CN115494406A - Battery parameter identification method and device and electronic equipment - Google Patents
Battery parameter identification method and device and electronic equipment Download PDFInfo
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Abstract
The invention provides a battery parameter identification method, a battery parameter identification device and electronic equipment, wherein the method comprises the following steps: determining model parameters included in a preset battery simulation model and determining the sensitivity of each model parameter; the sensitivity degree is used for representing the influence degree of the change of the model parameter on the simulation voltage output by the battery simulation model; determining the time identification interval of each model parameter according to the sensitivity degree corresponding to each model parameter; wherein, the higher the sensitivity degree is, the smaller the time identification interval is; based on battery measurement data uploaded by a client, identifying model parameter values according to time identification intervals corresponding to the model parameters, and synchronizing the identified model parameter values to the client so as to update the battery simulation model set in the client. According to the scheme, the cost can be reduced, and the identification accuracy can be improved.
Description
Technical Field
The embodiment of the invention relates to the technical field of batteries, in particular to a battery parameter identification method and device and electronic equipment.
Background
In the field of battery technology, it is generally necessary to obtain real-time parameters of a battery when estimating the state of the battery. Currently, the battery parameter identification method generally includes two methods, i.e., off-line parameter identification and on-line parameter identification. However, the offline identified parameters are less and less suitable for the battery aging state, and the online parameter identification method is higher in cost. Therefore, it is desirable to provide a low-cost and highly accurate identification method.
Disclosure of Invention
The embodiment of the invention provides a battery parameter identification method and device and electronic equipment, which can reduce the cost and improve the identification accuracy.
In a first aspect, an embodiment of the present invention provides a method for identifying battery parameters, including:
determining model parameters included in a preset battery simulation model and determining the sensitivity of each model parameter; the sensitivity degree is used for representing the influence degree of the change of the model parameter on the simulation voltage output by the battery simulation model;
determining the time identification interval of each model parameter according to the sensitivity degree corresponding to each model parameter; wherein, the higher the sensitivity degree is, the smaller the time identification interval is;
based on battery measurement data uploaded by a client, identifying model parameter values according to time identification intervals corresponding to the model parameters, and synchronizing the identified model parameter values to the client so as to update the battery simulation model set in the client.
In a possible implementation manner, the determining the sensitivity of each model parameter includes:
determining an identification value and a simulation voltage value of each model parameter in advance based on an off-line identification method, and determining a first average absolute error of the simulation voltage value and a measured voltage value;
determining a value range corresponding to each model parameter, and aiming at each model parameter, executing the following steps: selecting a plurality of average values in the value range corresponding to the model parameters, inputting each average value into the battery simulation model to obtain a corresponding simulation voltage value, and determining a second average absolute error corresponding to each average value; and determining the ratio of the maximum second average absolute error to the first average absolute error in the model parameter as the sensitivity corresponding to the model parameter.
In one possible implementation, the identification of the model parameter values is performed using a cuckoo search algorithm as a meta-heuristic algorithm.
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 spatial network, carrying out Monte Carlo simulation on the growth of an SEI film in the spatial 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 subjected to monte carlo simulation 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 the SEI film formation, a 0 Is the floor area of the individual cells, N A Is an Avogadro constant, U SEI Is a 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 probability of occurrence of the event as a quotient of the formation rate of an SEI film corresponding to the event divided 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 device for identifying battery parameters, including:
the first determining unit is used for determining model parameters included in a preset battery simulation model and determining the sensitivity of each model parameter; the sensitivity degree is used for representing the influence degree of the change of the model parameter on the simulation voltage output by the battery simulation model;
the second determining unit is used for determining the time identification interval of each model parameter according to the sensitivity corresponding to each model parameter; wherein, the higher the sensitivity degree is, the smaller the time identification interval is;
and the identification unit is used for identifying the model parameter values according to the time identification intervals corresponding to the model parameters based on the battery measurement data uploaded by the client, and synchronizing the identified model parameter values to the client so as to update the battery simulation model set in the client.
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 when the processor executes the computer program, the processor implements the method described in any embodiment of this specification.
In a fourth aspect, an embodiment of 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 parameter identification method, a battery parameter identification device and electronic equipment, wherein the sensitivity degree of each model parameter in a battery simulation model is determined, so that the sensitivity degree is utilized to determine the time identification interval corresponding to the model parameter, the time identification intervals of the model parameters with different sensitivity degrees are different, and the time identification interval for the model parameter with high sensitivity degree is small, so that all the model parameters do not need to be identified simultaneously during online identification every time, only the model parameters with different sensitivity degrees are identified according to the corresponding time identification intervals, the accuracy of the battery simulation model can be ensured, and the identification cost can be reduced.
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 illustrating a method for identifying battery parameters 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 diagram of a hardware architecture of an electronic device according to an embodiment of the present invention;
FIG. 5 is a diagram of a battery parameter identification apparatus according to an embodiment of the present invention;
fig. 6 is a structural diagram of another battery parameter identification device according to an embodiment of the invention.
Detailed Description
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, 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 making creative efforts belong to the protection scope of the present invention.
Referring to fig. 1, an embodiment of the present invention provides a method for identifying battery parameters, including:
and 104, identifying model parameter values according to time identification intervals corresponding to the model parameters based on battery measurement data uploaded by the client, and synchronizing the identified model parameter values to the client so as to update the battery simulation model set in the client.
In the embodiment of the invention, the sensitivity degree of each model parameter in the battery simulation model is determined, so that the time identification intervals of the corresponding model parameters are determined by using the sensitivity degree, the time identification intervals of the model parameters with different sensitivity degrees are different, and the time identification interval for the model parameter with high sensitivity degree is small, so that all the model parameters do not need to be identified simultaneously during each online identification, only the model parameters with different sensitivity degrees need to be identified according to the corresponding time identification intervals, the accuracy of the battery simulation model can be ensured, and the identification cost can be reduced.
The manner in which the various steps shown in fig. 1 are performed is described below.
Firstly, aiming at step 100, determining model parameters included in a preset battery simulation model and determining the sensitivity of each model parameter; the sensitivity degree is used for representing the influence degree of the change of the model parameter on the simulation voltage output by the battery simulation model.
In the embodiment of the invention, the battery simulation model can be only an electrochemical model, and can also be a simulation model formed by coupling the electrochemical model and an aging model. In order to ensure the accuracy of the battery simulation model, the battery simulation model in the embodiment of the present invention is obtained by coupling an electrochemical model and an aging model, and the specific construction process refers to fig. 2:
Wherein the battery parameters at least comprise size parameters, kinetic parameters and thermodynamic parameters 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:
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 The solid phase diffusion coefficient of lithium ions.
The liquid phase diffusion process describes the change rule of the liquid phase lithium ion concentration, and the control equation is as follows:
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:
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:
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, which is described by the equation:
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:
wherein E is ocv Is the open circuit voltage of the battery, R SEI Is internal resistance of SEI filmAnd j is the lithium ion flux density.
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:
wherein x is ave Average amount of lithium inserted into 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 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:
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:
wherein k is i I = n, p is the rate constant of the positive and negative electrodes, 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 Solid phase surface lithium insertion amount y surf And x surf And an approximate calculation formula for the reactant ion current density j, the transformation for equation (10) can be:
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:
wherein, c 1 And c 2 The concentrations of liquid-phase lithium ions on 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:
wherein R is ohm Is a lumped parameter expression 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:
wherein U is a battery terminal voltage, E OCV Is the open circuit voltage, eta, of the battery act To react the polarization overpotential, eta con To polarize the overpotential for concentration 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.
And step 204, starting from the bottom layer grid of the multilayer grid in the space network, carrying out Monte Carlo simulation on the growth of the SEI film in the space network to obtain an aging model of the battery.
The relevant parameters of the aging model are shown in the following table 2:
TABLE 2
The aging side reaction inside the battery mainly focuses 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 (KMC) method was used, considering the electrochemical kinetics equation, 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):
In the embodiment of the invention, the simulation time can be divided into a plurality of growth phases, so that the Monte Carlo simulation is respectively carried out on the growth of the SEI film aiming at each growth phase.
Since the SEI film growth is continuously growing in the height direction, when determining the mesh of the vacancies of the SEI film to be grown in the current growth stage, the maximum z value of the mesh of non-vacancies at each (x, y) position in the previous growth stage may be determined, and then the mesh at the position corresponding to the sum of each maximum z value and 1 may be determined as the mesh of vacancies of the SEI film to be grown in the current growth stage.
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 SEI film formation, a 0 Is the base area of a single grid, N A Is an Avogadro constant, U SEI Is the 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:
wherein eta n Is the reactive polarization overpotential of the negative electrode, E ocp,n Is the open circuit potential of the cathode.
When a defect such as a bend or a step occurs on the surface of the SEI film, the probability of generating particles in the underlying mesh is considered to be greater than that on the terrace. 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 thereof is 2 times higher than the formation rate of other non-vacancy grids which do 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 formation rate.
In this step 304, the determining the target vacancy grid for growing the SEI film in the current growth stage may include:
s1, determining the probability of occurrence of each event by dividing the formation rate of an SEI film corresponding to the event by the sum of the formation rates of all event SEI films;
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 an empty event, i.e., an event in which no transition reaction process occurs, and the calculation cost may be reduced using an empty 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 grid of target vacancies per growth phase growth SEI, then the grid of target vacancies for the current growth phase growth SEI film may be determined in this step 204 by the following formula:
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
：
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 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 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.
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:
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:
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:
wherein h is ave Is the average number of the grown SEI film molecules in the height direction, a 0 Is the base area of the individual cells, R n Is the radius of the anode active material particle, F isFaraday 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:
wherein Q all,0 Is the initial capacity of the battery.
According to the definition of the current, the side reaction current I caused by the SEI film generated on the surface of the particles can be obtained SEI ：
Wherein delta (Delta Q) is the difference of lithium ion charge amount, delta t KMC Is the simulated time step of the KMC.
Reaction current I of negative 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 (c):
where I is the current applied by the external circuit.
In addition, as can be seen from equation (17), the output parameters of the electrochemical model, including some of the input parameters of the aging model, are: reaction equilibrium potential U of SEI film SEI Solid phase potential
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.
The relevant parameters related to the battery simulation model are the model parameters included in the battery simulation model in step 100.
In an embodiment of the present invention, the determination manner of the sensitivity of each model parameter may include A1-A2:
a1, determining an identification value and a simulation voltage value of each model parameter in advance based on an off-line identification method, and determining a first average absolute error of the simulation voltage value and a measured voltage value;
the simulation voltage value is the terminal voltage which is output by inputting the identification value of the model parameter into the battery simulation model. The measured voltage value is the terminal voltage obtained by current actual measurement. The first average absolute error is the absolute value of the difference between the simulated voltage value and the measured voltage value.
A2, determining a value range corresponding to each model parameter, and executing the following steps aiming at each model parameter: selecting a plurality of average values in the value range corresponding to the model parameters, inputting each average value into the battery simulation model to obtain a corresponding simulation voltage value, and determining a second average absolute error corresponding to each average value; and determining the ratio of the maximum second average absolute error to the first average absolute error in the model parameter as the sensitivity corresponding to the model parameter.
The value range of the model parameter can be determined according to an empirical value or a historical identification value.
In step A2, selecting a plurality of average values within the value range is to ensure the balance of the values and reduce the influence on the sensitivity, for example, the value range is 1 to 2, and then the values of 1, 1.2, 1.4, 1.6, 1.8 and 2 may be selected. When the current model parameter is valued, the identification values are used for other model parameters, so that the simulation voltage value corresponding to each average value can be obtained, and the second average absolute error obtained by each average value is obtained.
In the embodiment of the present invention, the sensitivity is defined as: and when the model parameters are changed in the value range, the ratio of the maximum value in the second average absolute error to the first average absolute error. The larger the ratio, the higher the sensitivity.
The identification value used when the sensitivity degree of the model parameter is determined can be obtained by an off-line identification method, and the sensitivity degree can be directly applied to the subsequent identification process after being determined without determining the sensitivity degree again.
Then, aiming at the step 102, determining the time identification interval of each model parameter according to the sensitivity degree corresponding to each model parameter; wherein the higher the sensitivity, the smaller the time identification interval.
In an embodiment of the invention, since the higher the sensitivity is, the greater the influence of the sensitivity on the battery simulation model is, in order to ensure the accuracy of the simulation result of the battery simulation model, the model parameters with high sensitivity need to be identified in time so as to ensure the accuracy of the model parameters with high sensitivity. Therefore, the model parameters can be classified according to the sensitivity degree, and different time identification intervals are executed according to different classifications, so that the accuracy of the battery simulation model is ensured, and the identification cost can be reduced. One classification method is as follows: high sensitive parameters, low sensitive parameters and insensitive parameters.
One way to set the time identification interval is: the high sensitive parameter-the battery is charged and discharged once and then is identified once, the low sensitive parameter-the battery is charged and discharged three times and then is identified once, and the non sensitive parameter-the battery is charged and discharged ten times and then is identified once.
It should be noted that the above-mentioned classification method and the setting method of the time identification interval are only examples, and other classification methods and setting methods of the time identification interval may be used according to actual needs.
Finally, in step 104, based on the battery measurement data uploaded by the client, the model parameter values are identified according to the time identification intervals corresponding to the model parameters, and the identified model parameter values are synchronized to the client, so that the battery simulation model set in the client is updated.
In one embodiment of the invention, in order to improve the accuracy of parameter identification, the identification process can be placed in a cloud, the client uploads the battery measurement data to the cloud, the cloud identifies the model parameter values according to the time identification intervals corresponding to the model parameters, the model parameter values are synchronized to the client after identification is completed, and the client updates the battery simulation model according to the identified model parameter values, so that the simulation accuracy of the battery simulation model of the client can be ensured.
In addition, the cloud can also update the battery simulation model to form a digital twin body of the battery system, and the digital twin body is connected with each battery system through a network and comprises a database, a model library, a human-computer interaction interface and the like.
The identification of the model parameter values may be implemented in the existing manner, and one implementation manner may be to use Cuckoo Search Algorithm (CSA) as a meta heuristic Algorithm to perform the identification of the model parameter values.
CSA defines three rules that simplify the breeding behavior of cuckoo: (1) Each cuckoo can only lay one egg at a time, and the eggs are placed in randomly selected nests; (2) In a randomly selected set of nests, the best nest will be retained to the next generation; (3) The number of available bird nests is fixed, the probability of laying eggs by cuckoos is found by host birds and ranges from 0 to 1, and in the case, the host birds throw away the eggs or discard the bird nests in short time, so that a brand-new bird nest is built.
According to the above rules, CSA is implemented by the following steps:
step 1: defining an objective function, inputting initialization parameter values, setting the number of bird nests as n, initializing the positions of the bird nests, and finding out the position of the optimal bird nest according to the objective function.
Wherein the minimum root mean square error of the simulated voltage value of the battery simulation model and the measured voltage value of the battery may be determined as the objective function.
In step 1, the initialization parameter value may be a set value, or a parameter to be identified may be preliminarily identified by a deep learning method. Specifically, in the embodiment of the present invention, a Convolutional Neural Network (CNN) is used to perform preliminary parameter identification.
The input data of training and testing are the voltage, current, temperature and SOC of the battery obtained under the experimental working condition, and the output data are parameters of a battery simulation model obtained by using an off-line identification method. To generate a good data set, experiments can be performed with different aging states of the battery.
For application to CNN, input data needs to be preprocessed and converted into a three-dimensional data structure. Voltage, current, temperature and SOC time series data of 100 sampling points are taken to construct a 10 × 10 × 4 three-dimensional data structure. In the preprocessed data, each channel represents voltage, current, temperature, and SOC, respectively.
The built CNN model comprises an input layer, a convolution layer, a pooling layer, a full-connection layer and an output layer. The convolutional layer is a filter that extracts features from an image by convolution operation, the pooling layer reduces the dimension of an input image by a rule such as maximum pooling or average pooling, and the full link layer is coupled to the end of the CNN to smooth the output. Initializing parameters of a neural network, initializing a convolution kernel, bias, weight and the like into a random number, and setting hyper-parameters such as learning rate, iteration times and the like.
And 2, step: the position of the last generation of the optimal bird nest is reserved, and the positions and the states of other bird nests are updated by utilizing the Laevir flight, wherein the equation is as follows:
wherein, the position of the ith bird nest in the tth search, α is a step control quantity, which is usually 1, represents point-to-point multiplication, and L () is a lewy random search path, obeys the lewy probability distribution, and may be represented as:
for ease of calculation, the Levin random number is generated using the following equation:
where u and v follow a standard normal distribution, β =1.5, and Φ can be calculated by:
where Γ () is a standard gamma function.
And step 3: and comparing with the bird nest positions generated in the previous generation, and replacing the bird nest positions with the poor adaptation values by the bird nest positions with the good adaptation values to obtain a group of better bird nest positions.
And 4, step 4: after the position is updated, comparing the random number between 0 and 1 with the probability of finding the external bird egg by the host bird, if the random number is greater than the probability of finding the external bird egg, randomly changing the position of the bird nest, otherwise, keeping the position unchanged, and finally keeping the best group of bird nest positions.
The equation for the bird's nest position change then is:
wherein, the first and the second end of the pipe are connected with each other,
and
for two different randomly selected positions, s is the step size, H () is the Heaviside function, epsilon is the random number extracted in the uniform distribution between 0 and 1, and p a Is the probability that an egg is found by a host bird.
And 5: if the requirement of the maximum iteration times or the minimum error is not met, returning to the step 2; and if the maximum iteration times or the minimum error requirement is met, outputting the global optimal position.
As shown in fig. 4 and 5, an embodiment of the invention provides a battery parameter identification 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 parameter identification device 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 device 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 logically meaningful device, the device is formed by reading a corresponding computer program in a nonvolatile memory into a memory by a CPU of an electronic device where the device is located and running the computer program. The battery parameter identification device provided by the embodiment comprises:
a first determining unit 501, configured to determine model parameters included in a preset battery simulation model and determine a sensitivity of each model parameter; the sensitivity degree is used for representing the influence degree of the change of the model parameter on the simulation voltage output by the battery simulation model;
a second determining unit 502, configured to determine a time identification interval of each model parameter according to the sensitivity corresponding to each model parameter; wherein, the higher the sensitivity degree is, the smaller the time identification interval is;
the identifying unit 503 is configured to identify a model parameter value according to a time identification interval corresponding to the model parameter based on the battery measurement data uploaded by the client, and synchronize the identified model parameter value to the client, so as to update the battery simulation model set in the client.
In an embodiment of the present invention, when determining the sensitivity of each model parameter, the first determining unit is specifically configured to: determining an identification value and a simulation voltage value of each model parameter in advance based on an off-line identification method, and determining a first average absolute error of the simulation voltage value and a measured voltage value; determining a value range corresponding to each model parameter, and aiming at each model parameter, executing the following steps: selecting a plurality of average values in the value range corresponding to the model parameter, inputting each average value into the battery simulation model to obtain a corresponding simulation voltage value, and determining a second average absolute error corresponding to each average value; and determining the ratio of the maximum second average absolute error in the model parameter to the first average absolute error as the sensitivity corresponding to the model parameter.
In an embodiment of the present invention, the identification unit performs the identification of the parameter values of the ongoing model by using a cuckoo search algorithm as a meta-heuristic algorithm.
In an embodiment of the present invention, please refer to fig. 6, further comprising a model building unit 504, specifically configured to:
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 a battery cathode; 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 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:
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 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 formation rate of the SEI film of the vacancy lattice, K is the reaction rate constant of the SEI film, F is the Faraday constant, R is the ideal gas constant, T is the cell 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 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,
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.
In an embodiment of the present invention, the model constructing 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
：
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 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 illustrated structure of the embodiment of the invention does not specifically limit a battery parameter identification device. In other embodiments of the invention, a battery parameter identification device may include more or fewer components than shown, or some components may be combined, some components may be separated, 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 parameter identification method in any embodiment of the invention is realized.
An embodiment of the present invention further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the computer program causes the processor to execute a battery parameter identification method in 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 embodiments described above 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 one of 8230" does not exclude the presence of additional like elements in a process, method, article, or apparatus comprising 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: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, and 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 (10)
1. A battery parameter identification method is characterized by comprising the following steps:
determining model parameters included in a preset battery simulation model and determining the sensitivity of each model parameter; the sensitivity degree is used for representing the influence degree of the change of the model parameter on the simulation voltage output by the battery simulation model;
determining the time identification interval of each model parameter according to the sensitivity degree corresponding to each model parameter; wherein, the higher the sensitivity degree is, the smaller the time identification interval is;
based on battery measurement data uploaded by a client, identifying model parameter values according to time identification intervals corresponding to the model parameters, and synchronizing the identified model parameter values to the client so as to update the battery simulation model set in the client.
2. The method of claim 1, wherein determining the sensitivity of each model parameter comprises: determining an identification value and a simulation voltage value of each model parameter based on an off-line identification method in advance, and determining a first average absolute error between the simulation voltage value and a measured voltage value;
determining a value range corresponding to each model parameter, and aiming at each model parameter, executing the following steps: selecting a plurality of average values in the value range corresponding to the model parameter, inputting each average value into the battery simulation model to obtain a corresponding simulation voltage value, and determining a second average absolute error corresponding to each average value; and determining the ratio of the maximum second average absolute error in the model parameter to the first average absolute error as the sensitivity corresponding to the model parameter.
3. The method of claim 1, wherein the identifying model parameter values is performed using a cuckoo search algorithm as a meta-heuristic algorithm.
4. The method of claim 1, wherein the method of constructing the battery simulation model comprises:
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 spatial network, carrying out Monte Carlo simulation on the growth of an SEI film in the spatial 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.
5. The method of claim 4, wherein the growth of the SEI film in the spatial network in each growth phase is subjected to Monte Carlo simulation 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.
6. The method of claim 5,
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 formation rate of the SEI film of the vacancy lattice, K is the reaction rate constant of the SEI film, F is the Faraday constant, R is the ideal gas constant, T is the cell 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 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 atmosphere,
the determining the target vacancy grid of the SEI film growing in the current growth stage comprises the following steps:
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 atmosphere,
calculating the simulation time step length corresponding to the current growth stage by using the following formula
：
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.
7. The method of claim 4, wherein parametrically coupling the electrochemical model and the aging model comprises:
calculating the SEI film thickness in the battery aging process by using the aging model, and calculating the 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.
8. A battery parameter identification device, comprising:
the first determining unit is used for determining model parameters included in a preset battery simulation model and determining the sensitivity of each model parameter; the sensitivity degree is used for representing the influence degree of the change of the model parameter on the simulation voltage output by the battery simulation model;
the second determining unit is used for determining the time identification interval of each model parameter according to the sensitivity corresponding to each model parameter; wherein, the higher the sensitivity degree is, the smaller the time identification interval is;
and the identification unit is used for identifying the model parameter values according to the time identification intervals corresponding to the model parameters based on the battery measurement data uploaded by the client, and synchronizing the identified model parameter values to the client so as to update the battery simulation model set in the client.
9. An electronic device comprising a memory having stored therein a computer program and a processor that, when executing the computer program, implements the method of any 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.
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