CN115101138A - Lithium battery design optimization method and device based on parameter identification and storage medium - Google Patents
Lithium battery design optimization method and device based on parameter identification and storage medium Download PDFInfo
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Abstract
The invention provides a lithium battery design optimization method and device based on parameter identification and a storage medium, wherein the method comprises the following steps: acquiring a first type of parameter of an electrochemical model corresponding to a sample battery; acquiring external performance data of a sample battery after formation; identifying a second type of parameter of the electrochemical model based on the data-driven manner according to the first type of parameter and the external performance data; constructing an electrochemical model according to the first type of parameters and the second type of parameters; adjusting adjustable parameters of the electrochemical model, obtaining an output voltage curve of the electrochemical model after the parameters are changed, calculating a corresponding optimized target value according to the output voltage curve, and selecting the parameter which enables the optimized target value to be maximum in the adjustable range of the adjustable parameters as a target battery parameter. The invention greatly reduces the time and cost of battery design while optimizing the performance of the battery.
Description
Technical Field
The invention relates to the field of battery design, in particular to a lithium battery design optimization method and device based on parameter identification and a storage medium.
Background
In a traditional design method of a lithium battery system, various battery samples need to be generated by adjusting the size, the formula, the coating thickness, the compaction density and the like of a battery, and then the samples are tested to select an optimal design. The design method is complex, long in development period and high in cost.
As the simulation accuracy of the electrochemical model of the battery becomes higher and higher, some simulation software, such as comsol, etc., has played a role in the design of the battery. However, the simulation using the electrochemical model involves dozens of physical parameters which need to be input, some of the parameters need to be obtained by complicated chemical experiments, some of the parameters such as solid phase diffusion coefficient, conductivity, active material particle size and the like have certain difficulty even if obtained by experiments, and in short, the parameter obtaining requires high time and economic cost, so that the optimization of the battery design by a simulation software simulation method has certain limitations.
Disclosure of Invention
The invention aims to provide a lithium battery design optimization method and device based on parameter identification and a storage medium, which are used for solving the problems of long battery design optimization time and high cost in the prior art.
The technical scheme provided by the invention is as follows:
a lithium battery design optimization method based on parameter identification comprises the following steps: obtaining a first type of parameter of an electrochemical model corresponding to a sample cell, wherein the first type of parameter is obtained in the manufacturing process of the sample cell;
obtaining external performance data after the sample battery is formed, wherein the external performance data is obtained by testing the sample battery;
identifying a second type of parameter of the electrochemical model based on a data-driven manner based on the first type of parameter and the external performance data;
constructing the electrochemical model according to the first type of parameters and the second type of parameters;
adjusting adjustable parameters of the electrochemical model to obtain an output voltage curve of the electrochemical model after parameter change, calculating a corresponding optimized target value according to the output voltage curve, and selecting the parameter which enables the optimized target value to be maximum in the adjustable range of the adjustable parameters as a target battery parameter.
In some embodiments, the external performance data after the sample cell formation comprises: the open-circuit potential of the anode material and the stoichiometric number are respectively calculated according to the open-circuit potential of the anode material and the stoichiometric number, wherein the stoichiometric number is the ratio of the surface lithium ion concentration of the active material to the maximum lithium ion concentration of the active material; and/or, the voltage curve of the sample cell is below a first rate, the first rate is no greater than 1/20C; and/or the voltage curve of the sample battery under the second multiplying power under the constant-current working condition, wherein the second multiplying power is larger than the first multiplying power; and/or a voltage curve of the sample cell under dynamic conditions.
In some embodiments, said identifying a second type of parameter of said electrochemical model based on a data-driven manner based on said first type of parameter and said external performance data comprises:
and identifying the stoichiometric number of the positive electrode active material and the stoichiometric number of the negative electrode active material when the SOC is equal to 0 and the stoichiometric number when the SOC is equal to 100% based on a data driving mode according to the first type of parameter, the relation curve between the open-circuit potential and the stoichiometric number of the positive electrode material, the relation curve between the open-circuit potential and the stoichiometric number of the negative electrode material and the voltage curve of the sample battery under the condition of being lower than a first multiplying factor.
In some embodiments, said identifying a second type of parameter of said electrochemical model based on a data-driven manner based on said first type of parameter and said external performance data further comprises: and identifying high-sensitivity parameters except stoichiometric numbers based on a data driving mode according to the first type of parameters and the voltage curve of the sample battery under the constant-current working condition.
In some embodiments, after identifying the high sensitivity parameter other than the stoichiometric number, further comprising: and identifying the medium-low sensitivity parameter of the second type of parameter based on a data driving mode according to the high sensitivity parameter of the first type of parameter and the second type of parameter and the voltage curve of the sample battery under the dynamic working condition.
In some embodiments, the identifying a second type of parameter of the electrochemical model based on a data-driven manner comprises:
and identifying the second type of parameters of the electrochemical model by adopting a heuristic algorithm or a neural network model or Kalman filtering.
In some embodiments, the heuristic algorithm comprises a cuckoo algorithm, a genetic algorithm, a particle swarm algorithm.
In some embodiments, the adjusting an adjustable parameter of the electrochemical model comprises: automatically adjusting the adjustable parameters of the electrochemical model by a heuristic algorithm.
The invention also provides a device for calculating the solid-phase current density in the electrochemical model of the battery, which comprises:
the device comprises a first acquisition module, a second acquisition module and a control module, wherein the first acquisition module is used for acquiring first type parameters of an electrochemical model corresponding to a sample battery, and the first type parameters are acquired in the manufacturing process of the sample battery;
the second acquisition module is used for acquiring external performance data after the sample battery is formed, wherein the external performance data is obtained by testing the sample battery;
an identification module for identifying a second type of parameter of the electrochemical model based on a data-driven manner based on the first type of parameter and the external performance data;
the model establishing module is used for establishing the electrochemical model according to the first type of parameters and the second type of parameters;
the model optimization module is used for determining an optimization target and setting adjustable parameters and adjustable ranges of the electrochemical model; and adjusting the adjustable parameters, calculating a corresponding optimized target value according to the electrochemical model after the parameters are changed, and selecting the parameter which enables the optimized target value to be maximum in the adjustable range of the adjustable parameters as the target battery parameter.
The invention further provides a computer-readable storage medium, on which a computer program is stored, and the computer program, when executed by a processor, implements any of the foregoing methods for optimizing a lithium battery design based on parameter identification.
Compared with the prior art, the lithium battery design optimization method and device based on parameter identification and the storage medium provided by the invention can at least bring the following beneficial effects:
1. according to the invention, the first type of parameters of the electrochemical model are obtained by measurement or calculation in the battery manufacturing process, so that the number of parameters to be identified is reduced, and the identification precision is higher as the number of identified parameters is less.
2. According to the invention, the external performance data of the battery is obtained by testing the sample battery, and other parameters of the battery material are obtained based on a data-driven mode.
3. According to the invention, some parameters of the model are optimized through the electrochemical model, the external performance of the battery is improved, and the research and development period and the cost of the battery are reduced.
Drawings
The above features, technical features, advantages and implementations of a method and apparatus for optimizing a lithium battery design based on parameter identification will be further described in the following detailed description of preferred embodiments with reference to the accompanying drawings.
FIG. 1 is a flow chart of one embodiment of a method for optimizing a lithium battery design based on parameter identification according to the present invention;
FIG. 2 is a schematic structural diagram of an embodiment of a lithium battery design optimization device based on parameter identification according to the present invention;
FIG. 3 is a schematic representation of the relationship of the OCP of the positive electrode material to the stoichiometric number;
fig. 4 is a schematic diagram of the relationship of the negative electrode material OCP to the stoichiometric number;
FIG. 5 is a graphical representation of voltage data for a sample cell at 1/30C;
FIG. 6 is a graphical illustration of dynamic regime 1 (pulse test) current data;
FIG. 7 is a graphical representation of dynamic Condition 1 (pulse test) voltage data;
FIG. 8 is a graphical illustration of current data for dynamic regime 2;
FIG. 9 is a graphical illustration of voltage data for dynamic regime 2;
FIG. 10 is a graph of variation in positive electrode active material volume fraction and thickness parameter values found by a heuristic algorithm with mass energy density at 1C current magnification as a target function;
FIG. 11 is a mass-energy density relationship corresponding to initial and optimized parameters at different current magnifications.
Detailed Description
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following description will be made with reference to the accompanying drawings. It is obvious that the drawings in the following description are only some examples of the invention, and that for a person skilled in the art, other drawings and embodiments can be derived from them without inventive effort.
For the sake of simplicity, the drawings only schematically show the parts relevant to the present invention, and they do not represent the actual structure as a product. Moreover, in the interest of brevity and understanding, only one of the components having the same structure or function is illustrated schematically or designated in some of the drawings. In this document, "one" means not only "only one" but also a case of "more than one".
In an embodiment of the present invention, as shown in fig. 1, a method for optimizing a lithium battery design based on parameter identification includes:
step S100, obtaining a first type parameter of an electrochemical model corresponding to a sample battery, wherein the first type parameter is obtained in the manufacturing process of the sample battery;
step S200, obtaining external performance data after sample batteries are formed, wherein the external performance data are obtained by testing the sample batteries;
step S300, identifying a second type of parameter of the electrochemical model based on a data-driven mode according to the first type of parameter and external performance data;
step S400, constructing an electrochemical model according to the first type of parameters and the second type of parameters;
step S500, determining an optimization target, and setting adjustable parameters and adjustable ranges of an electrochemical model;
step S600 adjusts the adjustable parameters, obtains a corresponding optimized target value according to the electrochemical model after the parameters are changed, and selects a parameter that maximizes the optimized target value within the adjustable range of the adjustable parameters as a target battery parameter.
Specifically, the electrochemical model simulation requires a relatively large number of input parameters, as shown in the following table but not limited thereto:
| categories | Parameter(s) | Description of parameters | Sensitivity of the device |
| Geometric parameters | Lp | Thickness of positive electrode active material | Height of |
| Ln | Thickness of anode active material | High (a) | |
| Ls | Thickness of the diaphragm | In | |
| A | Active material area | Height of | |
| ε s | Porosity of the membrane | Is low in | |
| ε p | Volume fraction of positive electrode active material | Height of | |
| ε n | Volume fraction of anode active material | Height of | |
| Rp | Particle diameter of positive electrode active material | Height of | |
| Rn | Particle diameter of negative electrode active material | Height of | |
| Transmission parameters | Dp | Solid phase diffusion coefficient of positive electrode | Height of |
| Dn | Solid phase diffusion coefficient of negative electrode | Height of | |
| Ds | Diffusion coefficient of electrolyte | In | |
| Kinetic parameters | Kp | Positive electrode reaction rate constant | Height of |
| Kn | Rate constant of reaction of negative electrode | Height of | |
| Concentration parameter | C max,p | Maximum lithium ion concentration of the positive electrode | Height of |
| C max,n | Maximum lithium ion concentration of negative electrode | Height of |
In order for the simulation to be accurate, relatively accurate input parameters must be acquired. From the viewpoint of whether it is easy to determine an accurate value, input parameters of the electrochemical model are classified into two types: a first type of parameter and a second type of parameter. The first type of parameters are easily measurable parameters including parameters that can be directly measured during the manufacturing process of the battery, parameters that can be calculated from the measured values, and parameters provided by raw material suppliers. The second type of parameter is a parameter that is not easily measured, and includes a parameter that cannot be measured experimentally or a parameter that needs to be measured experimentally in a time-consuming and uneconomical manner.
The first type of parameter includes, but is not limited to, active material area A, positive active material thickness Lp, positive active material volume fraction ε p Thickness Ln of negative electrode active material, volume fraction ε of negative electrode active material n Membrane thickness Ls, membrane porosity epsilon s 。
The method comprises the steps of manufacturing a sample battery meeting the nominal capacity requirement according to preset positive and negative electrode materials, diaphragm materials and electrolyte materials, and obtaining first-class parameters of an electrochemical model through measurement or calculation in the manufacturing process of the sample battery, so that the number of model parameters needing to be identified can be reduced.
Parameter identification of the model refers to determining unknown parameters in the electrochemical model from readily available external performance data of the battery. In order to identify the second type of parameters, external performance data of the sample after battery formation, such as voltage and current during battery charging and discharging, is tested, and then data driving mode identification is adopted according to the external performance data. The data driving mode means that data is effectively and fully utilized through an artificial intelligence technology, and comprises a heuristic algorithm, a neural network model, Kalman filtering and the like. The external performance data after the sample battery formation is related to the method of driving the identification parameter by the corresponding data.
After all parameters of the model are obtained, some parameters of the model are optimized. Selecting some parameters as adjustable parameters, modifying parameter values in the adjustable range of the parameters, simulating the external performance of the battery through an electrochemical model, and determining the optimal parameter values according to the obtained external performance so as to determine the parameters of the target battery.
In the embodiment, a small number of sample batteries are manufactured firstly, and the first type of parameters of the electrochemical model corresponding to the sample batteries are obtained by measuring in the manufacturing process of the sample batteries, so that the number of the parameters to be identified is reduced; the second type of parameters of the electrochemical model which are not easy to measure are obtained based on a data driving mode through external performance data of the sample battery which are easy to measure, and compared with a measuring mode through a chemical experiment, a large amount of time is saved, and the method is more economical; the initial parameters of the model are optimized through the electrochemical model, the external performance of the battery is improved, sample batteries of various types do not need to be developed, and the research and development period and the cost of the battery are greatly reduced.
In one embodiment, the external performance data after sample cell formation includes: the relation curve between the open-circuit potential of the anode material and the stoichiometric number, and the relation curve between the open-circuit potential of the cathode material and the stoichiometric number, wherein the stoichiometric number is the ratio of the surface lithium ion concentration of the active material to the maximum lithium ion concentration of the active material; and/or the voltage curve of the sample battery under the constant current working condition is lower than that under a first multiplying power, and the first multiplying power is not more than 1/20C; and/or, the voltage curve of the sample battery under the second multiplying power under the constant current working condition, wherein the second multiplying power is larger than the first multiplying power; and/or a voltage profile of the sample cell under dynamic conditions.
The external performance data can be obtained by performing corresponding tests on the sample cell, wherein the curve of the relationship between the open-circuit potential and the stoichiometric number of the positive and negative electrode materials can also be searched from documents provided by material suppliers.
The second kind of parameters are divided into high sensitivity parameters and medium and low sensitivity parameters, the high sensitivity parameters refer to parameters influencing the output voltage of the electrochemical model under the constant current working condition, and the medium and low sensitivity parameters refer to parameters influencing the output voltage of the model under the large-magnification working condition or the dynamic working condition.
In one embodiment, the step S300 of identifying a second type of parameter of the electrochemical model based on the data-driven manner according to the first type of parameter and the external performance data includes:
the second type of parameters includes the stoichiometry of the positive and negative active materials at a state of charge SOC equal to 0 and the stoichiometry at a SOC equal to 100%; stoichiometric number is a highly sensitive parameter;
step S310 identifies the stoichiometric number of the positive and negative active materials when the state of charge SOC is equal to 0 and the stoichiometric number when the SOC is equal to 100% based on the data driving manner according to the first type of parameter, the curve of the relationship between the open-circuit potential and the stoichiometric number of the positive material, the curve of the relationship between the open-circuit potential and the stoichiometric number of the negative material, and the voltage curve of the sample battery at a rate lower than the first rate.
In one embodiment, the second class of parameters further includes highly sensitive parameters other than stoichiometry, and identifying such parameters includes:
step S320 identifies the high-sensitivity parameter of the second type of parameter based on the data driving manner according to the first type of parameter and the voltage curve of the sample cell under the second magnification under the constant-current working condition.
The second magnification is larger than the first magnification, and the second magnification is larger than 1/20C, such as 0.5C, 1C, etc.
In one embodiment, the second class of parameters further includes medium-low sensitivity parameters, and the identifying of such parameters includes:
step S330, according to the high-sensitivity parameters in the first type of parameters and the second type of parameters and the voltage curve of the sample battery under the dynamic working condition, identifying the medium-low sensitivity parameters of the second type of parameters based on a data driving mode.
In one embodiment, the identifying the second type of parameters of the electrochemical model based on the data-driven manner in step S300 includes: and identifying the second type of parameters of the electrochemical model by adopting a heuristic algorithm or a neural network model or Kalman filtering.
Heuristic algorithms include, but are not limited to, cuckoo algorithms, genetic algorithms, particle swarm algorithms.
In one embodiment, adjusting the adjustable parameters of the electrochemical model in step S600 includes: and automatically adjusting the adjustable parameters of the electrochemical model through a heuristic algorithm.
The automatic optimization through the heuristic algorithm has higher efficiency than the manual parameter adjustment optimization. Any one or more of the input parameters in the electrochemical model may be used as adjustable parameters. The optimization objective may be one of output voltage, battery capacity, battery energy, mass energy density, volumetric energy density, power density.
In an embodiment of the present invention, as shown in fig. 2, a lithium battery design optimization apparatus based on parameter identification includes:
a first obtaining module 100, configured to obtain a first type of parameter of an electrochemical model corresponding to a sample cell, where the first type of parameter is obtained in a manufacturing process of the sample cell;
a second obtaining module 200, configured to obtain external performance data after formation of the sample battery, where the external performance data is obtained by testing the sample battery;
an identification module 300 for identifying a second type of parameter of the electrochemical model based on a data-driven manner based on the first type of parameter and the external performance data;
a model building module 400 for building an electrochemical model based on the first type of parameters and the second type of parameters;
the model optimization module 500 is used for determining an optimization target, and setting adjustable parameters and an adjustable range of the electrochemical model; adjusting adjustable parameters of the electrochemical model, calculating a corresponding optimized target value according to the electrochemical model after the parameters are changed, and selecting the parameter which enables the optimized target value to be maximum in the adjustable range of the adjustable parameters as a target battery parameter.
Specifically, in order to optimize the battery design using the electrochemical model simulation, the input parameters of the model need to be determined first. The input parameters of the model are many and can be divided into a first type parameter and a second type parameter. The first type of parameters are parameters that are easy to measure and the second type of parameters are parameters that are not easy to measure. The second category of parameters is further classified into high-sensitivity parameters and medium-low sensitivity parameters.
A sample cell meeting the nominal capacity requirement is manufactured, and a first type parameter of an electrochemical model is obtained through measurement or calculation in the manufacturing process of the sample cell.
Then testing the sample battery, and testing the external performance data of the sample battery after formation; and identifying the second type of parameters by adopting a data driving mode according to the external performance data.
After all parameters of the model are obtained, some parameters of the model are optimized.
In one embodiment, the external performance data after sample cell formation includes: the relation curve between the open-circuit potential of the anode material and the stoichiometric number, and the relation curve between the open-circuit potential of the cathode material and the stoichiometric number, wherein the stoichiometric number is the ratio of the lithium ion concentration on the surface of the active material to the maximum lithium ion concentration of the active material; and/or the voltage curve of the sample battery under the constant current working condition is lower than that under a first multiplying power, and the first multiplying power is not more than 1/20C; and/or, the voltage curve of the sample battery under the second multiplying power under the constant current working condition, wherein the second multiplying power is larger than the first multiplying power; and/or, a voltage profile of the sample cell under dynamic conditions.
In one embodiment, the recognition module is further to:
identifying the stoichiometric number of the positive electrode active material and the stoichiometric number of the negative electrode active material when the SOC is equal to 0 and the stoichiometric number when the SOC is equal to 100% based on a data driving mode according to a first type of parameter, a relation curve between the open-circuit potential and the stoichiometric number of the positive electrode material, a relation curve between the open-circuit potential and the stoichiometric number of the negative electrode material and a voltage curve of the sample battery under the condition that the voltage curve is lower than a first multiplying factor;
identifying high-sensitivity parameters except stoichiometric numbers based on a data driving mode according to the first type of parameters and a voltage curve of the sample battery under the constant-current working condition at a second magnification;
and identifying the medium-low sensitivity parameters of the second type of parameters based on a data driving mode according to the high sensitivity parameters of the first type of parameters and the second type of parameters and the voltage curve of the sample battery under the dynamic working condition.
In one embodiment, the identification module is further configured to identify the second type of parameter of the electrochemical model using a heuristic algorithm or a neural network model or kalman filtering.
Heuristic algorithms include, but are not limited to, cuckoo's algorithm, genetic algorithm, particle swarm algorithm.
In one embodiment, the model optimization module is further configured to automatically adjust the adjustable parameters of the electrochemical model through a heuristic algorithm.
Preferably, one or more of the following parameters are selected as adjustable parameters: active material area, positive active material thickness, negative active material thickness, diaphragm thickness, positive active material volume fraction, negative active material volume fraction, diaphragm porosity.
The optimization objective may be one of output voltage, battery capacity, battery energy, mass energy density, volumetric energy density, power density.
In an embodiment of the present invention, a computer-readable storage medium has a computer program stored thereon, and when the computer program is executed by a processor, the method for optimizing a lithium battery design based on parameter identification as described in the foregoing embodiments can be implemented. That is, when part or all of the technical solutions of the embodiments of the present invention contributing to the prior art are embodied by means of a computer software product, the computer software product is stored in a computer-readable storage medium. The computer readable storage medium may be any portable computer program code entity or device. For example, the computer readable storage medium may be a U disk, a removable magnetic disk, a magnetic diskette, an optical disk, a computer memory, a read-only memory, a random access memory, etc.
The invention also provides a specific application scenario embodiment, and the method and the device for optimizing the lithium battery design based on parameter identification are applied to the optimization design of the ternary NMC battery. It should be noted that the method provided in this embodiment is also applicable to other lithium batteries.
Taking a ternary NMC battery as an example, the nominal capacity is designed to be 5Ah, and positive and negative electrode materials, a diaphragm material and an electrolyte material are fixed. The optimization design steps of the sample cell are as follows:
step S1: and acquiring a first type of parameter.
In the production of sample cells, some parameters are obtained by direct measurement, for example, the active material area a is 0.1027m 2 Positive electrode active material thickness L p 75.6 μm, negative electrode active material thickness L n 85.2 μm, diaphragm thickness L s 12 μm or the like; some parameters are calculated by a formula based on directly measured values, such as volume fraction epsilon of positive electrode active material p 0.665 volume fraction epsilon of the negative electrode active material n 0.75, diaphragm porosity ε s 0.47, etc. The parameters are collectively referred to as the first type of parameters, which are related to the geometric dimensions of the battery and are easily obtained in the battery production process, but not all geometric parameters, such as the particle sizes of the positive and negative electrode active materials, are easily obtained.
Step S2: the base curve of the cell was tested.
The base curve to be acquired is associated with a method of driving the identification parameter with the corresponding data.
The present embodiment needs to obtain the following curves:
obtaining an OCP-theta curve (a relation curve of an open circuit and a stoichiometric number of the material) of the positive and negative electrode materials through testing the sample battery, as shown in FIGS. 3 and 4; and may also be looked up from literature provided by the material supplier.
The voltage-time curve at 1/30C rate of the test sample cell, as shown in fig. 5, was used to identify the stoichiometry of the positive and negative active materials at a state of charge SOC equal to 0 and the stoichiometry at a SOC equal to 100%.
The sample cell was tested for voltage-time curves at 1C rate for identifying high sensitivity parameters other than the aforementioned stoichiometry.
The voltage-time curves of the test sample cells at the pulsed test current (or dynamic regime) are shown in fig. 6 and 7 to identify medium and low sensitivity parameters.
Step S3: and identifying the second type of parameters in a data driving mode.
The parameters except the first kind of parameters are collectively called as second kind of parameters, the second kind of parameters are parameters which are not easy to obtain through experimental measurement, and the second kind of parameters comprise the stoichiometric number of the positive and negative electrode materials, other high-sensitivity parameters except the stoichiometric number, and medium-low sensitivity parameters.
And identifying the second type of parameters in a data driving mode based on the obtained first type of parameters, working condition data which can be used for identifying the parameters and the like.
Since there is a certain error in the geometric parameters measured by the experimental equipment, in order to eliminate the measurement error as much as possible, the parameter values obtained in step S1 still need to be further identified, so a range can be set on the basis of the measured parameter values, such as: [0.95 θ,1.05 θ ], θ represents the parameter value measured in step S1. The remaining parameter values, which are not directly measured, may be literature-based parameter ranges.
The embodiment performs parameter identification through a Cuckoo Search Algorithm (CSA):
setting the 0.5C working condition as a training set of identification parameters, setting the 1C working condition data as a verification set, and taking the loss function as the actual working condition voltage V cell And the model output voltage V sim Mean square voltage difference MSE of (c):
at 0.5C and 1C, if MSE<5mv 2 Then, the identification of the high sensitivity parameter is completed.
After the high-sensitivity parameters are identified,and fixing the high-sensitivity parameter value, and identifying the medium-low sensitivity parameter under the dynamic working condition. Wherein the training condition of the identification parameter is pulse dynamic condition 1 (as shown in fig. 6 and 7), the verification condition is dynamic condition 2 (as shown in fig. 8 and 9), and if MSE is<10mv 2 The identification of the medium and low sensitivity parameters is completed.
Step S4: and obtaining an optimal adjusting scheme based on the electrochemical model adjusting parameters.
All electrochemical model parameters of the sample cell can be obtained through step S3. Setting adjustable parameters and the range thereof, determining an optimization target and an objective function, and searching for an optimal solution within the range of the adjustable parameters.
For example, the cell output voltage is used as an optimization target.
Since the battery material is not changed and the nominal capacity is fixed, the cell output voltage curve after the parameters are changed can be simulated by adjusting the parameters such as the effective coating area of the electrode (i.e. the aforementioned active material area a), the thickness, the porosity, the volume fraction and the like.
Now assume that only the thickness and volume fraction of the positive electrode coating are changed:
raw sample cell: positive electrode active material thickness L p 75.6 μm, volume fraction ε of positive electrode active material p 0.665; the thickness of the positive active material is increased as follows:
the volume fraction of the positive electrode active material is reduced as follows:
the output voltage of the original sample battery and the battery with modified parameters can be simulated through an electrochemical model. Within the range of meeting the actual conditions and the physical significance, the optimal scheme can be obtained by continuously modifying the parameters.
Battery capacity may also be an optimization goal. The battery capacity Qcell can be calculated by the following formula:
AL P ε P FC max，p (θ 0 ，p-θ 100，p )＝Q p ＝Q cell ＝Q n ＝AL n ε n FC max，n (θ 100，n -θ 0，n )；
wherein, theta i,p The stoichiometric number of the positive electrode material at which SOC is i%, θ i,n The SOC is i% and F is a faraday constant.
In practical situations, the number of adjustable parameters is large, the limiting conditions required to be met under different situations are different, and it is still a difficult task to obtain the optimal solution by manually calculating the adjustable parameters.
Such as: with the mass energy density E as the optimization target,
M＝A(ρ p L p ε p +ρ n L n ε n +ρ e (L p (1-ε p -ε p，f )+L n (1-ε n -ε n，f )+L s ε s )+ρ p，f ε p，f +ρ n，f ε n，f ，
wherein M is the battery mass, rho is the density, epsilon is the volume fraction, subscript p represents the positive electrode, n represents the negative electrode, s represents the separator, f represents the auxiliary materials such as the binder and the like.
Assuming that only the thickness and volume fraction of the positive electrode coating were varied:
the volume fraction of the positive electrode active material and the variation graph of the thickness parameter value, which are searched by a heuristic algorithm with the mass energy density under the current multiplying power of 1C as the objective function, are shown in fig. 10.
With the final optimized value as a parameter value, the mass energy density can be simulated by setting different current multiplying factors, as shown in fig. 11, it can be seen that the mass energy density of the optimized parameter value exceeds the initial value under most of the current multiplying factors. Different optimization targets and objective functions can be set, and battery design can be optimized by adjusting different parameters and parameter ranges.
In the embodiment, the initial electrochemical model parameter value of the sample battery can be obtained by measurement or calculation in the battery manufacturing process, and a smaller identification range is set for the measured parameter value, so that the parameter identification precision is improved. Through manufacturing the sample battery first, through the battery external performance data voltage electric current that easily surveys etc. other parameters of battery material are obtained to the mode based on data drive, compare the measuring mode through chemical experiment, have saved a large amount of time, have more economical. After the parameters which are difficult to measure are obtained in a data driving mode, the parameters are modified within the parameter physical significance range, the external performance of the battery is simulated through an electrochemical model, and the research and development period and the cost of the battery are greatly reduced.
It should be noted that the above embodiments can be freely combined as necessary. The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (10)
1. A lithium battery design optimization method based on parameter identification is characterized by comprising the following steps:
obtaining a first type of parameter of an electrochemical model corresponding to a sample cell, wherein the first type of parameter is obtained in the manufacturing process of the sample cell;
obtaining external performance data after the sample battery is formed, wherein the external performance data is obtained by testing the sample battery;
identifying a second type of parameter of the electrochemical model based on a data-driven manner based on the first type of parameter and the external performance data;
constructing the electrochemical model according to the first type of parameters and the second type of parameters;
determining an optimization target, and setting adjustable parameters and adjustable ranges of the electrochemical model;
and adjusting the adjustable parameters, obtaining corresponding optimized target values according to the electrochemical model after the parameters are changed, and selecting the parameters which enable the optimized target values to be maximum in the adjustable range of the adjustable parameters as target battery parameters.
2. The parameter identification-based lithium battery design optimization method of claim 1, wherein the external performance data after the sample battery formation comprises:
the relation curve between the open-circuit potential of the positive electrode material and the stoichiometric number, and the relation curve between the open-circuit potential of the negative electrode material and the stoichiometric number, wherein the stoichiometric number is the ratio of the surface lithium ion concentration of the active material to the maximum lithium ion concentration of the active material; and/or, the voltage curve of the sample cell at less than a first rate, the first rate being no greater than 1/20C; and/or the voltage curve of the sample battery under a constant current working condition at a second multiplying power, wherein the second multiplying power is greater than the first multiplying power; and/or a voltage curve of the sample cell under dynamic conditions.
3. The method of claim 2, wherein the identifying a second type of parameter of the electrochemical model based on a data-driven manner based on the first type of parameter and the external performance data comprises:
and identifying the stoichiometric number of the positive electrode active material and the stoichiometric number of the negative electrode active material when the SOC is equal to 0 and the stoichiometric number when the SOC is equal to 100% based on a data driving mode according to the first type of parameter, the relation curve between the open-circuit potential and the stoichiometric number of the positive electrode material, the relation curve between the open-circuit potential and the stoichiometric number of the negative electrode material and the voltage curve of the sample battery under the condition of being lower than a first multiplying factor.
4. The method of claim 2, wherein the identifying a second type of parameter of the electrochemical model based on a data-driven manner based on the first type of parameter and the external performance data further comprises:
and identifying high-sensitivity parameters except stoichiometric numbers based on a data driving mode according to the first kind of parameters and a voltage curve of the sample battery under the second magnification under the constant-current working condition.
5. The method of claim 4, wherein after identifying the high-sensitivity parameter other than the stoichiometric number, the method further comprises:
and identifying the medium-low sensitivity parameter of the second type of parameter based on a data driving mode according to the high sensitivity parameter of the first type of parameter and the second type of parameter and the voltage curve of the sample battery under the dynamic working condition.
6. The method of claim 1, wherein the identifying the second type of parameters of the electrochemical model based on the data-driven manner comprises:
and identifying the second type of parameters of the electrochemical model by adopting a heuristic algorithm or a neural network model or Kalman filtering.
7. The method of claim 6, wherein the parameter identification-based lithium battery design optimization method,
the heuristic algorithm comprises a cuckoo algorithm, a genetic algorithm and a particle swarm algorithm.
8. The method of claim 1, wherein adjusting the adjustable parameters of the electrochemical model comprises:
automatically adjusting the adjustable parameters of the electrochemical model through a heuristic algorithm.
9. The utility model provides a lithium cell design optimization device based on parameter identification which characterized in that includes:
the device comprises a first acquisition module, a second acquisition module and a control module, wherein the first acquisition module is used for acquiring first type parameters of an electrochemical model corresponding to a sample battery, and the first type parameters are acquired in the manufacturing process of the sample battery;
the second acquisition module is used for acquiring external performance data after the sample battery is formed, and the external performance data is obtained by testing the sample battery;
an identification module for identifying a second type of parameter of the electrochemical model based on a data-driven manner based on the first type of parameter and the external performance data;
the model establishing module is used for establishing the electrochemical model according to the first type of parameters and the second type of parameters;
the model optimization module is used for determining an optimization target, and setting adjustable parameters and adjustable ranges of the electrochemical model; and adjusting the adjustable parameters, calculating a corresponding optimized target value according to the electrochemical model after the parameters are changed, and selecting the parameter which enables the optimized target value to be maximum in the adjustable range of the adjustable parameters as the target battery parameter.
10. A computer-readable storage medium having stored thereon a computer program, characterized in that:
the computer program, when executed by a processor, implements the parameter identification-based lithium battery design optimization method of any one of claims 1 to 8.
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