Energy storage battery charge state estimation method

Abstract

The invention discloses an energy storage battery charge state estimation method, and relates to the technical field of energy storage battery charge states. According to the estimation method, firstly, an energy storage battery variable RC parameter model is built, a state-space equation of the energy storage battery VPRC model is listed, 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 partitioned mode, and the energy storage battery is estimated. The VPRC model is corrected according to the variable parameter result of the energy storage battery calculated in a partitioned mode, finally, the charge state of the energy storage battery is estimated according to the variable parameters of the energy storage battery in the partitioned mode, and the charge state of the energy storage battery is updated by calculating parameters such as error covariance and a gain coefficient. According to the method, the inherent error of each estimation method on model parameters is eliminated, the accuracy of battery charge state estimation is greatly improved, the service life of the energy storage battery can be prolonged, and the safety of the battery in the using process is guaranteed.

Description

Technical Field

[0001] This invention relates to the field of energy storage battery state of charge technology, specifically to a method for estimating the state of charge of an energy storage battery. Background Technology

[0002] With the widespread application of renewable energy and the booming development of the new energy industry, energy storage batteries, as a core functional component of contemporary new energy technologies, are becoming increasingly important, and the market demand for energy storage batteries is experiencing explosive growth. In recent years, lithium-ion batteries have become the mainstream technology in the energy storage field due to their high energy density, low self-discharge, and excellent cycle performance.

[0003] In the field of energy storage, the state of charge (SOC) of a battery is a crucial parameter. SOC is defined as the ratio of a battery's current remaining capacity to its capacity in a fully charged / discharged state, ranging from 0% to 100%. A SOC of 0% indicates that the battery is fully discharged, while a SOC of 100% indicates that the battery is fully charged. SOC is an important basis for evaluating battery performance and usage status. Therefore, accurate and low-error SOC estimation is of great significance for decision-making in energy storage battery management systems, extending battery life, and ensuring battery safety. Currently, the main methods for calculating the state of charge (SOC) of energy storage batteries include the ampere-hour integration method, the open-circuit voltage method, the impedance method, and the Kalman filter method. These methods are all based on equivalent circuit models or electrochemical models of energy storage batteries. Therefore, the accuracy of the energy storage battery model has a fundamental impact on the estimation accuracy of the above methods. However, current methods for estimating the SOC of energy storage batteries only consider changes in ambient temperature, carbon rate, and charge-discharge cycles, and do not reflect changes in parameters within the battery across the entire SOC range. This leads to unavoidable errors in the estimation accuracy during the SOC estimation process.

[0004] In existing technologies, if the number of battery stages is too small, the parameter fitting will be rough and the error will be large. However, if the number of stages is too large, the computational burden will increase and overfitting will be more likely.

[0005] Therefore, in order to improve the accuracy of energy storage battery state of charge estimation and maximize the capacity of energy storage batteries, it is very important to study a new energy storage battery state of charge estimation method with variable model parameters. Summary of the Invention

[0006] (a) Technical problems to be solved

[0007] To address the shortcomings of existing technologies, this invention provides a method for estimating the state of charge of energy storage batteries, thus solving the technical problems mentioned in the background section.

[0008] (II) Technical Solution

[0009] To achieve the above objectives, the present invention provides the following technical solution: a method for estimating the state of charge of an energy storage battery, comprising:

[0010] S1: Build a variable RC parameter model for the energy storage battery;

[0011] The variable RC parameter model includes: variable battery internal resistance. Variable diffusion resistance Variable diffusion capacitor Battery open circuit voltage ,in and After parallel connection with Connected, and then with Series;

[0012] S2: List the state-space equations of the energy storage battery based on the variable RC parameter model;

[0013] S3: Calculate the variable RC parameters of the energy storage battery by region according to the staged parameter estimation method;

[0014] S4: Estimate the state of charge of the energy storage battery based on the variable RC parameters.

[0015] Preferably, the staged parameter estimation method includes the following steps:

[0016] S31: Divide the SOC range of the energy storage battery into N stages;

[0017] S32: Calculate the global parameters, which are the overall statistical characteristic values ​​of the RC parameter model;

[0018] S33: Calculate the stage parameters for each stage;

[0019] S34: Evaluate parameter fluctuations based on stage parameters;

[0020] S35: If the parameter fluctuation exceeds the first threshold, increase the stage number and return to S31; otherwise, lock the current stage number N.

[0021] Preferably, the average value of the overall characteristics includes:

[0022]

[0023] In the formula, For the whole Number of points , , The battery internal resistance, diffusion resistance, and diffusion capacitance are respectively measured in the th... indivual The internal resistance, diffusion resistance, and diffusion capacitance of the battery at the point. , , These are the battery internal resistance, diffusion resistance, and diffusion capacitance in the full range. The average value of the points.

[0024] Preferably, the maximum deviation of the variable parameters of the overall feature include:

[0025]

[0026] In the formula, For the first indivual Maximum deviation of battery internal resistance under different stages For the first indivual Maximum deviation of diffusion resistance under stage, For the first indivual Maximum deviation of diffusion capacitance under stage.

[0027] Preferably, the standard deviation of the variable parameter of the overall feature include:

[0028]

[0029] In the formula, For the first indivual Standard deviation of battery internal resistance under different stages. For the first indivual Standard deviation of diffusion resistance under different stages For the first indivual Standard deviation of diffusion capacitance under different stages.

[0030] Preferably, the set energy storage battery The number of range stages is , put the battery Divided into Calculate the variable parameters of the energy storage battery in each stage:

[0031]

[0032] In the formula, For energy storage batteries stage and , For the first indivual stage Number of points , , The first indivual The internal resistance, diffusion resistance, and diffusion capacitance of the energy storage battery at the point of view. , , The first indivual The average values ​​of the internal resistance, diffusion resistance, and diffusion capacitance of the energy storage battery at each stage.

[0033] Preferably, calculate the first indivual Maximum deviation of variable parameters in staged energy storage battery VPRC model :

[0034]

[0035] In the formula, For the first indivual Maximum deviation of battery internal resistance under different stages For the first indivual Maximum deviation of diffusion resistance under stage, For the first indivual Maximum deviation of diffusion capacitance under stage.

[0036] Preferably, calculate the first indivual Standard deviation of variable parameters in the VPRC model of staged energy storage battery :

[0037]

[0038] In the formula, For the first indivual Standard deviation of battery internal resistance under different stages. For the first indivual Standard deviation of diffusion resistance under different stages For the first indivual Standard deviation of diffusion capacitance under different stages.

[0039] Preferably, S35 will fully The maximum deviation and standard deviation of the energy storage battery model parameters under the range and the SPEA algorithm are compared. If the following first threshold condition is met:

[0040]

[0041] Then proceed to S4; otherwise, recalculate the stage number. = +1, and enter S31.

[0042] Preferably, the specific steps of step 2 are as follows:

[0043] S21: Define the current relationship in the VPRC model of an energy storage battery:

[0044]

[0045] In the formula, To output current to the energy storage battery, This represents the voltage value across the diffusion resistor / capacitor.

[0046] S22: Discretize the current relationship in the above VPRC model of the energy storage battery:

[0047] ;

[0048] S23: Calculate the state of charge of the VPRC model of the energy storage battery. :

[0049]

[0050] In the formula, Defined as the total design capacity of energy storage batteries. This is the ratio of the current usable capacity of the energy storage battery to its maximum charge / discharge capacity. This represents the initial charge of the energy storage battery.

[0051] S24: Discretize the state of charge of the above energy storage battery VPRC model:

[0052] ;

[0053] S25: Based on the current relationship and state of charge of the energy storage battery, the state-space equation of the VPRC model of the energy storage battery is obtained:

[0054] ;

[0055] S26: Step 2.6: Calculate the output voltage of the energy storage battery VPRC model :

[0056] .

[0057] (III) Beneficial Effects

[0058] This invention provides a method for estimating the state of charge (SOC) of an energy storage battery. It has the following advantages:

[0059] This method for estimating the state of charge (SOC) of an energy storage battery first establishes a variable RC parameter model of the battery and lists the state-space equations of the VPRC model, thus obtaining the state-space equations of the battery's output voltage and SOC. A staged parameter estimation method is proposed, calculating parameters such as the battery's variable internal resistance, diffusion resistance, and diffusion capacitance in different zones. The VPRC model is then corrected using the results of the zoned variable parameter calculations. Finally, the SOC of the battery is estimated based on the zoned variable parameters, and the SOC is updated by calculating parameters such as the error covariance and gain coefficient. Attached Figure Description

[0060] Figure 1 This is a flowchart of the present invention;

[0061] Figure 2 This is a schematic diagram of the VPRC model circuit of the energy storage battery of the present invention. Detailed Implementation

[0062] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0063] In this embodiment, a novel method for real-time estimation of the state of charge of an energy storage battery is described, such as... Figure 1 As shown, the specific steps of the novel real-time estimation method for the state of charge of energy storage batteries are as follows:

[0064] Step 1: Build a variable RC parameter (VPRC) model for the energy storage battery. The specific model structure is as follows: Figure 2 As shown: The internal resistance of the variable battery is... It is a variable diffusion resistance. For variable diffusion capacitor, This is the battery open-circuit voltage. and After parallel connection with Connected, and then with Series;

[0065] Step 2: List the state-space equations of the VPRC model of the energy storage battery. The specific steps are as follows:

[0066] Step 2.1: Define the current relationship in the VPRC model of the energy storage battery:

[0067]

[0068] In the formula, To output current to the energy storage battery, It is a variable diffusion resistance. For variable diffusion capacitor, The voltage values ​​for the diffusion resistor and diffusion capacitor are given. It is a differential operator;

[0069] Step 2.2: Discretize the current relationship of the above energy storage battery VPRC model:

[0070]

[0071] In the formula, For discrete system step marking, For the output current of the energy storage battery in the discrete system, Let be the voltage values ​​of the diffusion resistance and diffusion capacitance of the discrete system. For time step;

[0072] Step 2.3: Calculate the state of charge of the VPRC model of the energy storage battery. :

[0073]

[0074] In the formula, Defined as the total design capacity of energy storage batteries. This is the ratio of the current usable capacity of the energy storage battery to its maximum charge / discharge capacity. This represents the initial charge of the energy storage battery.

[0075] Step 2.4: Discretize the state of charge (SOC) of the above energy storage battery VPRC model:

[0076]

[0077] Step 2.5: Based on the current relationship and state of charge of the energy storage battery, obtain the state-space equation of the VPRC model of the energy storage battery:

[0078]

[0079] Step 2.6: Calculate the output voltage of the VPRC model of the energy storage battery. :

[0080]

[0081] In the formula, This is the battery open-circuit voltage. For partial differential operators, The internal resistance of the variable battery;

[0082] Step 3: Propose a staged parameter estimation method (SPEA) to calculate the variable RC parameters of the energy storage battery in different zones. The specific operation is as follows:

[0083] Step 3.1: In the VPRC model of the energy storage battery , , Set them as variable parameters, and calculate each variable parameter in the whole. The parameter value below:

[0084]

[0085] In the formula, For the whole Number of points , , The battery internal resistance, diffusion resistance, and diffusion capacitance are respectively measured in the th... indivual The internal resistance, diffusion resistance, and diffusion capacitance of the battery at the point. , , These are the battery internal resistance, diffusion resistance, and diffusion capacitance in the full range. The average value of the points;

[0086] Step 3.2: Calculate in the whole Maximum deviation of variable parameters in the VPRC model of the energy storage battery at the point of application :

[0087]

[0088] In the formula, For the whole Tap the maximum deviation of the battery's internal resistance. For the whole The maximum deviation of the diffusion resistance is indicated. For the whole The maximum deviation of the diffusion capacitance is measured.

[0089] Step 3.3: Calculate in the whole Standard deviation of variable parameters in the VPRC model of energy storage battery :

[0090]

[0091] In the formula, For the whole The standard deviation of the battery internal resistance at the specified point. For the whole Click on the standard deviation value of the diffusion resistance. For the whole Click on the standard deviation value of the diffusion capacitance;

[0092] Step 3.4: Configure the energy storage battery The number of range stages is , put the battery Divided into Calculate the variable parameters of the energy storage battery in each stage:

[0093]

[0094] In the formula, For energy storage batteries stage and , For the first indivual stage Number of points , , The first indivual The internal resistance, diffusion resistance, and diffusion capacitance of the energy storage battery at the point of view. , , The first indivual The average values ​​of the internal resistance, diffusion resistance, and diffusion capacitance of the energy storage battery at each stage;

[0095] Step 3.5: Calculate the first... indivual Maximum deviation of variable parameters in staged energy storage battery VPRC model :

[0096]

[0097] In the formula, For the first indivual Maximum deviation of battery internal resistance under different stages For the first indivual Maximum deviation of diffusion resistance under stage, For the first indivual Maximum deviation of diffusion capacitance under stage;

[0098] Step 3.6: Calculate the first... indivual Standard deviation of variable parameters in the VPRC model of staged energy storage battery :

[0099]

[0100] In the formula, For the first indivual Standard deviation of battery internal resistance under different stages. For the first indivual Standard deviation of diffusion resistance under different stages For the first indivual Standard deviation of diffusion capacitance under different stages;

[0101] Step 3.7: Put all Compare the maximum deviation and standard deviation of the energy storage battery model parameters under the range and the SPEA algorithm. If the following conditions are met:

[0102]

[0103] In the formula, For the whole Tap the maximum deviation of the battery's internal resistance. For the whole The maximum deviation of the diffusion resistance is indicated. For the whole The maximum deviation of the diffusion capacitance is indicated. For the whole The standard deviation of the battery internal resistance at the specified point. For the whole Click on the standard deviation value of the diffusion resistance. For the whole Click on the standard deviation value of the diffusion capacitance. For the first indivual Maximum deviation of battery internal resistance under different stages For the first indivual Maximum deviation of diffusion resistance under stage, For the first indivual Maximum deviation of diffusion capacitance under stage For the first indivual Standard deviation of battery internal resistance under different stages. For the first indivual Standard deviation of diffusion resistance under different stages For the first indivual Standard deviation of diffusion capacitance under different stages;

[0104] Then proceed to step 4.1; otherwise, recalculate the stage number. = +1, and proceed to step 3.4;

[0105] Step 4: Estimate the state of charge of the energy storage battery based on the variable parameters of the energy storage battery zone. The specific operation is as follows:

[0106] Step 4.1: Calculate the estimated state of charge (SOC) of the energy storage battery based on the variable parameters.

[0107]

[0108] Step 4.2: Calculate the systematic error covariance :

[0109]

[0110] In the formula, The system noise covariance;

[0111] Step 4.3: Calculate the output voltage value using the output equation:

[0112]

[0113] Step 4.4: Calculate the system gain coefficient :

[0114]

[0115] In the formula, To observe the noise covariance, The sign for conjugate transpose;

[0116] Step 4.5: Update the systematic error covariance:

[0117]

[0118] Step 4.6: Real-time estimation of energy storage battery state-of-charge parameters:

[0119]

[0120] In the formula, To estimate the output voltage value, This is the gain coefficient.

[0121] System noise covariance Covariance of observation noise The parameters can be obtained by adjusting the noise statistical characteristics or through trial and error.

[0122] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0123] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for estimating the state of charge of an energy storage battery, characterized in that, include: S1: Build a variable RC parameter model for the energy storage battery; The variable RC parameter model includes: variable battery internal resistance. Variable diffusion resistance Variable diffusion capacitor Battery open circuit voltage ,in and After parallel connection with Connected, and then with Series; S2: List the state-space equations of the energy storage battery based on the variable RC parameter model; S3: Calculate the variable RC parameters of the energy storage battery by region according to the staged parameter estimation method; S4: Estimate the state of charge of the energy storage battery based on the variable RC parameters; The staged parameter estimation method includes the following steps: S31: Divide the SOC range of the energy storage battery into N stages; S32: Calculate the global parameters, which are the overall statistical characteristic values ​​of the RC parameter model; S33: Calculate the stage parameters for each stage; S34: Evaluate parameter fluctuations based on stage parameters; S35: If the parameter fluctuation exceeds the first threshold, increase the stage number and return to S31; otherwise, lock the current stage number N.

2. The method for estimating the state of charge of an energy storage battery according to claim 1, characterized in that: The average value of the overall characteristics includes: ； In the formula, For the whole Number of points , , The battery internal resistance, diffusion resistance, and diffusion capacitance are respectively measured in the th... indivual The internal resistance, diffusion resistance, and diffusion capacitance of the battery at the point. , , These are the battery internal resistance, diffusion resistance, and diffusion capacitance in the full range. The average value of the points.

3. The method for estimating the state of charge of an energy storage battery according to claim 2, characterized in that: The maximum deviation of the variable parameters of the overall feature include: ； In the formula, For the whole Tap the maximum deviation of the battery's internal resistance. For the whole The maximum deviation of the diffusion resistance is indicated. For the whole The maximum deviation of the diffusion capacitance is measured.

4. The method for estimating the state of charge of an energy storage battery according to claim 3, characterized in that: The standard deviation of the variable parameter of the overall feature include: ； In the formula, For the whole The standard deviation of the battery internal resistance at the specified point. For the whole Click on the standard deviation value of the diffusion resistance. For the whole Click on the standard deviation value of the diffusion capacitance.

5. The method for estimating the state of charge of an energy storage battery according to claim 4, characterized in that: The set energy storage battery The number of range stages is , put the battery Divided into Calculate the variable parameters of the energy storage battery in each stage: ； In the formula, For energy storage batteries stage and , For the first indivual stage Number of points , , The first indivual The internal resistance, diffusion resistance, and diffusion capacitance of the energy storage battery at the point of view. , , The first indivual The average values ​​of the internal resistance, diffusion resistance, and diffusion capacitance of the energy storage battery at each stage.

6. The method for estimating the state of charge of an energy storage battery according to claim 5, characterized in that: Calculate the first indivual Maximum deviation of variable parameters in staged energy storage battery VPRC model : ； In the formula, For the first indivual Maximum deviation of battery internal resistance under different stages For the first indivual Maximum deviation of diffusion resistance under stage, For the first indivual Maximum deviation of diffusion capacitance under stage.

7. The method for estimating the state of charge of an energy storage battery according to claim 6, characterized in that: Calculate the first indivual Standard deviation of variable parameters in the VPRC model of staged energy storage battery : ； In the formula, For the first indivual Standard deviation of battery internal resistance under different stages. For the first indivual Standard deviation of diffusion resistance under different stages For the first indivual Standard deviation of diffusion capacitance under different stages.

8. The method for estimating the state of charge of an energy storage battery according to claim 7, characterized in that: S35 will be fully The maximum deviation and standard deviation of the energy storage battery model parameters under the range and the SPEA algorithm are compared. If the following first threshold condition is met: ； Then proceed to S4; otherwise, recalculate the stage number. = +1, and enter S31.

9. The method for estimating the state of charge of an energy storage battery according to claim 1, characterized in that: The specific steps of S2 are as follows: S21: Define the current relationship in the VPRC model of an energy storage battery: ； In the formula, To output current to the energy storage battery, This represents the voltage value across the diffusion resistor / capacitor. S22: Discretize the current relationship in the above VPRC model of the energy storage battery: ； S23: Calculate the state of charge of the VPRC model of the energy storage battery. : ； In the formula, Defined as the total design capacity of energy storage batteries. This is the ratio of the current usable capacity of the energy storage battery to its maximum charge / discharge capacity. This represents the initial charge of the energy storage battery. S24: Discretize the state of charge of the above energy storage battery VPRC model: ； S25: Based on the current relationship and state of charge of the energy storage battery, the state-space equation of the VPRC model of the energy storage battery is obtained: ； S26: Calculate the output voltage of the VPRC model of the energy storage battery. : 。

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