Lithium ion battery state-of-charge estimation method, equipment, medium and product

Abstract

The invention discloses a lithium ion battery state-of-charge estimation method and device, a medium and a product, and relates to the field of lithium batteries, the method comprises the following steps: constructing a constant current pseudo-steady state model based on an equivalent circuit model and an electrochemical mechanism model; adopting the constant-current pseudo-steady-state model to simulate the terminal voltage of the lithium ion battery; calculating constant-current pseudo-steady-state impedance according to the terminal voltage and the corresponding open-circuit voltage under different current multiplying power constant-current working conditions; constructing an observation dimension-increasing state estimator by taking the constant-current pseudo-steady-state impedance and the terminal voltage as observed quantities; and estimating the state of charge of the lithium ion battery by adopting a self-adaptive volume Kalman filtering algorithm according to the observation dimension-increasing state estimator. According to the invention, the estimation precision of the wide-temperature-range state of charge of the lithium ion battery can be improved.

Description

Technical Field

[0001] The present application relates to the field of lithium batteries, and in particular to a method, device, medium and product for estimating the state of charge of a lithium-ion battery. Background Art

[0002] In order to alleviate the environmental pollution and excessive energy consumption caused by traditional fossil fuels, new energy storage technology and electric vehicles have received strong support and rapid development in recent years. Lithium-ion batteries have been widely used in electric vehicles and energy storage systems due to their advantages such as high energy density, high power density, long cycle life, and no memory. The state of charge (SOC) of lithium-ion batteries is one of the key indicators of battery management systems. Accurate SOC estimation can prevent lithium-ion batteries from overcharging and over-discharging, so that they can work in an efficient and safe range. However, since lithium-ion batteries are highly complex and nonlinear electrochemical systems, they involve the embedding and extraction of lithium ions in solid electrodes, migration and diffusion in electrolytes, and interfacial reaction kinetics. These processes are easily affected by temperature, charge and discharge conditions, and aging. Therefore, it is difficult to achieve accurate estimation of their SOC in this field.

[0003] Many researchers have done a lot of work on SOC estimation of lithium batteries. At present, SOC estimation methods can be mainly divided into three categories: open-loop estimation based on traditional methods, closed-loop estimation based on models, and estimation methods based on data-driven methods. Among them, traditional methods mainly include the ampere-hour integration method and the open-circuit voltage method. The ampere-hour integration method has a simple principle and is a standard SOC calculation method. However, due to the lack of feedback, its accuracy is easily affected by initial values ​​and cumulative errors. The open-circuit voltage method requires the battery to undergo a long period of static depolarization. Therefore, these two methods are not suitable for online estimation of lithium battery SOC alone. The model-based closed-loop estimation method mainly uses the equivalent circuit model combined with the filtering algorithm to realize the online estimation of lithium battery SOC. This method is the mainstream method for lithium battery SOC estimation and is also a research hotspot in this field. The equivalent circuit models currently used for lithium battery state estimation mainly include the first-order RC equivalent circuit model, the second-order RC equivalent circuit model, and the fractional-order equivalent circuit model. The fractional-order equivalent circuit model relies on electrochemical impedance spectroscopy analysis and simulates the dynamic response of the battery voltage by adding Weber impedance and constant phase elements. Although it has high accuracy, the model is relatively complex, and because fractional-order differentials need to consider more historical data, its practical application is greatly limited. The integer-order equivalent circuit model can relatively accurately reflect the dynamic characteristics of the battery by only using the series and parallel connection of resistors and capacitors. Due to its simple structure, it is currently the most widely used model. Then, based on the combination of the equivalent circuit model and the filtering algorithm, the state space equation is adopted, the ampere-hour integration method and the capacitor voltage response are used as the state transfer equation, and the voltage observation value is used as feedback to realize the closed-loop SOC estimation.

[0004] At present, the main research hotspots are concentrated on model parameter identification, filtering algorithm selection and noise processing technology. However, the equivalent circuit model itself is an approximate model, and various parameter identification methods are based on the results in the least squares optimal sense. Therefore, the ability to improve model accuracy is limited. Summary of the invention

[0005] The purpose of this application is to provide a lithium-ion battery state of charge estimation method, device, medium and product, which can improve the estimation accuracy of the lithium-ion battery state of charge over a wide temperature range.

[0006] To achieve the above objectives, this application provides the following solutions:

[0007] In a first aspect, the present application provides a method for estimating a state of charge of a lithium-ion battery, comprising:

[0008] A constant current pseudo-steady-state model is constructed based on the equivalent circuit model and electrochemical mechanism model;

[0009] The constant current pseudo-steady-state model is used to simulate the terminal voltage of the lithium-ion battery;

[0010] Calculating the constant current pseudo-steady-state impedance according to the terminal voltage and the corresponding open circuit voltage under constant current conditions with different current magnifications;

[0011] Constructing an observation dimension-increasing state estimator using the constant-current pseudo-steady-state impedance and the terminal voltage as observation quantities;

[0012] According to the observed dimension-increased state estimator, an adaptive volumetric Kalman filter algorithm is used to estimate the state of charge of the lithium-ion battery.

[0013] Optionally, the constant current pseudo-steady-state model is obtained by regarding the capacitor as being open-circuited based on a first-order RC equivalent circuit model.

[0014] Optionally, the expression for simulating the terminal voltage of a lithium-ion battery using the constant current pseudo-steady-state model is:

[0015] U t =IR (qs,SOC,I) +U OCV ;

[0016] Among them, U t represents the terminal voltage, I represents the current, and R (qs,SOC,I) represents the constant current pseudo-steady-state impedance, U OCV Indicates the open circuit voltage.

[0017] Optionally, according to the terminal voltage and the corresponding open circuit voltage under different current rate constant current conditions, the expression for calculating the constant current pseudo-steady-state impedance is:

[0018] R qs,c =(U t -U OCV ) / I c ;

[0019]

[0020] Among them, R qs,c It represents the constant current pseudo steady-state impedance under the constant current condition of current ratio c, U t Indicates terminal voltage, U OCV Indicates the open circuit voltage, I c Indicates the current under the condition of constant current with current ratio c, R qs,m It represents the average value of constant current pseudo steady-state impedance under all current multiple constant current conditions, R qs,cj represents the constant current pseudo-steady-state impedance under the j-th current multiplier constant current condition, β j represents the weighting coefficient corresponding to the constant current pseudo-steady-state impedance under the j-th current multiplier constant current condition, R qs,wmIt represents the weighted value of the constant current pseudo-steady-state impedance under all current rate constant current conditions, N represents the number of different current rates, sum() represents the summation function, exp() represents the exponential function with the natural constant e as the base, and abs() represents the absolute value function.

[0021] Optionally, the state space expression of the observed dimension-increased state estimator includes a state equation and an observation equation;

[0022] The state equation is:

[0023] The observation equation is:

[0024] Among them, SOC k Indicates the state of charge at time k, SOC k-1 represents the state of charge at time k-1, U 1,k represents the voltage of the inertia link at time k, U 1,k-1 represents the voltage of the inertia link at time k-1, R 0,k represents the ohmic impedance at time k, R 0,k-1 represents the ohmic impedance at time k-1, R 1,k represents the resistance of the inertia link at time k, R 1,k-1 represents the resistance of the inertia link at time k-1, τ 1,k represents the time constant of the inertia link at time k, τ 1,k-1 represents the time constant of the inertia link at time k-1, U t,k represents the terminal voltage at time k, R qs,k represents the weighted value of the constant current pseudo-steady-state impedance at time k, I k represents the current at time k, I k-1 represents the current at time k-1, U OCV,k represents the open circuit voltage at time k, ω k-1 represents the process noise at time k-1, v 1,k represents the measurement noise of the terminal voltage at time k, v 2,k represents the measurement noise of the constant current pseudo-steady-state impedance at time k, A represents the state transfer matrix, and B represents the matrix that converts the input into the state.

[0025] Optionally, according to the observed dimension-increased state estimator, an adaptive volumetric Kalman filter algorithm is used to estimate the state of charge of the lithium-ion battery, including:

[0026] Setting initial values; the initial values ​​include initial values ​​of state quantities and covariance matrices;

[0027] Calculate volume points based on covariance matrix and state quantity;

[0028] Volume point propagation and state update are performed based on the state equation of the observed dimensionality-increased state estimator; the dimension of the state quantity is 5, including: state of charge, voltage of the inertia link, ohmic impedance, resistance of the inertia link and time constant of the inertia link;

[0029] Calculate the estimated covariance matrix based on the state quantity and the process noise matrix;

[0030] Calculate volume points based on the estimated covariance matrix and state quantities;

[0031] Volume point propagation and observation estimation are performed based on the observation equation of the observation-dimensional state estimator; the dimension of the observation quantity is 2, including: constant current pseudo-steady-state impedance and terminal voltage;

[0032] Calculate the observation estimation covariance matrix and the cross covariance matrix based on the state quantity, observation quantity and measurement noise matrix;

[0033] Calculate the Kalman gain matrix based on the observed estimated covariance matrix and the cross covariance matrix;

[0034] Perform a posteriori state update and covariance matrix update based on the Kalman gain matrix, the observed estimated covariance matrix and the observed quantity;

[0035] Calculate the residual matrix based on the observed values;

[0036] The process noise matrix and the measurement noise matrix are updated according to the residual matrix, the Kalman gain matrix and the observed quantity.

[0037] Optionally, the initial value of the covariance matrix is ​​diag(1e-5,1e-6,1e-6,1e-6,1e-3), the initial value of the process noise matrix is ​​diag(1e-6,1e-6,1e-6,1e-6,1e-6), and the initial value of the measurement noise matrix is ​​diag(1e-3,1e-4); wherein diag() represents the construction of a diagonal matrix.

[0038] In a second aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the lithium-ion battery state of charge estimation method.

[0039] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which implements the lithium-ion battery state of charge estimation method when executed by a processor.

[0040] In a fourth aspect, the present application provides a computer program product, including a computer program, which implements the lithium-ion battery state of charge estimation method when executed by a processor.

[0041] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0042] The present application provides a lithium-ion battery state of charge estimation method, device, medium and product, which takes into account the reflection of electrochemical mechanism information on the equivalent circuit model, establishes a constant current pseudo-steady-state model, uses the constant current pseudo-steady-state model to simulate the terminal voltage of the lithium-ion battery, and calculates the constant current pseudo-steady-state impedance according to the terminal voltage and the corresponding open circuit voltage under constant current conditions with different current ratios, thereby constructing an observation dimension-increasing state estimator with the constant current pseudo-steady-state impedance and the terminal voltage as observation quantities, and uses an adaptive volumetric Kalman filtering algorithm to estimate the state of charge of the lithium-ion battery, thereby achieving accurate estimation of the state of charge of the lithium-ion battery over a wide temperature range. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0044] Figure 1 This is a flow chart of a method for estimating the state of charge of a lithium-ion battery in one embodiment of the present application;

[0045] Figure 2 1 is a structural diagram of a first-order RC equivalent circuit model of a lithium-ion battery in one embodiment of the present application. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0047] In order to solve the problem that the current equivalent circuit model and electrochemical mechanism model are basically in a state of separation, and the reflection of the electrochemical mechanism information on the equivalent circuit model is not considered. This application proposes the concept of constant current pseudo-steady state, and comprehensively applies the relationship between impedance and SOC, current and temperature in the constant current pseudo-steady state to state observation and parameter identification, thereby organically combining the equivalent circuit model and the electrochemical mechanism model to improve the accuracy of the model and the SOC estimation ability. Taking into account the uneven temperature distribution inside the battery under low temperature conditions, at this time, there will be a large error between the open circuit voltage calculated by the lithium battery and its true value. Therefore, the ability of the equivalent circuit model at low temperature to describe the dynamic characteristics of the battery voltage is limited. This application proposes an adaptive volumetric Kalman filter (Adaptive Cubature Kalman Filter, ACKF) and a constant current pseudo-steady state impedance R qs Combined algorithm model.

[0048] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0049] In an exemplary embodiment, the present application provides a method for estimating the state of charge of a lithium-ion battery, such as Figure 1 As shown, the method comprises the following steps:

[0050] S1: A constant current pseudo-steady-state model is constructed based on the equivalent circuit model and electrochemical mechanism model.

[0051] First, the equivalent circuit model of lithium-ion batteries is studied. In order to achieve accurate estimation of the SOC of lithium-ion batteries, a suitable equivalent circuit model needs to be established. This application compares and analyzes the simulation accuracy of 12 equivalent circuit models for the terminal voltage of ternary lithium batteries, and finally selects the first-order RC equivalent circuit model for modeling. Its structure is as follows Figure 2 As shown, U OCV represents the open circuit voltage, R0 represents the ohmic impedance, R1 and C1 represent the inertial link parameters, and U t represents the terminal voltage, U1 represents the voltage of the inertia link, I represents the current, the direction of which is positive for charging and negative for discharging, R qs Represents the constant current pseudo-steady-state impedance.

[0052] The voltage characteristic formula of the first-order RC equivalent circuit model is as follows:

[0053]

[0054] In the SOC estimation of lithium-ion batteries, the voltage is often recursively calculated in a discrete form, namely:

[0055]

[0056] Among them, τ1=R1C1 represents the time constant of the inertial link, Δt represents the sampling interval, exp() represents the exponential function with the natural constant e as the base, and k represents the number of moments.

[0057] According to the above equivalent circuit model, the chemical mechanism of lithium-ion batteries in the process of charging and discharging is studied, and the constant current pseudo-steady-state characteristics are analyzed to obtain a constant current pseudo-steady-state model, that is, Figure 2 Based on the first-order RC equivalent circuit model shown in the figure, the capacitor is considered to be open circuit, at which point R qs =R0+R1, the model is simplified and more accurate under constant current conditions.

[0058] S2: using the constant current pseudo-steady-state model to simulate the terminal voltage of the lithium-ion battery.

[0059] In the constant current pseudo-steady state, the expression of the terminal voltage is as follows:

[0060] U t =IR (qs,SOC,I) +U OCV .

[0061] Among them, U t represents the terminal voltage, I represents the current, and R (qs,SOC,I) Represents the constant current pseudo-steady-state impedance, and is expressed as R in subsequent expressions qs Instead, it is a function of SOC and I, U OCV Indicates the open circuit voltage.

[0062] S3: Calculate the constant current pseudo-steady-state impedance according to the terminal voltage and the corresponding open circuit voltage under the constant current working conditions with different current magnifications.

[0063] This application uses the difference between the terminal voltage and the open circuit voltage under different rates of constant current discharge to calculate. Finally, the impedance under multiple rates is weighted averaged to obtain the information used for model feedback, that is, the constant current pseudo-steady-state impedance R qs The derivation process is described by the following formula:

[0064] R qs,c =(U t -U OCV ) / I c ;

[0065]

[0066] Among them, R qs,c It represents the constant current pseudo steady-state impedance under the constant current condition of current ratio c, U t Indicates terminal voltage, U OCV Indicates the open circuit voltage, I c Indicates the current under the condition of constant current with current ratio c, Rqs,m It represents the average value of constant current pseudo steady-state impedance under all current multiple constant current conditions, R qs,cj represents the constant current pseudo-steady-state impedance under the j-th current multiplier constant current condition, β j represents the weighting coefficient corresponding to the constant current pseudo-steady-state impedance under the j-th current multiplier constant current condition, R qs,wm It represents the weighted value of the constant current pseudo-steady-state impedance under all current rate constant current conditions, N represents the number of different current rates, sum() represents the summation function, exp() represents the exponential function with the natural constant e as the base, and abs() represents the absolute value function.

[0067] S4: constructing an observation dimension-increasing state estimator using the constant-current pseudo-steady-state impedance and the terminal voltage as observation quantities.

[0068] Since lithium batteries are nonlinear systems, after comparing and analyzing the advantages and disadvantages of various algorithms, ACKF was finally used to implement the model parameter R qs And real-time estimation of battery SOC. In order to realize state estimation, the battery model needs to be expressed in the form of state space.

[0069] Since the capacitance effect in the model disappears under constant current conditions, the total ohmic impedance of the model under dynamic conditions is also at the corresponding R qs This indicates that R qs It can be used as an observation to provide effective constraints and observation equations for parameter identification and state estimation. Based on this, the proposed algorithm model is called the observation-dimensional state estimator, and its state space expression is as follows:

[0070] Equation of state:

[0071] Observation equation:

[0072] Among them, SOC k Indicates the state of charge at time k, SOC k-1 represents the state of charge at time k-1, U 1,k represents the voltage of the inertia link at time k, U 1,k-1 represents the voltage of the inertia link at time k-1, R 0,k represents the ohmic impedance at time k, R 0,k-1 represents the ohmic impedance at time k-1, R 1,k represents the resistance of the inertia link at time k, R 1,k-1 represents the resistance of the inertia link at time k-1, τ 1,k represents the time constant of the inertia link at time k, τ 1,k-1 represents the time constant of the inertia link at time k-1, U t,k represents the terminal voltage at time k, R qs,krepresents the weighted value of the constant current pseudo-steady-state impedance at time k, I k represents the current at time k, I k-1 represents the current at time k-1, U OCV,k represents the open circuit voltage at time k, ω k-1 represents the process noise at time k-1, v 1,k represents the measurement noise of the terminal voltage at time k, v 2,k represents the measurement noise of the constant current pseudo-steady-state impedance at time k, A = diag[1,-exp(-Δt / τ 1,k-1 ),1,1,1] represents the state transfer matrix, B=[Δt / Q c ,R 1,k-1 (1-exp(-Δt / τ 1,k-1 )),0,0,0] T represents the matrix that transforms input into state, Q c Indicates the total battery capacity.

[0073] S5: Estimate the state of charge of the lithium-ion battery using an adaptive volumetric Kalman filter algorithm according to the observed dimensionality-increased state estimator.

[0074] The ACKF algorithm used in this application mainly consists of four steps, namely setting the initial value, time update, measurement update and noise matrix update. The dimension of the state quantity is 5, including: charge state, voltage of the inertial link, ohmic impedance, resistance of the inertial link and time constant of the inertial link. The dimension of the observation quantity is 2, including: constant current pseudo-steady-state impedance and terminal voltage. The ACKF algorithm mainly involves the calculation and update of volume points, and its specific calculation process is given below.

[0075] 1. Set the initial value.

[0076] The initial value includes the initial value of the state quantity And the initial value P0 of the covariance matrix is ​​as follows:

[0077]

[0078] 2. Time update.

[0079] 2.1 Calculate the volume point X based on the covariance matrix and state quantity i,k , the formula is as follows:

[0080] S k =chol(P k );

[0081]

[0082] Among them, chol() represents the Cholesky decomposition function, n represents the state dimension, In×n represents the identity matrix, and X(:,i) represents the i-th column of the matrix.

[0083] 2.2 Based on the state equation of the observed dimensionality-increasing state estimator, volume point propagation and state update are performed. The formula is as follows:

[0084]

[0085] Where f(·) represents the state transition function.

[0086] 2.3 Calculate the estimated covariance matrix based on the state quantity and process noise matrix The formula is as follows:

[0087]

[0088] 3. Measurement update.

[0089] 3.1 Calculate the volume point X based on the estimated covariance matrix and state quantity i,k+1 , the formula is as follows:

[0090]

[0091] 3.2 Based on the observation equation of the observation-dimensional state estimator, volume point propagation and observation estimation are performed. The formula is as follows:

[0092]

[0093] Where g(·) represents the measurement function, Y i,k+1 represents the observed value at time k+1, Represents the estimated value of the observation at time k+1.

[0094] 3.3 Calculate the observation estimation covariance matrix based on the state quantity, observation quantity and measurement noise matrix and the cross covariance matrix The formula is as follows:

[0095]

[0096] 3.4 Calculate the Kalman gain matrix K based on the observed estimated covariance matrix and the cross covariance matrix k+1 , the formula is as follows:

[0097]

[0098] 3.5 Based on the Kalman gain matrix, the observed estimated covariance matrix and the observed quantity, the posterior state update and the covariance matrix update are obtained. and P k+1 , the formula is as follows:

[0099]

[0100] 4. Adaptive adjustment of process noise and observation noise, that is, updating of noise matrix.

[0101] 4.1 Calculate the residual matrix H based on the observed value k , the formula is as follows:

[0102]

[0103] Among them, Y i , They represent the observed value and the estimated value of the i-th volume point respectively, and L represents the window length, which is selected as 5 in this application.

[0104] 4.2 Update the process noise matrix and measurement noise matrix based on the residual matrix, Kalman gain matrix and observations to obtain Q k and R k , the formula is as follows:

[0105]

[0106] It should be noted that the setting of the initial value has a certain impact on the algorithm. The state dimension of the ACKF model is 5 and the observation dimension is 2, so the corresponding covariance matrix and process noise matrix are both 5×5 square matrices, and the measurement noise matrix is ​​a 2×2 square matrix. That is, the initial value of the covariance matrix P0=diag(1e-5,1e-6,1e-6,1e-6,1e-3), the initial value of the process noise matrix Q0=diag(1e-6,1e-6,1e-6,1e-6,1e-6), and the initial value of the measurement noise matrix R0=diag(1e-3,1e-4), where diag() indicates the construction of a diagonal matrix.

[0107] In summary, the lithium-ion battery state of charge estimation method provided in this application mainly performs the following tasks:

[0108] 1. Analyze the equivalent circuit model of lithium-ion batteries, compare and study the simulation accuracy of different equivalent circuit models on the voltage of ternary lithium batteries, and explore suitable lithium-ion equivalent circuit models.

[0109] 2. Explore the mechanism of the charging and discharging process of lithium-ion batteries, and explore the lithium ion concentration distribution state inside the solid phase of the battery under constant current conditions. Study the correlation between electrochemical polarization and battery SOC and current under constant current pseudo-steady state, study the description of battery voltage characteristics in the form of ohmic impedance, and derive a method to use constant current pseudo-steady-state impedance to indicate current rate and SOC under constant current conditions.

[0110] 3. By studying the accuracy and feasibility of real-time estimation of lithium-ion battery SOC using different algorithms, the ACKF algorithm is used to achieve online update of model parameters and real-time estimation of battery SOC.

[0111] 4. Express the battery model in the form of state space.

[0112] 5. Based on the disappearance of the capacitance effect in the model under constant current conditions, the total ohmic impedance of the model under dynamic conditions is also at the corresponding R qs Fluctuates around, so choose R qs As an observation quantity, it provides effective constraints and observation equations for parameter identification and state estimation, and the proposed algorithm model is called the observation increased-dimensional state estimator. The observation increased-dimensional state estimator and its state space expression are constructed.

[0113] 6. R qs The calculation is carried out for research and analysis, and the difference between the terminal voltage and the open circuit voltage under constant current discharge at different rates is used for calculation. Finally, the impedance under multiple rates is weighted averaged to obtain the R used in the model. qs value.

[0114] 7. The battery is discharged at constant current at different rates. R qs It is calculated according to the derived formula and weighted averaged using the derived formula. Finally, the state of charge of the lithium battery is experimentally analyzed based on the observed increased-dimensional state estimator combined with ACKF to accurately estimate the state of charge of the lithium battery.

[0115] In an exemplary embodiment, the present application further provides a computer device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0116] In an exemplary embodiment, the present application further provides a computer-readable storage medium storing a computer program, which implements the steps in the above-mentioned method embodiments when executed by a processor.

[0117] In an exemplary embodiment, the present application further provides a computer program product, including a computer program, which implements the steps in the above-mentioned method embodiments when executed by a processor.

[0118] In this application, all actions to obtain signals, information or data are carried out in compliance with the relevant data protection laws and policies of the country where the application is located, and with the authorization of the corresponding device owner. It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant legal provisions.

[0119] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0120] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0121] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0122] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for estimating the state of charge of a lithium-ion battery, characterized in that: include: A constant current pseudo-steady-state model is constructed based on the equivalent circuit model and electrochemical mechanism model; The constant current pseudo-steady-state model is used to simulate the terminal voltage of the lithium-ion battery; Calculating the constant current pseudo-steady-state impedance according to the terminal voltage and the corresponding open circuit voltage under constant current conditions with different current magnifications; Constructing an observation dimension-increasing state estimator using the constant-current pseudo-steady-state impedance and the terminal voltage as observation quantities; According to the observed dimension-increased state estimator, an adaptive volumetric Kalman filter algorithm is used to estimate the state of charge of the lithium-ion battery.

2. The method for estimating the state of charge of a lithium-ion battery according to claim 1, characterized in that: The constant current pseudo-steady-state model is obtained by considering the capacitor as an open circuit on the basis of a first-order RC equivalent circuit model.

3. The method for estimating the state of charge of a lithium-ion battery according to claim 1, characterized in that: The expression for simulating the terminal voltage of a lithium-ion battery using the constant current pseudo-steady-state model is: U t =IS (qs,SOC,I) +U OCV ; Among them, U t represents the terminal voltage, I represents the current, and R (qs,SOC,I) represents the constant current pseudo-steady-state impedance, U OCV Indicates the open circuit voltage.

4. The method for estimating the state of charge of a lithium-ion battery according to claim 1, characterized in that: According to the terminal voltage and the corresponding open circuit voltage under different current magnification constant current conditions, the expression for calculating the constant current pseudo-steady-state impedance is: R qs,c =(U t -U OCV ) / I c ; Among them, R qs,c It represents the constant current pseudo steady-state impedance under the constant current condition of current ratio c, U t Indicates terminal voltage, U OCV Indicates the open circuit voltage, I c Indicates the current under constant current condition with current ratio c, R qs,m It represents the average value of constant current pseudo steady-state impedance under all current multiple constant current conditions, R qs,cj represents the constant current pseudo-steady-state impedance under the j-th current multiplier constant current condition, β j represents the weighting coefficient corresponding to the constant current pseudo-steady-state impedance under the j-th current multiplier constant current condition, R qs,wm It represents the weighted value of the constant current pseudo-steady-state impedance under all current rate constant current conditions, N represents the number of different current rates, sum() represents the summation function, exp() represents the exponential function with the natural constant e as the base, and abs() represents the absolute value function.

5. The method for estimating the state of charge of a lithium-ion battery according to claim 1, characterized in that: The state space expression of the observed dimension-increased state estimator includes a state equation and an observation equation; The state equation is: The observation equation is: Among them, SOC k Indicates the state of charge at time k, SOC k-1 represents the state of charge at time k-1, U 1,k represents the voltage of the inertia link at time k, U 1,k-1 represents the voltage of the inertia link at time k-1, R 0,k represents the ohmic impedance at time k, R 0,k-1 represents the ohmic impedance at time k-1, R 1,k represents the resistance of the inertia link at time k, R 1,k-1 represents the resistance of the inertial link at time k-1, τ 1,k represents the time constant of the inertia link at time k, τ 1,k-1 represents the time constant of the inertia link at time k-1, U t,k represents the terminal voltage at time k, R qs,k represents the weighted value of the constant current pseudo-steady-state impedance at time k, I k represents the current at time k, I k-1 represents the current at time k-1, U OCV,k represents the open circuit voltage at time k, ω k-1 represents the process noise at time k-1, v 1,k represents the measurement noise of the terminal voltage at time k, v 2,k represents the measurement noise of the constant current pseudo-steady-state impedance at time k, A represents the state transfer matrix, and B represents the matrix that converts the input into the state.

6. The method for estimating the state of charge of a lithium-ion battery according to claim 1, characterized in that: According to the observed dimension-increased state estimator, an adaptive volumetric Kalman filter algorithm is used to estimate the state of charge of the lithium-ion battery, including: Setting initial values; the initial values ​​include initial values ​​of state quantities and covariance matrices; Calculate volume points based on covariance matrix and state quantity; Volume point propagation and state update are performed based on the state equation of the observed dimensionality-increased state estimator; the dimension of the state quantity is 5, including: state of charge, voltage of the inertia link, ohmic impedance, resistance of the inertia link and time constant of the inertia link; Calculate the estimated covariance matrix based on the state quantity and the process noise matrix; Calculate volume points based on the estimated covariance matrix and state quantities; Volume point propagation and observation estimation are performed based on the observation equation of the observation-dimensional state estimator; the dimension of the observation quantity is 2, including: constant current pseudo-steady-state impedance and terminal voltage; Calculate the observation estimation covariance matrix and the cross covariance matrix based on the state quantity, observation quantity and measurement noise matrix; Calculate the Kalman gain matrix based on the observed estimated covariance matrix and the cross covariance matrix; Perform a posteriori state update and covariance matrix update based on the Kalman gain matrix, the observed estimated covariance matrix and the observed quantity; Calculate the residual matrix based on the observed values; The process noise matrix and the measurement noise matrix are updated according to the residual matrix, the Kalman gain matrix and the observed quantity.

7. The method for estimating the state of charge of a lithium-ion battery according to claim 6, characterized in that: The initial value of the covariance matrix is ​​diag(1e-5,1e-6,1e-6,1e-6,1e-3), the initial value of the process noise matrix is ​​diag(1e-6,1e-6,1e-6,1e-6,1e-6), and the initial value of the measurement noise matrix is ​​diag(1e-3,1e-4); wherein diag() represents the construction of a diagonal matrix.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for estimating the state of charge of a lithium-ion battery according to any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for estimating the state of charge of a lithium-ion battery according to any one of claims 1 to 7 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for estimating the state of charge of a lithium-ion battery according to any one of claims 1 to 7 is implemented.

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