CN-121978541-A - Energy storage battery state of charge estimation method

CN121978541ACN 121978541 ACN121978541 ACN 121978541ACN-121978541-A

Abstract

The invention discloses a method for estimating the state of charge of an energy storage battery, and relates to the technical field of the state of charge of the energy storage battery. According to the estimation method, firstly, a variable RC parameter model of the energy storage battery is built and a state space equation of the model of the energy storage battery VPRC is arranged, so that a state space equation of the output voltage and the state of charge of the energy storage battery is obtained, a staged parameter estimation method is provided, parameters such as variable internal resistance, diffusion resistance and diffusion capacitance of the energy storage battery are calculated in a partitioning mode, the model VPRC is corrected according to the variable parameter result of the energy storage battery calculated in the partitioning mode, and finally the state of charge of the energy storage battery is estimated according to the variable parameter of the energy storage battery in the partitioning mode, and the state of charge of the energy storage battery is updated through calculating the parameters such as error covariance and gain coefficient.

Inventors

XU FAN
JIANG YIJUN
JIN YAXI
HE TAO
CONG XIAOMING

Assignees

国网安徽省电力有限公司马鞍山供电公司

Dates

Publication Date: 20260505
Application Date: 20260320

Claims (9)

1. A method for estimating state of charge of an energy storage battery, comprising: S1, building a variable RC parameter model of an energy storage battery; the variable RC parametric model includes a variable power Chi Nazu Variable diffusion resistance Variable diffusion capacitance Open circuit voltage of battery Wherein And (3) with Parallel and then with In series with Serial connection; S2, listing a state space equation of the energy storage battery according to the variable RC parameter model; S3, according to a staged parameter estimation method, calculating variable RC parameters of the energy storage battery in a partitioning mode; s4, estimating the charge state of the energy storage battery according to the variable RC parameters; the staged parameter estimation method comprises the following steps: S31, dividing the SOC range of the energy storage battery into N stages; s32, calculating global parameters, wherein the global parameters are the overall statistical characteristic values of the RC parameter model; S33, calculating a stage parameter for each stage; S34, evaluating parameter fluctuation according to the stage parameters; And S35, if the parameter fluctuation condition is larger than the first threshold value, the number of stages is increased, and then the step S31 is returned, otherwise, the current stage number N is locked.
2. The method for estimating a state of charge of an energy storage battery according to claim 1, wherein the average value of the overall characteristic comprises: ; In the formula, Is all that The number of points is set to be equal to the number of points, 、、 Respectively the internal resistance, the diffusion resistance and the diffusion capacitance of the battery are in the first place Personal (S) The cell internal resistance, diffusion resistance and diffusion capacitance of the point, 、、 The internal resistance, the diffusion resistance and the diffusion capacitance of the battery are all respectively Average of points.
3. The method for estimating a state of charge of an energy storage battery as set forth in claim 2, wherein said overall characteristic is a variable parameter maximum deviation Comprising the following steps: ; In the formula, Is all that The maximum deviation of the internal resistance of the battery under the point, Is all that The maximum deviation of the diffusion resistance under the point, Is all that The maximum deviation of the diffusion capacitance under the point.
4. The method for estimating a state of charge of an energy storage battery as set forth in claim 3, wherein said integral feature comprises a variable parameter standard deviation value Comprising the following steps: ; In the formula, Is all that Standard variance value of internal resistance of the battery under the point, Is all that Standard variance value of the diffusion resistance under the point, Is all that Standard variance value of diffusion capacitance under point.
5. The method for estimating a state of charge of an energy storage battery according to claim 4, wherein said setting of said energy storage battery The number of the range stages is A battery is provided Divided into And (3) calculating variable parameters of the energy storage battery in each stage: ; In the formula, Is an energy storage battery Stage and take , Is the first Personal (S) Of stages The number of points is set to be equal to the number of points, 、、 Respectively the first Personal (S) The internal resistance, diffusion resistance and diffusion capacitance of the energy storage battery of the point, 、、 Respectively the first Personal (S) The average value of the internal resistance, the diffusion resistance and the diffusion capacitance of the energy storage battery in the stage.
6. The method for estimating a state of charge of an energy storage battery as set forth in claim 5, wherein the first step of calculating Personal (S) Variable parameter maximum deviation of stage energy storage battery VPRC model : ; In the formula, Is the first Personal (S) The maximum deviation of the internal resistance of the battery under the stage, Is the first Personal (S) The maximum deviation of the diffusion resistance under the stage, Is the first Personal (S) The diffusion capacitance maximum deviation under the stage.
7. The method for estimating a state of charge of an energy storage battery as set forth in claim 6, wherein the first step of calculating Personal (S) Variable parameter standard deviation value of stage energy storage battery VPRC model : ; In the formula, Is the first Personal (S) Standard variance value of internal resistance of the battery under the stage, Is the first Personal (S) The standard variance value of the diffusion resistance under the stage, Is the first Personal (S) Standard variance value of diffusion capacitance under stage.
8. The method for estimating a state of charge of an energy storage battery according to claim 7, wherein S35 is a total of Comparing the maximum deviation value of the energy storage battery model parameters under the range and the SPEA algorithm with the standard deviation value, and if the following condition of the first threshold is satisfied: ; s4 is entered, otherwise the number of stages is recalculated = +1, And proceeds to S31.
9. The method for estimating a state of charge of an energy storage battery according to claim 1, wherein the step of S2 is as follows: s21, defining a model current relation of the energy storage battery VPRC: ; In the formula, For the energy storage battery to output a current, A voltage value of the diffusion resistor/capacitor; s22, discretizing the current relation of the model of the energy storage battery VPRC: ; S23, calculating the charge state of the energy storage battery VPRC model : ; In the formula, Defined as the total design capacity of the energy storage battery, As the ratio of the current available power to the maximum charge/discharge power of the energy storage battery, The initial electric quantity of the energy storage battery is; s24, discretizing the charge state of the energy storage battery VPRC model: ; s25, obtaining a state space equation of the model of the energy storage battery VPRC according to the current relation and the state of charge of the energy storage battery: ; s26, calculating output voltage of energy storage battery VPRC model : 。

Description

Energy storage battery state of charge estimation method Technical Field The invention relates to the technical field of state of charge of an energy storage battery, in particular to a state of charge estimation method of the energy storage battery. Background With the wide application of renewable energy sources and the vigorous development of new energy industry, the importance of the energy storage battery as a core functional component of the modern new energy technology is increasingly prominent, and meanwhile, the market demand of the energy storage battery is in explosive growth. In recent years, lithium ion batteries have become a mainstream technology in the energy storage field by virtue of characteristics such as high energy density, low self-discharge, and excellent cycle performance. In the field of energy storage, the state of charge of an energy storage battery is a critical parameter, and the state of charge is defined as the ratio of the current remaining capacity of the battery to the capacity of the battery in a fully charged and discharged state, wherein the value ranges from 0% to 100%, when the state of charge is 0%, the state of charge indicates that the battery is fully discharged, and when the state of charge is 100%, the state of charge indicates that the battery is fully charged. The state of charge is an important basis for evaluating the performance and the use state of the battery, so that accurate and low-error state of charge estimation has important significance for decision making of an energy storage battery management system, prolonging of the service life of the battery and guaranteeing of the safety of the battery. At present, the calculation method of the state of charge of the energy storage battery mainly comprises an ampere-hour integration method, an open-circuit voltage method, an impedance method, a Kalman filtering method and the like, and the methods are all charge state calculation methods based on models such as an equivalent model of an energy storage battery circuit or an electrochemical model, so that the model precision of the energy storage battery has a fundamental influence on the estimation precision of the method, however, the current estimation method of the state of charge of the energy storage battery only considers the changes in the aspects of environmental temperature, carbon rate, charge-discharge cycle and the like, and parameter changes in the battery are not reflected in the whole range of the state of charge, which can cause unavoidable errors in the aspect of estimation precision in the estimation process of the state of charge. In the prior art, if the number of stages of the battery is too small, rough parameter fitting and large error are caused, but if the number of stages is too large, the calculation load is increased, and fitting is easy to be performed too much. Therefore, in order to improve the estimation accuracy of the state of charge of the energy storage battery, it is important to develop the capacity of the energy storage battery to the maximum extent and to research a novel method for estimating the state of charge of the energy storage battery with variable model parameters. Disclosure of Invention (One) solving the technical problems Aiming at the defects of the prior art, the invention provides a method for estimating the state of charge of an energy storage battery, which solves the technical problems mentioned in the background art. (II) technical scheme In order to achieve the purpose, the invention is realized by the following technical scheme that the method for estimating the state of charge of the energy storage battery comprises the following steps: S1, building a variable RC parameter model of an energy storage battery; the variable RC parametric model includes a variable power Chi Nazu Variable diffusion resistanceVariable diffusion capacitanceOpen circuit voltage of batteryWhereinAnd (3) withParallel and then withIn series withSerial connection; S2, listing a state space equation of the energy storage battery according to the variable RC parameter model; S3, according to a staged parameter estimation method, calculating variable RC parameters of the energy storage battery in a partitioning mode; and S4, estimating the charge state of the energy storage battery according to the variable RC parameter. Preferably, the step-type parameter estimation method includes the following steps: S31, dividing the SOC range of the energy storage battery into N stages; s32, calculating global parameters, wherein the global parameters are the overall statistical characteristic values of the RC parameter model; S33, calculating a stage parameter for each stage; S34, evaluating parameter fluctuation according to the stage parameters; And S35, if the parameter fluctuation condition is larger than the first threshold value, the number of stages is increased, and then the step S31 is returned, otherwise, the current stage number