CN115469236B

CN115469236B - Battery SOC estimation method and device and electronic equipment

Info

Publication number: CN115469236B
Application number: CN202211330577.XA
Authority: CN
Inventors: 刘新华; 于瀚卿; 张力升; 杨世春; 林家源; 曹瑞
Original assignee: Beihang University
Current assignee: Beihang University
Priority date: 2022-10-28
Filing date: 2022-10-28
Publication date: 2023-03-24
Anticipated expiration: 2042-10-28
Also published as: CN115469236A

Abstract

The invention provides a battery SOC estimation method, a battery SOC estimation device and electronic equipment, wherein the method comprises the following steps: determining parameters used for calculating the battery terminal voltage and measurable parameters outside the battery in a battery simulation model which is constructed in advance as initial parameters used for estimating the SOC of the battery; acquiring sampling data of each initial parameter; sampling data is an SOC value corresponding to the initial parameter as a variable and taking different values; for each initial parameter, calculating the correlation between the initial parameter and the SOC by using the sampling data of the initial parameter; taking a plurality of initial parameters with the correlation closest to 1 as characteristic parameters for estimating the SOC of the battery; performing model training by using the characteristic parameters to obtain a battery SOC estimation model; and estimating the SOC of the battery by using the SOC estimation model of the battery. According to the scheme, the estimation accuracy can be improved.

Description

Battery SOC estimation method and device and electronic equipment

Technical Field

The embodiment of the invention relates to the technical field of batteries, in particular to a battery SOC estimation method, a battery SOC estimation device and electronic equipment.

Background

Conventional methods for estimating the State of Charge (SOC) of a battery include table lookup methods, ampere-hour integration methods, model-based methods, and data-driven methods. Each method has corresponding disadvantages, and the estimation result of the traditional estimation method has lower accuracy.

Disclosure of Invention

The embodiment of the invention provides a battery SOC estimation method, a battery SOC estimation device and electronic equipment, which can improve estimation accuracy.

In a first aspect, an embodiment of the present invention provides a method for estimating a battery SOC, including:

determining parameters used for calculating the battery terminal voltage and measurable parameters outside the battery in a battery simulation model which is constructed in advance as initial parameters used for estimating the SOC of the battery; the parameters for calculating the battery terminal voltage include: at least one of a cell open circuit voltage, a reactive polarization overpotential, a concentration polarization overpotential, and an ohmic polarization overpotential; the battery external measurable parameters include: at least one of current, terminal voltage, and temperature;

acquiring sampling data of each initial parameter; the sampling data is the corresponding SOC value when the initial parameter is taken as a variable and different values are taken;

for each initial parameter, calculating the correlation between the initial parameter and the SOC by using the sampling data of the initial parameter;

taking a plurality of initial parameters with the correlation closest to 1 as characteristic parameters for estimating the SOC of the battery;

performing model training by using the characteristic parameters to obtain a battery SOC estimation model;

and estimating the SOC of the battery by using the SOC estimation model of the battery.

In one possible implementation, the initial parameters include: cell open circuit voltage, reactive polarization overpotential, concentration polarization overpotential, ohmic polarization overpotential, current, terminal voltage, and temperature.

In a possible implementation manner, when the initial parameter is a parameter for calculating a battery terminal voltage, the sampled data of the initial parameter is obtained by simulation based on the battery simulation model;

when the initial parameter is a measurable parameter outside the battery, the sampling data of the initial parameter is obtained based on experimental measurement.

In a possible implementation manner, the method for constructing the battery simulation model includes:

establishing an electrochemical model of the battery based on battery parameters and a battery internal reaction mechanism;

establishing a spatial network for SEI film growth at the negative electrode of the battery; the bottom surface of the space network is the surface of the battery cathode particles, and the height direction of the space network is the radial direction of the battery cathode particles; the space network is divided into a plurality of layers of grids from the bottom surface to the height direction;

starting from a bottom grid of a plurality of layers of grids in the space network, carrying out Monte Carlo simulation on the growth of an SEI film in the space network to obtain an aging model of the battery;

and carrying out parameter coupling on the electrochemical model and the aging model to obtain the battery simulation model.

In one possible implementation, the SEI film growth in the spatial network in each growth phase is monte carlo simulated as follows:

determining vacancy grids of the SEI film to be grown in the current growth stage based on non-vacancy grids in the spatial network in the last growth stage; the non-vacancy grids are grids on which an SEI film is grown;

respectively calculating the forming rate of the SEI film aiming at each vacancy grid of the SEI film to be grown in the current growth stage;

and respectively taking each vacancy grid as an SEI film growth event, determining a target vacancy grid for growing the SEI film in the current growth stage by combining the forming rate, and calculating the simulation time step corresponding to the current growth stage.

In one possible implementation, the formation rate of the SEI film of the vacancy grid is calculated using a first equation, a second equation, and a third equation as follows:

wherein Γ is the rate of formation of an SEI film of a vacancy grid, K is the reaction rate constant of the SEI film, F is the Faraday constant, R is the ideal gas constant, T is the battery temperature, η _SEI Is the local overpotential of the SEI film, J _s,0 Is the exchange current density of SEI film formation, a ₀ Is the base area of a single grid, N _A Is the Avogastron constant, U _SEI Is the reaction equilibrium potential of the SEI film,

is the potential of the solid phase,

is the potential of the liquid phase, R _SEI Is SEI film internal resistance, j is lithium ion flux density;

and/or the presence of a gas in the gas,

the determining a target vacancy grid for growing the SEI film at the current growth stage comprises:

for each event, determining the occurrence probability of the event by dividing the formation rate of the SEI film corresponding to the event by the sum of the formation rates of all event SEI films;

generating a first random number, and determining an event with the occurrence probability closest to the first random number as an event required to be executed in the current growth stage; the first random number is a uniform random number which is more than 0 and less than 1, and the vacancy grid corresponding to the event closest to the first random number is a target vacancy grid;

and/or the presence of a gas in the gas,

calculating the simulation time step length corresponding to the current growth stage by using the following formula

：

Wherein is gamma _m The forming rate of an SEI film corresponding to an event M, wherein M is the total number of all events, and M and M are positive integers; second random number xi ₂ Is a uniform random number greater than 0 and less than 1.

In one possible implementation, the parametrically coupling the electrochemical model and the aging model includes:

calculating the SEI film thickness in the battery aging process by using the aging model, and calculating the variation of the target input parameter in the electrochemical model based on the calculated SEI film thickness; the target input parameters are input parameters influenced by an SEI film;

recalculating a value of an output parameter using the electrochemical model based on the amount of change in the target input parameter; wherein the output parameters of the electrochemical model comprise part of the input parameters of the aging model.

In a second aspect, an embodiment of the present invention further provides a battery SOC estimation apparatus, including:

the initial parameter determining unit is used for determining parameters used for calculating the battery terminal voltage and measurable parameters outside the battery in a battery simulation model which is constructed in advance as initial parameters used for estimating the SOC of the battery; the parameters for calculating the terminal voltage of the battery include: at least one of a cell open circuit voltage, a reactive polarization overpotential, a concentration polarization overpotential, and an ohmic polarization overpotential; the battery external measurable parameters include: at least one of current, terminal voltage, and temperature;

the sampling data acquisition unit is used for acquiring sampling data of each initial parameter; the sampling data is the corresponding SOC value when the initial parameter is taken as a variable and different values are taken;

a correlation calculation unit, configured to calculate, for each initial parameter, a correlation between the initial parameter and the SOC using the sampling data of the initial parameter;

the characteristic parameter selection unit is used for taking a plurality of initial parameters with the correlation closest to 1 as characteristic parameters for estimating the SOC of the battery;

the model training unit is used for carrying out model training by utilizing the characteristic parameters to obtain a battery SOC estimation model;

and the estimation unit is used for estimating the SOC of the battery by utilizing the SOC estimation model of the battery.

In a third aspect, an embodiment of the present invention further provides an electronic device, which includes a memory and a processor, where the memory stores a computer program, and the processor executes the computer program to implement the method according to any embodiment of this specification.

In a fourth aspect, the present invention further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed in a computer, the computer program causes the computer to execute the method described in any embodiment of the present specification.

The embodiment of the invention provides a battery SOC estimation method, a device and electronic equipment, which not only take measurable parameters outside a battery as initial parameters for alternative selection, but also take parameters used for calculating the terminal voltage of the battery in a battery simulation model capable of simulating the internal state of the battery as the initial parameters for alternative selection, expand the alternative initial parameters, select characteristic parameters with the highest correlation with SOC in each initial parameter based on the sampling data of each initial parameter, and thus train the battery SOC estimation model by using the selected characteristic parameters, so that the estimation result of the trained battery SOC estimation model can be more accurate.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.

Fig. 1 is a flowchart of a method for estimating a battery SOC according to an embodiment of the present invention;

FIG. 2 is a flowchart of a method for building a battery simulation model according to an embodiment of the present invention;

FIG. 3 is a flow chart of a Monte Carlo simulation method according to an embodiment of the present invention;

fig. 4 is a hardware architecture diagram of an electronic device according to an embodiment of the present invention;

fig. 5 is a structural diagram of a battery SOC estimation device according to an embodiment of the present invention;

fig. 6 is a structural diagram of another battery SOC estimation device according to an embodiment of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer and more complete, the technical solutions in the embodiments of the present invention will be described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention, and based on the embodiments of the present invention, all other embodiments obtained by a person of ordinary skill in the art without creative efforts belong to the scope of the present invention.

Referring to fig. 1, an embodiment of the present invention provides a method for estimating a battery SOC, including:

step 100, determining parameters used for calculating the battery terminal voltage and measurable parameters outside the battery in a battery simulation model which is constructed in advance as initial parameters used for estimating the SOC of the battery; the parameters for calculating the battery terminal voltage include: at least one of a cell open circuit voltage, a reactive polarization overpotential, a concentration polarization overpotential, and an ohmic polarization overpotential; the battery external measurable parameters include: at least one of current, terminal voltage, and temperature;

102, acquiring sampling data of each initial parameter; the sampling data is the corresponding SOC value when the initial parameter is taken as a variable and different values are taken;

104, calculating the correlation between each initial parameter and the SOC by using the sampling data of the initial parameter;

step 106, taking a plurality of initial parameters with the correlation closest to 1 as characteristic parameters for estimating the SOC of the battery;

step 108, performing model training by using the characteristic parameters to obtain a battery SOC estimation model;

and step 110, estimating the SOC of the battery by using the SOC estimation model of the battery.

In the embodiment of the invention, not only can the measurable parameters outside the battery be used as initial parameters for carrying out the alternative, but also the parameters used for calculating the terminal voltage of the battery in the battery simulation model capable of simulating the internal state of the battery can be used as the initial parameters for carrying out the alternative, the alternative initial parameters are expanded, and the characteristic parameters with the highest correlation with the SOC are selected in each initial parameter based on the sampling data of each initial parameter, so that the selected characteristic parameters are utilized for carrying out the training of the battery SOC estimation model, and the estimation result of the trained battery SOC estimation model can be more accurate.

The manner in which the various steps shown in fig. 1 are performed is described below.

First, with respect to step 100, parameters for calculating the battery terminal voltage and battery-external measurable parameters in a battery simulation model constructed in advance are determined as initial parameters for performing the estimation of the battery SOC.

Wherein the parameters for calculating the battery terminal voltage include: at least one of a cell open circuit voltage, a reactive polarization overpotential, a concentration polarization overpotential, and an ohmic polarization overpotential; the battery external measurable parameters comprise: at least one of current, terminal voltage, and temperature.

The battery simulation model is used for simulating the battery state and can simulate the battery terminal voltage based on the internal reaction mechanism of the battery. Therefore, the parameters that can estimate the SOC value of the battery are not only the parameters measurable outside the battery, but also the parameters used for calculating the terminal voltage of the battery in the battery simulation model can be used for estimation of the SOC of the battery.

In order to determine the parameters for calculating the battery terminal voltage in the battery simulation model, the process of constructing the battery simulation model will be described first.

Referring to fig. 2, the process of constructing the battery simulation model may include the following steps 200 to 206:

step 200, establishing an electrochemical model of the battery based on the battery parameters and the internal reaction mechanism of the battery.

Wherein the battery parameters include at least a dimensional parameter, a kinetic parameter, and a thermodynamic parameter of the battery.

The battery parameter acquisition method can comprise modes of product specifications, experimental tests, literature queries and the like.

The relevant parameters of the electrochemical model are shown in table 1 below.

TABLE 1

The electrochemical model is used to describe the macroscopic cell-level voltage behavior as well as the microscopic cell particle surface behavior to provide the terminal voltage and state variables needed for the aging model of the cell. Therefore, a quasi-two-dimensional model or a simplified model thereof may be used, and in an embodiment of the present invention, a quasi-two-dimensional model may be used as the electrochemical model.

The equations in the charge and discharge process described by the electrochemical mechanism model include: using the ion solid phase and liquid phase diffusion equations described by Fick's second law; using an internal solid-phase and liquid-phase potential change equation of the battery described by ohm's law; the equation for the electrochemical reaction of the solid-liquid interface is described using the Butler-Volmer kinetic equation.

Based on the above-described process equations, a series of partial differential equations and algebraic equations are established as models for describing the characteristics of the lithium ion battery from an electrochemical point of view, and the above-described process will be described in detail below:

the solid phase diffusion occurs in the r direction in the spherical active particles, the change rule of the concentration of the solid-phase lithium ions of the positive electrode and the negative electrode is described, and the control equation is as follows:

(1)

wherein, c _s Is the solid-phase lithium ion concentration, t is a time variable, r is the radial coordinate of the spherical active particle, D _s Is the lithium ion solid phase diffusion coefficient.

The liquid phase diffusion process describes the change rule of the liquid phase lithium ion concentration, and the control equation is as follows:

(2)

wherein epsilon _e Is porosity, c _e Is the concentration of liquid-phase lithium ions, t is a time variable, x is a coordinate in the thickness direction of the electrode, D _e,eff A is the specific surface area of the electrode, t is the effective liquid-phase diffusion coefficient of lithium ion ₊ For cation mobility, j is the lithium ion flux density.

The change in solid phase potential is described using solid phase ohm's law:

(3)

wherein σ _eff In order to be effective in solid phase ionic conductivity,

is the solid phase potential, R is the molar gas constant, T is the cell temperature, F is the Faraday constant, i _s Is the solid phase current density.

The change in liquid phase potential is described using liquid phase ohm's law:

(4)

wherein, κ _eff For the effective ionic conductivity of the liquid phase,

is the potential of the liquid phase, i _e Is the liquid phase current density.

The electrochemical reaction at the solid-liquid interface results in a corresponding overpotential for maintaining a certain electrochemical reaction rate, and the equation is described as:

(5)

wherein i ₀ To exchange the current density, α _a And alpha _c The transfer coefficients of the anode and the cathode are respectively, eta is the surface overpotential of the electrode active particles, R is a molar gas constant, T is the temperature of the battery, and F is a Faraday constant.

The overpotential on the surface of the electrode active material particles is related to liquid phase potential, solid phase potential and open circuit voltage, and can be described as follows:

(6)

wherein E is _ocv Is the open circuit voltage of the battery, R _SEI J is the lithium ion flux density, representing the internal resistance of the SEI film.

It should be noted that the equation of description relating to the liquid phase portion includes the positive and negative electrodes and the separator region, while the equation of description relating to the solid phase portion includes only the positive and negative electrode regions.

In addition, the basic working process of the lithium ion battery also describes the de-intercalation process of lithium ions at the positive electrode and the negative electrode, and the formula under the assumption of uniform reaction distribution is as follows:

(7)

(8)

wherein x is _ave Is the average lithium insertion amount of the negative electrode, x ₀ For the initial amount of lithium intercalation in the negative electrode, I is the current applied by the external circuit, Q _n Is the negative electrode capacity, y _ave Is the average amount of lithium inserted into the positive electrode, y ₀ For initial lithium insertion into the positive electrode, Q _p The positive electrode capacity.

Defining Dy and Dx as the variation range of lithium embedding amount of positive and negative electrodes under deep discharge of the battery respectively, and calculating the formula as follows:

(9)

wherein Q is _all Is the battery capacity.

Electrochemical model solution can be performed by matching the control equations described above with corresponding boundary conditions or by simplification.

Meanwhile, the process is simplified to a certain extent, and the calculation cost is reduced on the premise of ensuring the precision.

The battery needs to have a correspondingly large reaction polarization overpotential while maintaining the electrochemical reaction rate. Obtaining a calculation equation of the reaction polarization overpotential through a Tafel curve function relation of electrochemical reaction kinetics:

(10)

wherein k is _i I = n, p is the positive and negative reaction rate constant, c _max,i Maximum lithium ion concentration of the positive and negative electrode materials, c _surf,i The lithium ion concentration on the surface of the positive electrode material particle and the negative electrode material particle c ₀ Is the initial concentration of liquid-phase lithium ions, m _i I = n, p is an intermediate variable calculated by positive and negative reaction polarization overpotentials, R is an ideal gas constant, T is temperature, and F is a faraday constant.

According to positive and negative electrode capacity Q _p And Q _n The amount y of embedded lithium on the solid phase surface _surf And x _surf And an approximate calculation formula for the reactant ion current density j, the transformation for equation (10) can be:

(11)

the concentration of liquid-phase lithium ions in the direction of the inner polar plate of the battery has gradient distribution, which causes the generation of concentration polarization overpotential, and the calculation formula is as follows:

(12)

wherein, c ₁ And c ₂ The liquid-phase lithium ion concentrations at the surfaces of the positive and negative current collectors, respectively.

The potential change process of the interior of the battery following the ohm theorem can be simplified into ohm polarization overpotential, and the calculation formula is as follows:

(13)

wherein R is _ohm In the form of lumped parameter representation of ohmic internal resistance.

The terminal voltage of the battery can be calculated according to the open-circuit voltage and the three polarization overpotentials, and the calculation formula is as follows:

(14)

wherein U is a battery terminal voltage, E _OCV Is the open circuit voltage, eta, of the battery _act To polarize the overpotential, eta _con For concentration polarization over-potential, eta _ohm Ohmic polarization overpotential.

In the embodiment of the present invention, equation (14) may be determined as an electrochemical model for describing the battery terminal voltage U.

Step 202, establishing a spatial network for SEI film growth at the negative electrode of the battery; the bottom surface of the space network is the surface of the battery cathode particles, and the height direction of the space network is the radial direction of the battery cathode particles; the spatial network is divided into a multi-layer grid from the bottom surface to the height direction.

And 204, starting from the bottom grid of the multilayer grid in the spatial network, carrying out Monte Carlo simulation on the growth of the SEI film in the spatial network to obtain an aging model of the battery.

Wherein, the relevant parameters of the aging model are shown in the following table 2:

TABLE 2

The aging side reaction inside the battery is mainly focused on the growth of the SEI film on the surface of the negative electrode particles. During repeated charge and discharge cycles, the SEI film continuously grows, consuming active materials inside the battery, increasing the internal resistance of the battery and reducing the capacity of the battery. Therefore, in the present example, the kinetic Monte Carlo (Monte Carlo, KMC) method was used, and electrochemical kinetics equations were considered to describe the SEI film growth process.

In the present example, it is assumed that the components of the SEI film have only stable inorganic crystals, and it is considered that the SEI film formation process reaction occurs instantaneously while ignoring the dissolution of the SEI film and the progress of the lithium precipitation reaction.

First, a spatial network for SEI film growth is established. The spatial network is the KMC modeling area, i.e., the negative initial SEI film surface space. Since the SEI film thickness is much smaller than the radius of the anode active material particle, it can be considered that the SEI film grows on a plane (xy plane), and the height direction (z-axis direction) is the radial direction of the anode particle. In one implementation, the spatial network is divided into a plurality of layers of grids in the height direction, each layer of grid may include L × L grids, L is a positive integer, and the increase of the particle height at each grid is used to indicate the growth of the SEI film in the z-axis direction.

Subsequently, the spatial network is initialized. The SEI film formed in the formation phase is considered to be uniform, i.e., in the initial condition, the SEI film molecules are considered to be uniformly distributed on the lowermost mesh in the spatial network all over, and the initial simulation time is set to 0.

Next, monte carlo simulations were performed for SEI film growth in the inter-network.

In one embodiment of the present invention, referring to fig. 3, monte carlo simulation can be performed for the SEI film growth in the spatial network in each growth phase as follows (steps 300-304):

step 300, determining vacancy grids of an SEI film to be grown in the current growth stage based on non-vacancy grids in the spatial network in the last growth stage; the non-vacancy grids are grids in which an SEI film has been grown.

In the embodiment of the invention, the simulation time can be divided into a plurality of growth stages, so that Monte Carlo simulation is respectively carried out on the growth of the SEI film aiming at each growth stage.

Since the SEI film growth is continuously growing in the height direction, when determining the mesh of vacancies of the SEI film to be grown in the current growth stage, the maximum z value of the mesh of non-vacancies in each (x, y) position in the previous growth stage can be determined, and then the mesh in the position corresponding to the sum of each maximum z value and 1 is determined as the mesh of vacancies of the SEI film to be grown in the current growth stage.

Step 302, aiming at each vacancy grid of the SEI film to be grown in the current growth stage, respectively calculating the formation rate of the SEI film.

In one embodiment of the present invention, the formation rate of the SEI film of the vacancy grid can be calculated using the following formula:

wherein Γ is the rate of formation of an SEI film of a vacancy grid, K is the reaction rate constant of the SEI film, F is the Faraday constant, R is the ideal gas constant, T is the battery temperature, η _SEI Is the local overpotential of the SEI film, J _s,0 Is the exchange current density of the SEI film formation, a ₀ Is the base area of a single grid, N _A Is the Avogastron constant, U _SEI Is a reaction equilibrium potential of the SEI film,

is the potential of the solid phase and is,

is the potential of the liquid phase, R _SEI Is the internal resistance of the SEI film, and j is the lithium ion flux density.

Transforming equation (17) in conjunction with equation (6) can result in a simplified calculation of the local overpotential of the SEI film:

(18)

wherein eta is _n Is the reactive polarization overpotential of the negative electrode, E _ocp,n Is the open circuit potential of the cathode.

When the surface of the SEI film has defects such as bending or steps, the probability of generating particles in the bottom grid is considered to be higher than that on the platform. Thus, if at least one of the adjacent cells (including front, back, left, and right) of the same layer is occupied, the rate of particle formation on that cell is greater. In one embodiment of the present invention, the method further comprises: determining whether a non-vacancy grid adjacent to the vacancy grid exists in the grid layer to which the vacancy grid belongs; determining the product of the calculated formation rate and the electrochemical constant, if any, as the formation rate of the SEI film of the vacancy grid; the electrochemical constant is greater than 1. Preferably, the electrochemical constant is 2. It is understood that when the electrochemical constant is 2, if a non-vacancy grid adjacent to the vacancy grid exists in the grid layer to which the vacancy grid belongs, the formation rate is 2 times that of other non-vacancy grid which does not exist. Therefore, the simulation accuracy of the aging model can be improved.

And step 304, taking each vacancy grid as an SEI film growth event, determining a target vacancy grid for growing the SEI film in the current growth stage and calculating the simulation time step corresponding to the current growth stage by combining the forming rate.

In this step 304, the determining a target vacancy grid for growing the SEI film in the current growth phase may include:

s1, determining a quotient value of the forming rate of an SEI film corresponding to each event divided by the sum of the forming rates of all event SEI films as the occurrence probability of the event;

s2, generating a first random number, and determining an event with the occurrence probability closest to the first random number as an event to be executed in the current growth stage; the first random number is a uniform random number which is greater than 0 and less than 1, and the vacancy grid corresponding to the event closest to the first random number is a target vacancy grid.

In S1, if 100 (L = 10) grids are included in each grid layer, and each empty grid is an event, the total number of all events is 100.

Further, the formation of SEI film molecules may also be a null event, i.e., an event in which no transition reaction process occurs, and the calculation cost may be reduced by using a null event algorithm. Therefore, in one embodiment of the present invention, all events further include null events, wherein the rate of formation of the null events is a set value. By introducing null events, problems caused by further coupling of the models can be avoided.

In another embodiment, only one event may be performed per calculation, that is, one mesh of target vacancies for growing the SEI film per growth stage, then the mesh of target vacancies for growing the SEI film in the current growth stage may be determined in this step 204 by the following formula:

(19)

wherein, gamma is _m The forming rate of an SEI film corresponding to an event M, wherein M is the total number of all events, and M and M are positive integers; first random number xi ₁ Is a uniform random number greater than 0 and less than 1, and h is an event corresponding to the target null grid satisfying equation (19).

In one embodiment of the present invention, in step 304, the following formula is used to calculate the simulation time step corresponding to the current growth stage

：

(20)

Wherein is gamma _m The forming speed of an SEI film corresponding to an event M, wherein M is the total number of all events, and M and M are positive integers; second random number xi ₂ Is a uniform random number greater than 0 and less than 1.

In the embodiment of the invention, the simulation time step length corresponding to each growth stage and the target vacancy grid of the SEI film growth event are calculated by continuously selecting random numbers, so that an aging model for simulating the SEI film growth process can be obtained.

And step 206, performing parameter coupling on the electrochemical model and the aging model to obtain a battery simulation model of the battery.

The growth of an SEI film in the battery aging process can influence input parameters of an electrochemical model, and output parameters of the electrochemical model can also influence the input parameters in the aging model, so that the two models can be mutually influenced to realize coupling.

Specifically, the parameter coupling the electrochemical model and the aging model may include: calculating the SEI film thickness in the battery aging process by using the aging model, and calculating the variation of the target input parameter in the electrochemical model based on the calculated SEI film thickness; the target input parameters are input parameters influenced by an SEI film; recalculating a value of an output parameter using the electrochemical model based on the amount of change in the target input parameter; wherein the output parameters of the electrochemical model comprise part of the input parameters of the aging model.

In an embodiment of the present invention, the target input parameters in the electrochemical model may include: at least one of ohmic internal resistance of the negative electrode SEI film, battery capacity, and negative electrode particle reaction current.

The growth of the battery-aged SEI film resulted in an increase in ohmic resistance by an amount Δ R _SEI,n Can be calculated using the following formula:

(21)

wherein, delta _ave Is the average thickness, κ, of the surface of the negative active material particles increasing with the aging SEI film of the battery _SEI Is the conductivity of the SEI film.

Ohmic internal resistance R of negative electrode SEI film in electrochemical model _SEI,n Can be calculated by the following formula for influencing formula (6) in the electrochemical model:

(22)

wherein R is _SEI,n,0 Is an initial value of the ohmic internal resistance of the SEI film.

Because one SEI film molecule contains two lithium ions, the charge quantity delta Q of active lithium ions consumed by the growth of the SEI film is as follows:

(23)

wherein h is _ave Is the average number of the growing SEI film molecules in the height direction, a ₀ Is the base area of a single grid, R _n Is the radius of the anode active material particle, F is the Faraday constant, N _A Is the Avogastron constant.

Battery capacity Q _all Can be calculated from the amount of lithium ion charge consumed, and is used to influence equation (9) in the electrochemical model:

(24)

wherein Q is _all,0 Is the initial capacity of the battery.

According to the definition of the current, the side reaction current I caused by the generation of the SEI film on the surface of the particle can be obtained _SEI ：

(25)

Wherein, delta (delta Q) is the difference of lithium ion charge amount, delta t _KMC Is the simulated time step of KMC.

Reaction current I of negative electrode active material particles _n Can be calculated by the following formula for influencing m in formula (11) in the electrochemical model _n In the calculation formula (2):

(26)

where I is the current applied by the external circuit.

In addition, according to the formula (17), the output parameters of the electrochemical model including part of the input parameters of the aging model are: reaction equilibrium potential U of SEI film _SEI Potential of solid phase

Liquid phase potential

SEI film internal resistance R _SEI And a lithium ion flux density j.

After the coupling is completed, a battery simulation model for describing the characteristics of the battery is obtained.

According to the battery simulation model and the formula (14), the open-circuit voltage E of the battery can be obtained _OCV Reaction polarization overpotential eta _act Concentration polarization overpotential eta _con And ohmic polarization overpotential η _ohm Is determined as an initial parameter.

Preferably, the initial parameters determined in step 100 are: battery open circuit voltage, reactive polarization overpotential, concentration polarization overpotential, ohmic polarization overpotential, current, terminal voltage, and temperature.

102, acquiring sampling data of each initial parameter; the sampling data is the corresponding SOC value when the initial parameter is taken as a variable and different values are taken.

The manner in which the sample data is acquired for different initial parameters is also different.

In the first case, when the initial parameter is a measurable parameter outside the battery, then the sampled data of the initial parameter is obtained based on experimental measurements.

In the first case, for several initial parameters of current, terminal voltage and temperature, sampling may be performed at different sampling moments to obtain current, terminal voltage, temperature and SOC values corresponding to the sampling moments, thereby obtaining sampling data pairs of the initial parameters and the SOC values.

In the second case, when the initial parameter is a parameter for calculating the terminal voltage of the battery, the sampled data of the initial parameter is obtained by simulation based on the battery simulation model.

In the second case, for several initial parameters of the battery open-circuit voltage, the reaction polarization overpotential, the concentration polarization overpotential, and the ohmic polarization overpotential, the current sampled at the sampling time of the first case may be input into the battery simulation model, and other parameters are kept unchanged, and the battery simulation model is used to calculate the initial parameter value corresponding to the sampling time, so as to obtain the sampling data pair of the initial parameter and the SOC value.

And 104, calculating the correlation between the initial parameters and the SOC by using the sampling data of the initial parameters for each initial parameter.

Before the step 104, in order to ensure the accuracy of the correlation calculation result, data cleaning may be performed on the sampled data to screen out abnormal sampling points and data filtering, so that noise caused by abnormal values and sampling errors may be effectively removed.

In the embodiment of the invention, data filtering can be performed by using a Savitzky Golay (SG) filter, and the SG filter is a time domain filtering method based on a local least squares polynomial approximation algorithm and has excellent filtering performance in high-frequency digital signal filtering.

In one embodiment of the invention, the correlation between the initial parameters and the SOC can be determined through a Pearson correlation analysis method to find high-quality characteristic parameters, and the initial parameters with high correlation with the SOC are selected as the input of a battery SOC estimation model, so that the model training efficiency and the prediction accuracy are effectively improved. The pearson correlation coefficient is calculated as:

(12)

wherein r is _xy The correlation between the initial parameter x and the SOC value y is shown, n is the number of the sampling data corresponding to the initial parameter, is the average value of the values corresponding to the initial parameter x, and is the average value of the SOC values corresponding to the initial parameter x.

And 106, taking a plurality of initial parameters with the correlation closest to 1 as characteristic parameters for estimating the SOC of the battery.

Wherein, the closer the correlation is to 1, the higher the correlation is. When selecting the feature parameters, the selection may be performed according to a proportion of the initial parameters, or may be performed according to a specified number, for example, if the number of the feature parameters is 5, then 5 initial parameters with a correlation closest to 1 are selected as the feature parameters from 7 initial parameters.

And 108, performing model training by using the characteristic parameters to obtain a battery SOC estimation model.

In one embodiment of the invention, the battery SOC estimation model can use deep learning with a Long Short-Term Memory (LSTM) neural network as a core. And constructing the extracted characteristic parameters into a time sequence, and inputting the time sequence into the LSTM for training. The activation function in the LSTM uses a tanh function, the activation function in the full-connection layer selects a sigmoid function, the gradient descent algorithm selects an RMSprop algorithm, and a Dropout technology is selected to inhibit model overfitting training.

In LSTM, the input data to the model needs to be constructed in a time series form. Firstly, constructing data of characteristic parameters into a two-dimensional matrix according to a certain time sequence length; then, a plurality of two-dimensional matrixes are constructed into a three-dimensional tensor, namely a batch. When the model is trained, the batch is taken as a unit, so that the training efficiency of the model can be effectively improved.

In one embodiment of the invention, an RMSprop algorithm can be adopted, and the gradient is subjected to exponential weighting moving average according to element square, so that the convergence speed and accuracy are obviously improved.

In order to prevent overfitting during model training, dropout technique may be used in one embodiment of the present invention. During forward propagation, the activation value of a certain neuron stops working at a certain probability, so that the interaction between hidden nodes is reduced, the generalization of the model is improved, the dependence of the model on local characteristics is reduced, and the over-fitting phenomenon is inhibited.

After the model is trained, SOC estimation is performed by using a battery SOC estimation model. According to the data collected and calculated by the battery simulation model, the characteristic parameters with high correlation are constructed into a time sequence and input into the trained model to obtain the estimation result of the SOC of the battery.

As shown in fig. 4 and 5, an embodiment of the present invention provides a battery SOC estimation device. The device embodiments may be implemented by software, or by hardware, or by a combination of hardware and software. From a hardware aspect, as shown in fig. 4, for a hardware architecture diagram of an electronic device where a battery SOC estimation apparatus according to an embodiment of the present invention is located, in addition to the processor, the memory, the network interface, and the nonvolatile memory shown in fig. 4, the electronic device where the apparatus is located in the embodiment may also include other hardware, such as a forwarding chip responsible for processing a packet. Taking a software implementation as an example, as shown in fig. 5, as a logical device, a CPU of the electronic device reads a corresponding computer program in the non-volatile memory into the memory for running. The present embodiment provides a battery SOC estimation device, including:

an initial parameter determining unit 501, configured to determine a parameter used for calculating a battery terminal voltage and a measurable parameter outside the battery in a battery simulation model that is constructed in advance, as an initial parameter for estimating a battery SOC; the parameters for calculating the battery terminal voltage include: at least one of a cell open circuit voltage, a reactive polarization overpotential, a concentration polarization overpotential, and an ohmic polarization overpotential; the battery external measurable parameters include: at least one of current, terminal voltage, and temperature;

a sample data obtaining unit 502, configured to obtain sample data of each initial parameter; the sampling data is the corresponding SOC value when the initial parameter is taken as a variable and different values are taken;

a correlation calculation unit 503, configured to calculate, for each initial parameter, a correlation between the initial parameter and the SOC by using the sampling data of the initial parameter;

a characteristic parameter selection unit 504, configured to use a plurality of initial parameters with correlations closest to 1 as characteristic parameters for performing battery SOC estimation;

a model training unit 505, configured to perform model training using the characteristic parameters to obtain a battery SOC estimation model;

an estimating unit 506, configured to perform battery SOC estimation using the battery SOC estimation model.

In one embodiment of the invention, the initial parameters include: cell open circuit voltage, reactive polarization overpotential, concentration polarization overpotential, ohmic polarization overpotential, current, terminal voltage, and temperature.

In an embodiment of the present invention, when the initial parameter is a parameter for calculating a battery terminal voltage, the sampled data of the initial parameter is obtained based on the simulation of the battery simulation model;

In an embodiment of the present invention, please refer to fig. 6, further comprising a model building unit 507, specifically configured to:

In an embodiment of the present invention, the model construction unit is specifically configured to perform monte carlo simulation on the SEI film growth in the spatial network in each growth phase as follows:

In one embodiment of the present invention, the model construction unit is specifically configured to calculate the formation rate of the SEI film of the vacancy grid using a first formula, a second formula, and a third formula as follows:

wherein Γ is the rate of formation of an SEI film of a vacancy grid, K is the reaction rate constant of the SEI film, F is the Faraday constant, R is the ideal gas constant, T is the battery temperature, η _SEI Is the local overpotential of the SEI film, J _s,0 Is the exchange current density of the SEI film formation, a ₀ Is the floor area of the individual cells, N _A Is an Avogadro constant, U _SEI Is the reaction equilibrium potential of the SEI film,

is the potential of the solid phase,

In an embodiment of the present invention, the model construction unit is further configured to determine whether a non-null mesh adjacent to the null mesh exists in the mesh layer to which the null mesh belongs; determining the product of the calculated formation rate and the electrochemical constant, if any, as the formation rate of the SEI film of the vacancy grid; the electrochemical constant is greater than 1.

In an embodiment of the present invention, when determining a target vacancy grid of an SEI film grown in a current growth stage, the model construction unit is specifically configured to determine, for each event, a quotient obtained by dividing a formation rate of an SEI film corresponding to the event by a sum of formation rates of SEI films of all events, as an occurrence probability of the event; generating a first random number, and determining an event with the occurrence probability closest to the first random number as an event required to be executed in the current growth stage; the first random number is a uniform random number which is more than 0 and less than 1, and the vacancy grid corresponding to the event closest to the first random number is a target vacancy grid;

in an embodiment of the present invention, the model building unit is specifically configured to calculate the simulation time step corresponding to the current growth stage by using the following formula

：

In an embodiment of the present invention, all events further include null events, where a rate of formation of the null events is a set value.

In an embodiment of the present invention, the model construction unit, when parameter-coupling the electrochemical model and the aging model, is specifically configured to calculate an SEI film thickness in a battery aging process by using the aging model, and calculate a variation of a target input parameter in the electrochemical model based on the calculated SEI film thickness; the target input parameters are input parameters influenced by an SEI film; recalculating a value of an output parameter using the electrochemical model based on the amount of change in the target input parameter; wherein the output parameters of the electrochemical model comprise part of the input parameters of the aging model.

It is to be understood that the structure illustrated in the embodiment of the present invention does not constitute a specific limitation to one type of battery SOC estimation device. In other embodiments of the invention, a battery SOC estimation apparatus may include more or fewer components than shown, or combine certain components, or split certain components, or a different arrangement of components. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

Because the content of information interaction, execution process, and the like among the modules in the device is based on the same concept as the method embodiment of the present invention, specific content can be referred to the description in the method embodiment of the present invention, and is not described herein again.

The embodiment of the invention also provides electronic equipment which comprises a memory and a processor, wherein the memory is stored with a computer program, and when the processor executes the computer program, the battery SOC estimation method in any embodiment of the invention is realized.

An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, causes the processor to execute a method for estimating a battery SOC according to any embodiment of the present invention.

Specifically, a system or an apparatus equipped with a storage medium on which software program codes that realize the functions of any of the above-described embodiments are stored may be provided, and a computer (or a CPU or MPU) of the system or the apparatus is caused to read out and execute the program codes stored in the storage medium.

In this case, the program code itself read from the storage medium can realize the functions of any of the above-described embodiments, and thus the program code and the storage medium storing the program code constitute a part of the present invention.

Examples of the storage medium for supplying the program code include a floppy disk, a hard disk, a magneto-optical disk, an optical disk (e.g., CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD + RW), a magnetic tape, a nonvolatile memory card, and a ROM. Alternatively, the program code may be downloaded from a server computer via a communications network.

Further, it should be clear that the functions of any one of the above-described embodiments may be implemented not only by executing the program code read out by the computer, but also by causing an operating system or the like operating on the computer to perform a part or all of the actual operations based on instructions of the program code.

Further, it is to be understood that the program code read out from the storage medium is written to a memory provided in an expansion board inserted into the computer or to a memory provided in an expansion module connected to the computer, and then causes a CPU or the like mounted on the expansion board or the expansion module to perform part or all of the actual operations based on instructions of the program code, thereby realizing the functions of any of the above-described embodiments.

It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising a" \8230; "does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.

Those of ordinary skill in the art will understand that: all or part of the steps for realizing the method embodiments can be completed by hardware related to program instructions, the program can be stored in a computer readable storage medium, and the program executes the steps comprising the method embodiments when executed; and the aforementioned storage medium includes: ROM, RAM, magnetic or optical disks, etc. that can store program codes.

Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims

1. A battery SOC estimation method, comprising:

determining parameters used for calculating the battery terminal voltage and measurable parameters outside the battery in a battery simulation model which is constructed in advance as initial parameters used for estimating the SOC of the battery; the parameters for calculating the terminal voltage of the battery include: at least one of a cell open circuit voltage, a reactive polarization overpotential, a concentration polarization overpotential, and an ohmic polarization overpotential; the battery external measurable parameters include: at least one of current, terminal voltage, and temperature;

estimating the battery SOC by using the battery SOC estimation model;

the method for constructing the battery simulation model comprises the following steps:

performing parameter coupling on the electrochemical model and the aging model to obtain the battery simulation model;

monte carlo simulation of SEI film growth in the spatial network in each growth phase was performed as follows:

2. The method of claim 1, wherein the initial parameters comprise: cell open circuit voltage, reactive polarization overpotential, concentration polarization overpotential, ohmic polarization overpotential, current, terminal voltage, and temperature.

3. The method according to claim 1 or 2,

when the initial parameter is a parameter used for calculating the terminal voltage of the battery, the sampling data of the initial parameter is obtained based on the simulation of the battery simulation model;

and when the initial parameter is a measurable parameter outside the battery, obtaining the sampling data of the initial parameter based on experimental measurement.

4. The method of claim 1,

calculating a formation rate of the SEI film of the vacancy grid using a first equation, a second equation, and a third equation as follows:

wherein Γ is the rate of formation of an SEI film of a vacancy grid, K is the reaction rate constant of the SEI film, F is the Faraday constant, R is the ideal gas constant, T is the battery temperature, η _SEI Is the local overpotential of the SEI film, J _s,0 Is the exchange current density of the SEI film formation, a ₀ Is the floor area of the individual cells, N _A Is the Avogastron constant, U _SEI Is a reaction equilibrium potential of the SEI film,

is the potential of the solid phase,

5. The method of claim 1,

the determining the target vacancy grid of the SEI film growing in the current growth stage comprises the following steps:

generating a first random number, and determining an event with the occurrence probability closest to the first random number as an event required to be executed in the current growth stage; the first random number is a uniform random number which is greater than 0 and less than 1, and the vacancy grid corresponding to the event closest to the first random number is a target vacancy grid.

6. The method of claim 1,

：

7. The method of claim 1, wherein parametrically coupling the electrochemical model and the aging model comprises:

8. A battery SOC estimation device, characterized by comprising:

an estimation unit configured to perform battery SOC estimation using the battery SOC estimation model;

the model building unit is used for building an electrochemical model of the battery based on battery parameters and a battery internal reaction mechanism; establishing a spatial network for SEI film growth at the negative electrode of the battery; the bottom surface of the space network is the surface of the battery cathode particles, and the height direction of the space network is the radial direction of the battery cathode particles; the space network is divided into a plurality of layers of grids from the bottom surface to the height direction; starting from a bottom grid of a plurality of layers of grids in the space network, carrying out Monte Carlo simulation on the growth of an SEI film in the space network to obtain an aging model of the battery; performing parameter coupling on the electrochemical model and the aging model to obtain the battery simulation model;

the model construction unit is specifically configured to perform monte carlo simulation on the SEI film growth in the spatial network in each growth phase in the following manner: determining vacancy grids of the SEI film to be grown in the current growth stage based on non-vacancy grids in the spatial network in the last growth stage; the non-vacancy grids are grids on which an SEI film is grown; respectively calculating the forming rate of the SEI film aiming at each vacancy grid of the SEI film to be grown in the current growth stage; and respectively taking each vacancy grid as an SEI film growth event, determining a target vacancy grid for growing the SEI film in the current growth stage by combining the forming rate, and calculating the simulation time step corresponding to the current growth stage.

9. An electronic device comprising a memory in which a computer program is stored and a processor which, when executing the computer program, carries out the method according to any one of claims 1-7.

10. A computer-readable storage medium, on which a computer program is stored which, when executed in a computer, causes the computer to carry out the method of any one of claims 1-7.