A single state variable soc estimation method and apparatus

Abstract

The application relates to the technical field of battery management, and provides a single-state-variable SOC estimation method, which comprises the following steps: inputting an SOC value of an initialized lithium battery and an actual current value of the lithium battery into a single-particle model of the lithium battery to obtain an output voltage value of the lithium battery; the single-particle model comprises a single particle and an extended model thereof, and a thermal coupling model based on a single-particle correlation model; comparing the output voltage value of the lithium battery with an actual voltage value of the lithium battery to obtain an initial SOC estimation value of a single-state variable of the lithium battery; and filtering the initial SOC estimation value of the single-state variable through an extended Kalman filter to obtain a target SOC estimation value of the single-state variable. The application can accurately estimate the volume average concentration and the SOC value of the active material inside the lithium battery based on the single-particle model and the electrochemical model of the extended model thereof (or the electrothermal coupling model thereof).

Description

Technical Field

[0001] This invention relates to the field of battery management technology, and more particularly to a method and apparatus for estimating the state of charge (SOC) of a single state variable. Background Technology

[0002] Energy storage power stations use a large number of lithium batteries. Estimating their state of charge (SOC) is not only an important function of the battery management system (BMS), but the SOC value is also an important parameter of the performance of energy storage power stations. Accurate estimation of SOC can significantly improve the performance of batteries and maximize the regulation role and economic value of power stations in frequency regulation energy storage and "peak shaving and valley filling".

[0003] Currently, mainstream SOC estimation methods rely on macroscopic physical quantities such as current and voltage for prediction. Commonly used methods include the open-circuit voltage method and the ampere-hour integration method. The ampere-hour integration method requires integrating the charging or discharging current over a period of time and summing it with the initial charge level. Its accuracy depends on the accuracy of the initial SOC, but in real-world operating conditions, it's difficult to know the battery's initial SOC, thus reducing its accuracy. The open-circuit voltage method measures the battery's open-circuit voltage (OCV) corresponding to each SOC value, establishing a mapping relationship between the battery's OCV and SOC. However, because measuring the open-circuit voltage requires disconnecting the battery from the external circuit and measuring after a certain time interval, it's not suitable for online prediction. Summary of the Invention

[0004] The purpose of this invention is to provide a single-state variable SOC estimation method to solve the above-mentioned problems.

[0005] The technical solution provided by this invention is as follows:

[0006] In some embodiments, the present invention provides a SOC estimation method for a single-state variable, comprising:

[0007] Input the initial SOC value of the lithium battery and the actual current value of the lithium battery into the single-particle model of the lithium battery to obtain the output voltage value of the lithium battery;

[0008] By comparing the output voltage value of the lithium battery with the actual voltage value of the lithium battery, the initial SOC estimate of the single-state variable of the lithium battery is obtained;

[0009] The initial SOC estimate of the single-state variable is filtered by an extended Kalman filter to obtain the target SOC estimate of the single-state variable.

[0010] In some implementations, it also includes:

[0011] Input the initial SOC value of the lithium battery and the actual current value of the lithium battery into the single-particle model of the lithium battery to obtain the initial volume concentration of the positive electrode and then the initial volume concentration of the negative electrode of the lithium battery.

[0012] The initial concentration of the positive electrode and the initial concentration of the negative electrode of the lithium battery are initialized based on the SOC value of the initial lithium battery.

[0013] Based on the initialized initial concentration of the negative electrode volume and the initialized initial concentration of the positive electrode volume, the average concentration of the positive electrode volume and the average concentration of the negative electrode volume are obtained.

[0014] In some embodiments, the initialization process of the positive and negative electrode volume concentrations of the lithium battery based on the initial SOC value of the lithium battery includes:

[0015] The formula for initialization is:

[0016] C n，ini =C n，0 +SOC×(C n，100 -C n，0 )

[0017] C p，ini =C p，0 +SOC×(C p，100 -C p，0 )

[0018] Among them, C n，ini The initial concentration of the negative electrode after initialization, C n，100 C represents the initial volumetric concentration of the negative electrode when the actual SOC value is 100%. n，0 The initial concentration of the negative electrode volume when the actual SOC value = 0; C p，ini The initial concentration of the positive electrode after initialization, C p，100 Cp is the initial positive electrode volume concentration when the actual SOC value is 100%, 0 is the initial positive electrode volume concentration when the actual SOC value is 0, and SOC is the actual SOC value.

[0019] In some implementations, the step of inputting the initial SOC value and the actual current value of the lithium battery into a single-event model of the lithium battery to obtain the output voltage value of the lithium battery includes:

[0020] The positive electrode volume average concentration or the negative electrode volume average concentration is selected as the state variable.

[0021] The governing equations for solid phase concentration and boundary conditions for calculating state variables based on the single-particle model of the lithium battery;

[0022] The output voltage value of the lithium battery is calculated based on the state variables.

[0023] In some embodiments, the governing equations for solid phase concentration and the boundary condition calculations for state variables based on the single-particle model of the lithium battery include:

[0024] The governing equation for the solid phase concentration in the single-particle model of the lithium battery is:

[0025]

[0026] Where r is the length along the radial direction of the solid-phase active material, and D s Where C is the diffusion coefficient, C is the solid concentration, and t is the time.

[0027] The boundary conditions are as follows:

[0028]

[0029] Where, j n R is the molar flux of lithium ions per unit area per unit time; R is the radius of the active material.

[0030] Let the lithium ion concentration on the negative electrode surface be C. n，s ,but:

[0031]

[0032]

[0033] Among them, C n，avg R is the negative electrode volume average concentration. n The radius is the negative pole.

[0034] In some implementations, calculating the output voltage value of the lithium battery based on the system state variables includes:

[0035] The formula for calculating the output voltage of the lithium battery is as follows:

[0036] V = U P -U n +η p -η n -I*R

[0037] V = h(x, u)

[0038] Where x is the system state variable, and u is the input quantity; U P U n The equilibrium potential between the positive and negative electrodes, η p η n The reaction overpotential, I is the current value, R is the empirical resistance, and h is a function;

[0039] Choose C n，avg Let A be the system state variable, then A = [1], and the observation matrix is ​​obtained:

[0040]

[0041] Where, v = U P -U n +η p -η n -I*R is the observation equation.

[0042] In some embodiments, the step of comparing the output voltage value of the lithium battery with the actual voltage value of the lithium battery to obtain the initial SOC estimate of the single-state variable of the lithium battery includes:

[0043] Based on the initial negative electrode volume average concentration C n，ini The initial positive electrode volume average concentration is calculated using the following formula:

[0044]

[0045] Among them, L n For the thickness of the negative electrode active material, L p ε represents the thickness of the positive electrode active material. n ε represents the volume fraction of the negative electrode active material. p c is the volume fraction of the negative electrode active material. p，100 c is the positive electrode volume average concentration when the actual SOC value is 100%. n，100 This represents the negative electrode volume average concentration when the actual SOC value is 100%.

[0046] Based on the initial positive electrode volume average concentration and the output voltage value, the initial SOC estimate for a single-state variable is calculated as follows:

[0047]

[0048] Where x is the state variable, - sign indicates prior estimate, + sign indicates posterior estimate, k represents time, and Δt represents sampling time;

[0049] Calculate the prior error covariance:

[0050]

[0051] Where P is the state variable error covariance matrix, which outputs the initial SOC estimate during initialization; Q is the process noise covariance, which outputs the initial SOC estimate when updating the state variable error; A T Let be the transpose of matrix A.

[0052] In some implementations, the step of filtering the initial SOC estimate of the single-state variable using an extended Kalman filter to obtain the target SOC estimate of the single-state variable includes:

[0053] The state variables are updated using the extended Kalman filter, and the update formula for the state variables is as follows:

[0054]

[0055] Among them, z k This is the actual voltage value. The voltage value is obtained by substituting the initial SOC estimate and the actual SOC value at time k into the observation equation;

[0056] Among them, K k Calculated using the following formula:

[0057]

[0058] Where R is the measurement error covariance, and H T K is the transpose of matrix H; k This is the estimated value of the target SOC.

[0059] In some embodiments, the present invention also provides a single-state variable SOC estimation device, comprising:

[0060] The data input module is used to input the initial SOC value of the lithium battery and the actual current value of the lithium battery into the single-particle model of the lithium battery to obtain the output voltage value of the lithium battery.

[0061] The initial estimation module is used to compare the output voltage value of the lithium battery with the actual voltage value of the lithium battery to obtain the initial SOC estimate of the single-state variable of the lithium battery.

[0062] The filtering estimation module is used to filter the initial SOC estimate of the single-state variable through extended Kalman filtering to obtain the target SOC estimate of the single-state variable.

[0063] In some implementations, an initial processing module is also included, for:

[0064] Input the initial SOC value of the lithium battery and the actual current value of the lithium battery into the single-particle model of the lithium battery to obtain the initial volume concentration of the positive electrode and then the initial volume concentration of the negative electrode of the lithium battery.

[0065] The initial concentration of the positive electrode and the initial concentration of the negative electrode of the lithium battery are initialized based on the SOC value of the initial lithium battery.

[0066] Based on the initialized initial concentration of the negative electrode volume and the initialized initial concentration of the positive electrode volume, the average concentration of the positive electrode volume and the average concentration of the negative electrode volume are obtained.

[0067] Compared with the prior art, the SOC estimation method and apparatus for a single-state variable provided by the present invention can bring the following beneficial effects:

[0068] This invention accurately estimates the internal volume average concentration and SOC value of active materials in lithium batteries based on single-particle models and their extended electrochemical models (or their electrothermal coupling models). Attached Figure Description

[0069] The preferred embodiments will now be described in a clear and easy-to-understand manner, with reference to the accompanying drawings, to further explain the above-mentioned characteristics, technical features, advantages, and implementation methods of a single-state variable SOC estimation method and apparatus.

[0070] Figure 1 This is a flowchart of an embodiment of a single-state variable SOC estimation method according to the present invention;

[0071] Figure 2 This is a schematic diagram of voltage changes according to the present invention;

[0072] Figure 3 This is a schematic diagram of the filtered SOC estimate of the present invention;

[0073] Figure 4 This is a schematic diagram of an embodiment of a single-state variable SOC estimation device according to the present invention. Detailed Implementation

[0074] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the specific implementation methods of the present invention will be described below with reference to the accompanying drawings. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings and other implementation methods can be obtained based on these drawings without any creative effort.

[0075] To keep the drawings concise, only the parts relevant to the invention are shown schematically in each figure, and they do not represent the actual structure of the product. Furthermore, for ease of understanding, in some figures, only one of components with the same structure or function is shown schematically, or only one is labeled. In this document, "one" can mean not only "only one" but also "more than one".

[0076] In one embodiment, such as Figure 1 As shown, this invention provides a SOC estimation method for a single-state variable, comprising:

[0077] S101 inputs the initial SOC value of the lithium battery and the actual current value of the lithium battery into the single-particle model of the lithium battery to obtain the output voltage value of the lithium battery.

[0078] Among them, single-particle models include single-particle and its extended models, as well as thermally coupled models based on single-particle related models, such as SPM, SPME, and ESPM.

[0079] Single particle model

[0080] single particle model with electrolyte,

[0081] Enhanced single particle model, etc.

[0082] S102 compares the output voltage value of the lithium battery with the actual voltage value of the lithium battery to obtain the initial SOC estimate of the single-state variable of the lithium battery.

[0083] S103 filters the initial SOC estimate of the single-state variable using an extended Kalman filter to obtain the target SOC estimate of the single-state variable.

[0084] In this embodiment, the present invention accurately estimates the internal volume average concentration and SOC value of lithium battery active materials based on the single-particle model and its extended model electrochemical model (or its electrothermal coupling model).

[0085] In one embodiment, it also includes:

[0086] Input the initial SOC value of the lithium battery and the actual current value of the lithium battery into the single-particle model of the lithium battery to obtain the initial volume concentration of the positive electrode and then the initial volume concentration of the negative electrode of the lithium battery.

[0087] The initial concentration of the positive electrode and the initial concentration of the negative electrode of the lithium battery are initialized based on the SOC value of the initial lithium battery.

[0088] Based on the initialized initial concentration of the negative electrode volume and the initialized initial concentration of the positive electrode volume, the average concentration of the positive electrode volume and the average concentration of the negative electrode volume are obtained.

[0089] In one embodiment, the initialization process of the positive and negative electrode volume concentrations of the lithium battery based on the initial SOC value of the lithium battery includes:

[0090] The formula for initialization is:

[0091] C n，ini =C n，0 +SOC×(C n，100 -C n，0 )

[0092] C p，ini =C p，0 +SOC×(C p，100 -C p，0 )

[0093] Among them, C n，ini The initial concentration of the negative electrode after initialization, C n，100 C represents the initial volumetric concentration of the negative electrode when the actual SOC value is 100%. n，0 The initial concentration of the negative electrode volume when the actual SOC value = 0; C p，ini The initial concentration of the positive electrode after initialization, C p，100 The initial concentration C of the positive electrode volume when the actual SOC value is 100% p，0 The initial concentration of the positive electrode volume when the actual SOC value is 0; SOC is the actual SOC value.

[0094] In one embodiment, the step of inputting the initial SOC value and the actual current value of the lithium battery into the single-particle model of the lithium battery to obtain the output voltage value of the lithium battery includes:

[0095] The positive electrode volume average concentration or the negative electrode volume average concentration is selected as the state variable.

[0096] The governing equations for solid phase concentration and boundary conditions for calculating state variables based on the single-particle model of the lithium battery;

[0097] The output voltage value of the lithium battery is calculated based on the state variables.

[0098] In one embodiment, the governing equations for solid phase concentration and the calculation of boundary conditions for state variables based on the single-particle model of the lithium battery include:

[0099] The governing equation for the solid phase concentration in the single-particle model of the lithium battery is:

[0100]

[0101] Where r is the length in the radial direction of the solid-phase active material, and D s Where C is the diffusion coefficient, C is the solid concentration, and t is time;

[0102] The boundary conditions are as follows:

[0103]

[0104] Where, j n R is the molar flux of lithium ions per unit area per unit time; R is the radius of the active material.

[0105] Let the lithium ion concentration on the negative electrode surface be C. n，s ,but:

[0106]

[0107]

[0108] Among them, C n，avg R is the negative electrode volume average concentration. n The radius is the negative pole.

[0109] In one embodiment, calculating the output voltage value of the lithium battery based on the system state variables includes:

[0110] The formula for calculating the output voltage of the lithium battery is as follows:

[0111] V = U P -U n +η p -η n -I*R

[0112] V = h(x, u)

[0113] Where x is the system state variable, and u is the input quantity; U P U n The equilibrium potential between the positive and negative electrodes, η p η n The reaction overpotential, I is the current value, R is the empirical resistance, and h is a function;

[0114] Choose C n，avg Let A be the system state variable, then A = [1], and the observation matrix is ​​obtained:

[0115]

[0116] Where V = U P -U n +η p -ηn-I*R is the observation equation.

[0117] In one embodiment, the step of comparing the output voltage value of the lithium battery with the actual voltage value of the lithium battery to obtain the initial SOC estimate of the single-state variable of the lithium battery includes:

[0118] Based on the initial negative electrode volume average concentration C n，ini The initial positive electrode volume average concentration is calculated using the following formula:

[0119]

[0120] Among them, L n For the thickness of the negative electrode active material, L p ε represents the thickness of the positive electrode active material. n ε represents the volume fraction of the negative electrode active material. p c is the volume fraction of the negative electrode active material. p，100 c is the positive electrode volume average concentration when the actual SOC value is 100%. n，100 This represents the negative electrode volume average concentration when the actual SOC value is 100%.

[0121] Based on the initial positive electrode volume average concentration and the output voltage value, the initial SOC estimate for a single-state variable is calculated as follows:

[0122]

[0123] Where x is the state variable, - sign indicates prior estimate, + sign indicates posterior estimate, k represents time, and Δt represents sampling time;

[0124] Calculate the prior error covariance:

[0125]

[0126] Where P is the state variable error covariance matrix, which outputs the initial SOC estimate during initialization; Q is the process noise covariance, which outputs the initial SOC estimate when updating the state variable error; A T Let be the transpose of matrix A.

[0127] In one embodiment, filtering the initial SOC estimate of the single-state variable using an extended Kalman filter to obtain the target SOC estimate of the single-state variable includes:

[0128] The state variables are updated using the extended Kalman filter, and the update formula for the state variables is as follows:

[0129]

[0130] Among them, z k This is the actual voltage value. The voltage value is obtained by substituting the initial SOC estimate and the actual SOC value at time k into the observation equation;

[0131] Among them, K k Calculated using the following formula:

[0132]

[0133] Where R is the measurement error covariance, and HT K is the transpose of matrix H; k This is the estimated value of the target SOC.

[0134] In one embodiment, the present invention provides a single-state variable SOC estimation method, comprising:

[0135] This method is suitable for predicting the SOC of batteries at low rates (generally not exceeding 0.3C). It involves only a single state variable and has the advantages of simple calculation and fast speed.

[0136] This invention provides a method for accurately estimating the volume-average concentration and state of charge (SOC) of active materials in lithium batteries based on a single-particle model and its extended electrochemical model (or its electrothermal coupling model). First, an electrochemical model is established for the battery, and the electrochemical model parameters are obtained (generally identified through parameter identification; specific details are beyond the scope of this patent). Then, the concentration, i.e., the SOC, is estimated using EKF filtering. The specific implementation method is as follows:

[0137] The case study uses a certain NMC ternary battery as an example:

[0138] Step 1:

[0139] An electrochemical model of the battery was established to obtain the battery SPM model parameters, focusing on the initial concentrations of the positive and negative electrodes when the battery SOC = 100% and SOC = 0, and C. n，100 C n，0 C p，100 C p，0 .

[0140] Step 2:

[0141] The positive and negative electrode concentrations of the model are initialized based on the battery data used for SOC estimation; if the data includes the SOC values ​​uploaded by the BMS, the initial concentrations of the positive and negative electrodes are initialized using the following formula:

[0142] C n，ini =C n，0 +SOC×(C n，100 -C n，0 )

[0143] C p，ini =C p，0 +SOC×(C p，100 -C p，0 )

[0144] If the data does not contain SOC values, the positive and negative electrodes can be initialized to SOC = 100% or the concentration corresponding to any SOC value, as shown in the formula above.

[0145] Step 3:

[0146] SOC is estimated using an Extended Kalman Filter (EKF).

[0147] 3.1 State Variables

[0148] The system state variable is selected by choosing either the positive or negative electrode volume average concentration. This patent takes the negative electrode volume average concentration as an example, and its symbol is: C. n，avg C p，avg ,Right now:

[0149] x = C n，avg

[0150] 3.2 Approximate Solution of SPM Model

[0151] The SPM model neglects changes in liquid phase concentration and liquid phase potential, considering only solid phase concentration. Its governing equation is:

[0152]

[0153] The concentrations of both the positive and negative electrodes follow the formula above, where r is the length of the solid active material along its radius, and D... s Let be the diffusion coefficient, and C be the solid concentration. The boundary conditions are:

[0154]

[0155] Where, j n R represents the molar flux of lithium ions per unit area per unit time, and R is the radius of the active material.

[0156] It is obtained through a two-parameter approximation, assuming the surface lithium ion concentration is C. n，s ,but:

[0157] Formula 1:

[0158]

[0159] Formula 2:

[0160]

[0161] Formula 3:

[0162] Voltage is calculated using the following formula:

[0163] V = U P -U n +η p -η n -I*R

[0164] Let V = h(x, u);

[0165] Where x is the state variable and u is the input current.

[0166] Among them, U P U n The equilibrium potential between the positive and negative electrodes, η p η n I represents the overpotential of the positive and negative electrode reactions, I represents the current, and R represents the empirical resistance.

[0167] In this embodiment, I is the current value, and u also refers to the current value. In essence, it is also I. It is just that in Kalman filtering, it is customary to use h(x, u) to represent the observation equation, where x represents the state variable and u represents the input quantity, i.e., the input current value.

[0168] 3.3 State transition matrix, observation matrix

[0169] Choose C n，avg If A is the system state variable, then A = [1], V = U P -U n +η p -η n -I*R is the observation equation.

[0170]

[0171] 3.4 Prior Estimation

[0172] Based on the initial negative electrode volume average concentration C n，ini C is calculated using the following formula. p，ini :

[0173]

[0174] Among them, L n ε represents the thickness of the negative electrode active material. n denoted as the volume fraction of the negative electrode active material, and p represents the positive electrode material.

[0175] Because initially, C n，s C p，s With C n，ini C p，ini The prior estimated voltage value is calculated using Formula 3.

[0176] Then, prior estimation of the state variables is performed according to Formula 1, that is:

[0177]

[0178] Where - indicates prior estimation, + indicates posterior estimation, k represents time, and Δt represents sampling time.

[0179] Calculate the prior error covariance:

[0180]

[0181] Where P is the state variable error covariance matrix, which is given an initial value during initialization, and Q is the process noise covariance, which is the error caused by updating the state variables according to Formula 1, and is given an initial value during initialization.

[0182] 3.5 Posterior estimation

[0183] According to EKF filtering, the update formula for the state variables is as follows:

[0184]

[0185] Update the state variables, where z k This refers to the measured voltage value, specifically the voltage value from the battery data described in step 2. The voltage value is obtained by substituting the prior estimate and the input value (current) at time k into the observation equation.

[0186] Among them, K k Calculated using the following formula:

[0187]

[0188] Where R is the measurement error covariance, which is the error in measuring the voltage and current values ​​in the battery data described in step 2 using a measuring instrument or sensor.

[0189] 3.6 SOC Estimation

[0190] By repeatedly calculating 3.5 and 3.6, the battery SOC value can be estimated using EKF filtering.

[0191] This invention relates to a method for estimating the state of charge (SOC) of a battery. The method involves establishing a single-particle model and its extended electrochemical model of the battery. First, the concentration values ​​of the model (i.e., SOC, or SOC values ​​estimated from a battery microsystem analysis (BMS)) are initialized. The measured current values ​​are input into the electrochemical model. The output voltage of the electrochemical model is compared with the actual battery voltage. An extended Kalman filter (EKF) is used to correct the state variables (average solid-phase concentration) to obtain a more accurate SOC.

[0192] It should be noted that this method is applicable to the SOC prediction of batteries at low rates (generally not exceeding 0.3C). Since it only involves a single state variable, it has the advantages of simple calculation and fast speed.

[0193] In one embodiment, such as Figure 3 As shown, the present invention also provides a single-state variable SOC estimation device, comprising:

[0194] The data input module 101 is used to input the initial SOC value of the lithium battery and the actual current value of the lithium battery into the single-particle model of the lithium battery to obtain the output voltage value of the lithium battery.

[0195] The initial estimation module 102 is used to compare the output voltage value of the lithium battery with the actual voltage value of the lithium battery to obtain the initial SOC estimate of the single-state variable of the lithium battery.

[0196] The filtering estimation module 103 is used to filter the initial SOC estimate of the single-state variable through extended Kalman filtering to obtain the target SOC estimate of the single-state variable.

[0197] In one embodiment, it further includes: an initial processing module, configured to:

[0198] Input the initial SOC value of the lithium battery and the actual current value of the lithium battery into the single-particle model of the lithium battery to obtain the initial volume concentration of the positive electrode and then the initial volume concentration of the negative electrode of the lithium battery.

[0199] The initial concentration of the positive electrode and the initial concentration of the negative electrode of the lithium battery are initialized based on the SOC value of the initial lithium battery.

[0200] Based on the initialized initial concentration of the negative electrode volume and the initialized initial concentration of the positive electrode volume, the average concentration of the positive electrode volume and the average concentration of the negative electrode volume are obtained.

[0201] This invention accurately estimates the internal volume average concentration and SOC value of active materials in lithium batteries based on single-particle models and their extended electrochemical models (or their electrothermal coupling models).

[0202] It should be noted that the above embodiments can be freely combined as needed. The above description is only a preferred embodiment of the present invention. It should be pointed out that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for estimating the State of Computation (SOC) of a single-state variable, characterized in that, Applicable to low-rate battery SOC estimation, including: Input the initial SOC value of the lithium battery and the actual current value of the lithium battery into the single-particle model of the lithium battery to obtain the output voltage value of the lithium battery; By comparing the output voltage value of the lithium battery with the actual voltage value of the lithium battery, the initial SOC estimate of the single-state variable of the lithium battery is obtained; The initial SOC estimate of the single-state variable is filtered by extended Kalman filtering to obtain the target SOC estimate of the single-state variable. The process of inputting the initial SOC value and actual current value of the lithium battery into the single-event model of the lithium battery to obtain the output voltage value of the lithium battery includes: The positive electrode volume average concentration or the negative electrode volume average concentration is selected as the state variable. The governing equations for solid phase concentration and boundary conditions for calculating state variables based on the single-particle model of the lithium battery; The output voltage value of the lithium battery is calculated based on the state variables.

2. The SOC estimation method for a single-state variable according to claim 1, characterized in that, Also includes: Input the initial SOC value of the lithium battery and the actual current value of the lithium battery into the single-particle model of the lithium battery to obtain the initial volume concentration of the positive electrode and then the initial volume concentration of the negative electrode of the lithium battery. The initial concentration of the positive electrode and the initial concentration of the negative electrode of the lithium battery are initialized based on the SOC value of the initial lithium battery. Based on the initialized initial concentration of the negative electrode volume and the initialized initial concentration of the positive electrode volume, the average concentration of the positive electrode volume and the average concentration of the negative electrode volume are obtained.

3. The SOC estimation method for a single-state variable according to claim 2, characterized in that, The initialization process for the initial concentration of the positive and negative electrodes of the lithium battery based on the SOC value of the initial lithium battery includes: The formula for initialization is: ； ； in, The initial concentration of the negative electrode volume after initialization. This represents the initial volumetric concentration of the negative electrode when the actual SOC value is 100%. The initial concentration of the negative electrode volume when the actual SOC value = 0; The initial concentration of the positive electrode after initialization. The initial concentration of the positive electrode volume when the actual SOC value is 100% The initial concentration of the positive electrode volume when the actual SOC value is 0; SOC is the actual SOC value.

4. The SOC estimation method for a single-state variable according to claim 1, characterized in that, The governing equations for solid phase concentration and the calculation of boundary conditions for state variables based on the single-particle model of the lithium battery include: The governing equation for the solid phase concentration in the single-particle model of the lithium battery is: ； Where r is the length of the solid-phase active material along its radius. Where C is the diffusion coefficient, C is the solid concentration, and t is time; The boundary conditions are as follows: ； in, R is the molar flux of lithium ions per unit area per unit time; R is the radius of the active material. Let the lithium ion concentration on the negative electrode surface be... ,but: ； ； in, The negative electrode volume average concentration. The radius is the negative pole.

5. The SOC estimation method for a single-state variable according to claim 4, characterized in that, The calculation of the output voltage value of the lithium battery based on the system state variables includes: The formula for calculating the output voltage of the lithium battery is as follows: ； ； Where x is the state variable and u is the input quantity; , This is the equilibrium potential between the positive and negative electrodes. , Reaction overpotential, Let R be the current value, R be the empirical resistance, and h be a function; Select If the system state variables are given, then matrix A = [1], yielding the observation matrix: ； in, The equation is the observation equation.

6. The SOC estimation method for a single-state variable according to claim 5, characterized in that, The process of comparing the output voltage value of the lithium battery with the actual voltage value of the lithium battery to obtain the initial SOC estimate of the single-state variable of the lithium battery includes: Based on the initial negative electrode volume average concentration The initial positive electrode volume average concentration is calculated using the following formula: ； in, The thickness of the negative electrode active material, This refers to the volume fraction of the negative electrode active material. This represents the average volume concentration of the positive electrode when the actual SOC value is 100%. This represents the negative electrode volume average concentration when the actual SOC value is 100%. Based on the initial positive electrode volume average concentration and the output voltage value, the initial SOC estimate for a single-state variable is calculated as follows: ； Where x is the state variable, - indicates a prior estimate, + indicates a posterior estimate, and k represents time. Indicates the sampling time; Calculate the prior error covariance: ； Where P is the state variable error covariance matrix, which outputs the initial SOC estimate during initialization; Q is the process noise covariance, which outputs the initial SOC estimate when initializing the error of updating the state variables. Let be the transpose of matrix A.

7. The SOC estimation method for a single-state variable according to claim 6, characterized in that, The process of filtering the initial SOC estimate of the single-state variable using an extended Kalman filter to obtain the target SOC estimate of the single-state variable includes: The state variables are updated using the extended Kalman filter, and the update formula for the state variables is as follows: ； in, This is the actual voltage value. The voltage value is obtained by substituting the initial SOC estimate and the actual SOC value at time k into the observation equation; in, Calculated using the following formula: ； Where R is the measurement error covariance. Let H be the transpose of matrix H; This is the target SOC estimate.

8. A single-state variable SOC estimation device, characterized in that, include: The data input module is used to input the initial SOC value of the lithium battery and the actual current value of the lithium battery into the single-particle model of the lithium battery to obtain the output voltage value of the lithium battery. The initial estimation module is used to compare the output voltage value of the lithium battery with the actual voltage value of the lithium battery to obtain the initial SOC estimate of the single-state variable of the lithium battery. The filtering estimation module is used to filter the initial SOC estimate of the single-state variable through extended Kalman filtering to obtain the target SOC estimate of the single-state variable. The process of inputting the initial SOC value and actual current value of the lithium battery into the single-event model of the lithium battery to obtain the output voltage value of the lithium battery includes: The positive electrode volume average concentration or the negative electrode volume average concentration is selected as the state variable. The governing equations for solid phase concentration and boundary conditions for calculating state variables based on the single-particle model of the lithium battery; The output voltage value of the lithium battery is calculated based on the state variables.

9. The SOC estimation device for a single-state variable according to claim 8, characterized in that, Also includes: The initial processing module is used for: Input the initial SOC value of the lithium battery and the actual current value of the lithium battery into the single-particle model of the lithium battery to obtain the initial volume concentration of the positive electrode and then the initial volume concentration of the negative electrode of the lithium battery. The initial concentration of the positive electrode and the initial concentration of the negative electrode of the lithium battery are initialized based on the SOC value of the initial lithium battery. Based on the initialized initial concentration of the negative electrode volume and the initialized initial concentration of the positive electrode volume, the average concentration of the positive electrode volume and the average concentration of the negative electrode volume are obtained.

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