency equivalent circuit interactive model considering hysteresis characteristics and the medium-low frequency equivalent circuit interactive model considering hysteresis characteristics by using Forgetting Factor Recursive Least Square (FFRLS), so as to obtain model parameters of each model, thereby facilitating subsequent SOC estimation based on the determined model parameters.
For example, the calculation formula of the parameter identification can be derived as the following mathematical expression:
in the formula, lambda is a forgetting factor, and is generally 0.95-1 in the energy storage battery, and theta k+1 Is the parameter identification result of the current time k + 1, theta k In order to input the parameter identification, the method comprises the following steps,
the measured value is an observed value of the energy storage system at the current moment K +1, y (K + 1) is a real feedback value of the energy storage system at the current moment K +1, K (K + 1) is a gain of the energy storage system at the current moment K +1, P (K + 1) is a covariance matrix of the energy storage system at the current moment K +1, and E is an identity matrix.
For example, referring to fig. 6 (a), fig. 6 (a) is a parameter identification result of an interaction model of a low frequency equivalent circuit according to an embodiment of the present invention; wherein, the resistance R 0 Fluctuating between 0.05-0.055 omega, resistance R 1 Fluctuating between 0.055 and 0.065 omega, resistance R 2 Fluctuation between 0.11-0.12 omega, capacitance C 1 Fluctuation between 1.8 and 2.0F, capacitance C 2 Fluctuating between 4.5-5F. Further, fig. 6 (b) shows a parameter identification result of a high frequency equivalent circuit interaction model according to an embodiment of the invention, as shown in fig. 6 (b), a resistance R 0 Fluctuating between 0.01 and 0.015 omega, resistance R 1 Fluctuates between 0.001-0.0012 omega, inductance L fluctuates between 0.01-0.015H, and capacitanceC 1 Fluctuating between 40-45F. It can be understood that, in this embodiment, only the embodiment that 4000 pieces of data are collected is shown to illustrate the beneficial effects of the present application, in practice, as the collected data volume increases with the time of real-time collection, the data volume may correspondingly increase, and this embodiment is only used as an example and is not limited specifically herein.
503. Respectively carrying out particle filtering processing on the interaction models by using the model parameters of the interaction models to determine the estimated SOC of each interaction model;
further, after the real-time parameter identification result of the interactive model is obtained, example filtering can be performed on the interactive model through the real-time model parameters, so that the estimated SOC of the interactive model is obtained.
In one possible implementation, the state equations of the energy storage system need to be filtered and updated, and therefore, step 503 includes A1-A6:
a1, determining a state equation and an observation equation of the energy storage system by using model parameters of the interaction model;
firstly, the state equation and the observation equation of the energy storage system can be determined through the model parameters, and the state equation and the observation equation of the system are as follows:
in the formula, A k-1 Is a system state transition matrix at time k-1, B k-1 For the system control matrix at time k-1, W k-1 Systematic process noise at time k-1, C k An observation matrix for the system at time k, D k Is a direct matrix of time k, V k For observing noise at time k, x k Is a state variable at time k, u k A variable is input for time k.
A2, for each interaction model, at an initialization time k =0, performing particle sampling based on prior probability of a state equation of the energy storage system, and determining N state particles at the initialization time, wherein the weight of the N state particles at each initialization time is 1/N; let k = k +1, enter the iterative process of particle filtering;
it should be noted that, for each interaction model, there is only one data point at the initialization time k =0, and therefore, for particle sampling at the initialization time, particle sampling is performed based on a prior probability of the state equation at the initialization time, so as to obtain N state particles at the initialization time
And will->
Set the weight of each particle in (1/N) to update the equation of state x at the time k =0 0 Is updated->
Then let k = k +1 enter the iterative process of particle filtering.
Wherein the content of the first and second substances,
in the formula, the function E [. Cndot]For expectation, P 0 i Is the covariance at this time.
A3, taking the system state transition probability density at the time k as the importance probability density at the time k, and sampling particles at the time k by using the importance probability density at the time k to obtain N state particles at the time k;
further, when k =1,2,3, \8230;, after N, updating of state particles at time k is performed by importance sampling, after initialization is completed, the system state transition probability density at time k is taken as the importance probability density at time k, and at time k, sampling of particles at time k is performed by using the importance probability density, thereby obtaining N state particles x at time k k i ，i＝1，2，……，N 。
In the example, importance sampling, the system state transition probability density is used as the importance probability density, and x is obtained by sampling the importance probability density at the time k k i ，i＝1,2,…,N。
q(x k ∣x 0:k-1 (i),z 1:k )＝p(x k ∣x k-1 (i))；
In the formula, p (x) k ∣x k-1 (i) Q (x) is the system state transition probability density k ∣x 0:k-1 (i),z 1:k ) Is the probability density of importance.
A4, determining the update weight of the N state particles at the k moment by using the observed value of the observation equation of the N state particles at the k moment and a preset weight update algorithm;
further, weight updating and normalization are performed through the step A4, and weight at the moment k is updated and then normalization processing is performed.
In the formula, w k i X at updated k time k i The weight value of the weight is calculated,
x at normalized k time k i And (6) weighting.
A5, resampling at the k moment by using the updated weight and a preset resampling threshold, and determining filtered state particles at the k moment;
further, resampling at the time k by using the updated weight and a preset resampling threshold, determining filtered state particles at the time k, specifically, resampling, setting a resampling threshold, comparing the weight of the particles with the threshold, if the particles with the weight smaller than the threshold are eliminated, resampling the original particles as particles with the same weight, otherwise, obtaining new particles with new amplitudes.
And A6, determining a state estimation value updated by the interactive model at the k moment according to the filtered state particles at the k moment and a preset state updating algorithm, wherein the state estimation value updated by the k moment at least comprises an estimated SOC of the interactive model at the k moment, enabling k = k +1, returning to execute the step of taking the system state transition probability density at the k moment as the importance probability density at the k moment, and sampling the particles at the k moment by using the importance probability density at the k moment to obtain N state particles at the k moment until the particle filtering at all the moments is completed.
Finally, updating the state value at the time k by using the filtered particles in the step A6, determining an updated state estimation value of the interaction model at the time k according to the filtered state particles at the time k and a preset state updating algorithm, and performing state updating and estimation covariance updating of the battery, wherein the state updating algorithm exemplarily includes the following mathematical expressions:
in the formula (I), the compound is shown in the specification,
for the updated state estimate at time k, <' >>
A covariance estimate updated for time k; n is the total number of state particles; />
The ith state particle at the moment k; />
The weight of the ith state particle at time k.
It should be noted that the medium and low frequency equivalent circuit interaction model adopts the identified parameters and the voltage and current obtained by the test, the oscillogram and the SOC estimation error obtained by performing SOC estimation by using the particle filter are shown in fig. 7 (a), fig. 7 (a) is an SOC estimation graph and an error graph of the low frequency equivalent circuit interaction model adopting the Particle Filter (PF) in the embodiment of the present invention, and it can be seen from the error graph that the error of the low frequency equivalent circuit interaction model in sampling is smaller than the error of the ampere-hour integration method.
The waveform diagram and the SOC estimation error obtained by SOC estimation through particle filtering are shown in fig. 7 (b), the SOC estimation diagram and the error diagram are used for the high-frequency equivalent circuit interaction model in the embodiment of the invention, the SOC estimated through the high-frequency equivalent circuit interaction model is more accurate than the SOC estimated through an ampere-hour integration method, and the estimation error of the ampere-hour integration method is continuously increased through the error diagram.
504. And performing SOC fusion processing by using the estimated SOC of each interactive model and a preset interactive multi-model to determine the target SOC of the energy storage system.
It should be noted that, in order to avoid repetition, the content of step 504 is similar to that of step 103 shown in fig. 1, and details are not repeated here, and specifically, reference may be made to the content of step 103 shown in fig. 1.
In a possible implementation manner, the fusing the SOC estimated by each model by using an interactive multi-model (IMM) in step 504 to obtain a fused SOC may include the following steps B1 to B6:
b1, inputting the updated state estimation value and covariance estimation value of each interactive model at the moment k into a preset interactive multi-model, and determining the state estimation value and covariance estimation value of the energy storage system at the moment k;
it should be noted that the state estimation values of the two models are obtained at each time, and in order to obtain the SOC state of the energy storage battery, data of the two models at the same time needs to be fused, specifically, the state estimation value and the covariance estimation value updated by each interactive model at the time k are input into a preset interactive multi-model, and the state estimation value and the covariance estimation value of the energy storage system at the time k are determined.
Referring to fig. 8, fig. 8 is a block diagram of an interactive multi-model particle filter system according to an embodiment of the present invention, that is, a first step is performed through step B1: and performing IMM input interaction, namely initializing or re-initializing the model condition to obtain the state vector and the covariance matrix of each particle filter input at the current moment of the model. For model j, the state estimation value and covariance estimation value of the energy storage system at the time k are as follows:
in the above formula:
the state estimation value of the nth particle in the model j at the kth moment is obtained; pi lj The probability of switching the interactive model j to the interactive model l; />
Is the value of the covariance matrix at time k; />
And the probability of the model j at the moment k, m is the total number of the interaction models, N is the particle number of the particle filter, and N is the total particle number at each moment.
B2, respectively carrying out particle filtering on each interactive model by using current data, voltage data, temperature data, and state estimation value and covariance estimation value of the energy storage system at the moment k, and estimating the state estimation value, the covariance estimation value, the residual error estimation value and the residual covariance estimation value of each interactive model at the moment k + 1;
further, the probability of the utilization model is updatedAfter the state particles are obtained, performing particle filtering again, specifically, using the current data, the voltage data, the temperature data and the state estimation value of the energy storage system at the k moment through B2 in the second step
And covariance estimate>
Respectively carrying out particle filtering on each interactive model, and estimating the state estimation value ^ of each interactive model at the moment of k +1>
Covariance estimate>
Residual estimate->
And residual covariance estimate>
Exemplarily, j =1, \8230forthe interaction model, N is represented by the voltage, the current, the pole column temperature and the on/off ratio of the energy storage battery>
And->
Particle Filter with @ofperiod k as input>
And &>
An estimate is obtained of the state of the next cycle k +1 and its covariance>
And &>
Residual and covariance thereof->
Further, the model probability is updated, and the model probability is updated through a likelihood function, which is specifically referred to the following.
B3, determining the likelihood function of each interactive model at the moment of k +1 by using the residual estimation value, the residual covariance estimation value and a preset density function which obeys Gaussian distribution;
illustratively, the likelihood function for the jth model may be expressed as:
wherein, N [. C]Representing a density function that follows a gaussian distribution,
is the likelihood function of model j at time k + 1.
B4, determining the model probability of each interaction model at the moment k +1 by using the likelihood function at the moment k +1 and a preset model probability algorithm;
for example, the model probability algorithm at the time of model k +1 is as follows:
in the formula (I), the compound is shown in the specification,
model probability for the interaction model j at time k + 1, based on>
Likelihood function for the interaction model j at the moment k +1, <' >>
The model probability of the interaction model j at the moment k is shown, and n is the particle number of the particle filter.
B5, performing fusion processing according to the model probability and the state estimation value and covariance estimation value at the moment k +1 to determine a target state estimation value of the energy storage system at the moment k + 1;
and B6, determining the target SOC of the energy storage system by using the target state estimation value and a preset SOC extraction algorithm.
And finally, performing IMM output interaction through the step B5 to obtain a target state estimation value. The fused SOC is output via B6. For example, the target state estimation value and the target covariance estimation value of the energy storage system at the time k +1 include the following mathematical expressions:
in the formula (I), the compound is shown in the specification,
the target state estimation value of the energy storage system at the moment k +1 is obtained; p is n (k +1k + 1) is a target covariance estimation value of the energy storage system at the moment k + 1; m is the total number of interaction models, and>
the model probability of the interaction model j at the moment k + 1; n is the number of particles of the particle filter; />
The state estimation value of the model j at the moment k + 1;
the SOC extraction algorithm includes the following mathematical expression:
in the formula, SOC ksum To be a target SOC for the energy storage system,
for the estimated value of the target state of the energy storage system at the moment k +1
It should be noted that, in the following description,
is a state matrix, wherein the SOC state is included, therefore, the target SOC of the energy storage battery can be extracted through the algorithm.
In this embodiment, a covariance matrix, a model probability and a state equation of the medium-low frequency equivalent circuit interaction model and the high-frequency equivalent circuit interaction model are interacted by using the IMM, and a more accurate battery SOC estimation value and an error thereof are finally calculated, as shown in fig. 9, fig. 9 is an SOC diagram and an error diagram of a battery using an interactive multi-model particle filter in the embodiment of the present invention, and it can be seen from the IMM filter error diagram that the error of battery SOC estimation using the IMM filter is the minimum and tends to be within 0.001, and the estimation effect is far better than that of a single filter estimation model.
The invention provides an SOC estimation method of an energy storage system, which is an energy storage battery SOC estimation method based on interactive multi-model particle filtering.
Referring to fig. 10, fig. 10 is a block diagram of an SOC estimation device of an energy storage system according to an embodiment of the present invention, where the device shown in fig. 10 includes:
model determination module 1001: the energy storage system interaction model determining method comprises the steps that an interaction model of a high-frequency equivalent circuit and an interaction model of a medium-low frequency equivalent circuit of the energy storage system are determined based on the charging and discharging characteristics of the energy storage system and collected current data, voltage data and temperature data of the energy storage system, and the charging and discharging characteristics at least comprise the charging and discharging hysteresis characteristics of the energy storage system;
SOC estimation module 1002: the device is used for respectively carrying out particle filtering processing on the interaction models and determining the estimation SOC of each interaction model;
SOC fusion module 1003: and the target SOC of the energy storage system is determined by performing SOC fusion processing by using the estimated SOC of each interactive model and a preset interactive multi-model.
It should be noted that the functions of each module in the apparatus shown in fig. 10 are similar to the contents of each step in the method shown in fig. 1, and for avoiding repetition, details of each step in the method shown in fig. 1 may be specifically referred to.
The invention provides a SOC estimation device of an energy storage system, which comprises: a model determination module: the method comprises the steps that an interaction model of a high-frequency equivalent circuit and an interaction model of a medium-low frequency equivalent circuit of the energy storage system are determined based on the charging and discharging characteristics of the energy storage system and collected current data, voltage data and temperature data of the energy storage system, and the charging and discharging characteristics at least comprise the charging and discharging hysteresis characteristics of the energy storage system; an SOC estimation module: the device is used for respectively carrying out particle filtering processing on the interaction models and determining the estimation SOC of each interaction model; an SOC fusion module: and the target SOC of the energy storage system is determined by using the estimated SOC of each interactive model and the preset interactive multi-model to perform SOC fusion processing. According to the mode, the energy storage battery SOC estimation method based on the interactive multi-model particle filter is provided, firstly, high-frequency equivalent circuit and middle-low frequency equivalent circuit modeling is carried out respectively according to the working frequency of the energy storage battery, the established model accords with the real charging and discharging characteristics of the battery, SOC calculation errors caused by frequency band changes of high frequency and middle-low frequency are reduced, then, the particle filter is used for carrying out SOC estimation on the high-frequency equivalent circuit and the middle-low frequency equivalent circuit, and then, the integrated SOC is calculated through an interactive multi-model algorithm.
FIG. 11 is a diagram illustrating an internal structure of a computer device in one embodiment. The computer device may specifically be a terminal, and may also be a server. As shown in fig. 11, the computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein 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 may also store a computer program which, when executed by the processor, causes the processor to carry out the above-mentioned method. The internal memory may also have stored therein a computer program which, when executed by the processor, causes the processor to perform the method described above. Those skilled in the art will appreciate that the architecture shown in fig. 11 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In an embodiment, a computer device is proposed, comprising a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to perform the steps of the method as shown in fig. 1 or fig. 2.
In an embodiment, a computer-readable storage medium is proposed, in which a computer program is stored which, when being executed by a processor, causes the processor to carry out the steps of the method as shown in fig. 1 or fig. 2.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a non-volatile computer-readable storage medium, and can include the processes of the embodiments of the methods described above when the program is executed. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. 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. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), rambus (Rambus) direct RAM (RDRAM), direct Rambus Dynamic RAM (DRDRAM), and Rambus Dynamic RAM (RDRAM), among others.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present application. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A method of estimating SOC of an energy storage system, the method comprising:
determining an interaction model of a high-frequency equivalent circuit and an interaction model of a medium-low frequency equivalent circuit of the energy storage system based on the charging and discharging characteristics of the energy storage system and the acquired current data, voltage data and temperature data of the energy storage system, wherein the charging and discharging characteristics at least comprise the hysteresis characteristics of the charging and discharging of the energy storage system;
respectively carrying out particle filtering processing on the interaction models, and determining the estimation SOC of each interaction model;
and performing SOC fusion processing by using the estimated SOC of each interactive model and a preset interactive multi-model to determine the target SOC of the energy storage system.
2. The method of claim 1, wherein said separately subjecting said interaction models to particle filtering to determine an estimated SOC for each of said interaction models comprises:
respectively carrying out parameter identification on the interactive models by utilizing the current data, the voltage data, the temperature data and a preset forgetting factor recursive least square algorithm, and determining a parameter identification result of each interactive model, wherein the parameter identification result at least comprises model parameters of the interactive models;
and respectively carrying out particle filtering processing on the interaction models by using the model parameters of the interaction models to determine the estimated SOC of each interaction model.
3. The method of claim 2, wherein the performing particle filter processing on the interaction models respectively by using model parameters of the interaction models to determine an estimated SOC of each interaction model comprises:
determining a state equation and an observation equation of the energy storage system by using the model parameters of the interaction model;
for each interaction model, at an initialization time k =0, performing particle sampling based on prior probability of a state equation of the energy storage system, and determining N state particles at the initialization time, wherein the weight of the N state particles at each initialization time is 1/N; enabling k = k +1 to enter an iterative process of particle filtering;
taking the system state transition probability density at the time k as the importance probability density at the time k, and sampling particles at the time k by using the importance probability density at the time k to obtain N state particles at the time k;
determining the update weight values of the N state particles at the k moment by using the observed values of the observation equations of the N state particles at the k moment and a preset weight value update algorithm;
resampling at the k moment by using the updated weight and a preset resampling threshold, and determining filtered state particles at the k moment;
and determining an updated state estimation value of the interactive model at the time k according to the filtered state particles at the time k and a preset state updating algorithm, wherein the updated state estimation value at the time k at least comprises an estimated SOC of the interactive model at the time k, enabling k = k +1, returning to execute the step of taking the system state transition probability density at the time k as the importance probability density at the time k, and sampling the particles at the time k by using the importance probability density at the time k to obtain N state particles at the time k until the particle filtering at all the times is completed.
4. The method according to claim 3, wherein the determining the target SOC state of the energy storage system by using the estimated SOC state of each interactive model and a preset interactive multi-model for SOC fusion processing comprises:
inputting the updated state estimation value and covariance estimation value of each interactive model at the moment k into a preset interactive multi-model, and determining the state estimation value and covariance estimation value of the energy storage system at the moment k;
respectively carrying out particle filtering on each interactive model by using the current data, the voltage data, the temperature data, the state estimation value and the covariance estimation value of the energy storage system at the moment k, and estimating the state estimation value, the covariance estimation value, the residual error estimation value and the residual covariance estimation value of each interactive model at the moment k + 1;
determining the likelihood function of each interaction model at the moment of k +1 by using the residual error estimated value, the residual error covariance estimated value and a preset density function which obeys Gaussian distribution;
determining the model probability of each interaction model at the moment k +1 by using the likelihood function at the moment k +1 and a preset model probability algorithm;
performing fusion processing according to the model probability and the state estimation value and covariance estimation value at the moment k +1 to determine a target state estimation value of the energy storage system at the moment k + 1;
and determining the target SOC of the energy storage system by using the target state estimation value and a preset SOC extraction algorithm.
5. The method of claim 2, wherein the parameter identification result comprises the following mathematical expression:
wherein λ is forgetting factor, θ k+1 Is the parameter identification result of the current time k +1, theta k For the purpose of inputting the parameter identification,
the measured value is an observed value of the energy storage system at the current moment K +1, y (K + 1) is a real feedback value of the energy storage system at the current moment K +1, K (K + 1) is a gain of the energy storage system at the current moment K +1, P (K + 1) is a covariance matrix of the energy storage system at the current moment K +1, and E is an identity matrix.
6. The method of claim 3, wherein the state update algorithm comprises the following mathematical expression:
in the formula (I), the compound is shown in the specification,
for the updated state estimate at time k, <' >>
A covariance estimation value updated for the time k; n is the total number of state particles; />
The ith state particle at the moment k; />
The weight of the ith state particle at time k.
7. The method of claim 4, wherein the target state estimate and the target covariance estimate for the energy storage system at time k +1 comprise the following mathematical expressions:
in the formula (I), the compound is shown in the specification,
the target state estimation value of the energy storage system at the moment k +1 is obtained; p is n (k +1 < k + > 1) is a target covariance estimated value of the energy storage system at the moment of k + 1; m is the total number of interaction models, and>
the model probability of the interaction model j at the moment k + 1; n is the number of particles of the particle filter; />
The state estimation value of the model j at the moment k + 1;
the SOC extraction algorithm includes the following mathematical expression:
in the formula, SOC ksum To be a target SOC for the energy storage system,
and the estimated value of the target state of the energy storage system at the moment k +1 is obtained. />
8. An apparatus for estimating SOC of an energy storage system, the apparatus comprising:
a model determination module: the energy storage system interaction model determining method comprises the steps that an interaction model of a high-frequency equivalent circuit and an interaction model of a medium-low frequency equivalent circuit of the energy storage system are determined based on the charging and discharging characteristics of the energy storage system and collected current data, voltage data and temperature data of the energy storage system, and the charging and discharging characteristics at least comprise the charging and discharging hysteresis characteristics of the energy storage system;
an SOC estimation module: the device is used for respectively carrying out particle filtering processing on the interaction models and determining the estimation SOC of each interaction model;
an SOC fusion module: and the target SOC of the energy storage system is determined by performing SOC fusion processing by using the estimated SOC of each interactive model and a preset interactive multi-model.
9. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, causes the processor to carry out the steps of the method according to any one of claims 1 to 7.
10. A computer arrangement comprising a memory and a processor, characterized in that the memory stores a computer program which, when executed by the processor, causes the processor to carry out the steps of the method according to any one of claims 1 to 7.
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| CN116449221B (en) * | 2023-06-14 | 2023-09-29 | 浙江天能新材料有限公司 | Lithium battery state of charge prediction method, device, equipment and storage medium |
| CN116736139A (en) * | 2023-07-13 | 2023-09-12 | 江苏果下科技有限公司 | SOC estimation method of household energy storage system |
| CN116736139B (en) * | 2023-07-13 | 2024-02-02 | 江苏果下科技有限公司 | SOC estimation method of household energy storage system |
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