CN116819344B - Lithium battery nucleation overpotential prediction method, device, vehicle and medium - Google Patents
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- 229910052744 lithium Inorganic materials 0.000 title claims abstract description 155
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 title claims abstract description 154
- 230000006911 nucleation Effects 0.000 title claims abstract description 150
- 238000010899 nucleation Methods 0.000 title claims abstract description 150
- 238000000034 method Methods 0.000 title claims abstract description 63
- 238000001556 precipitation Methods 0.000 claims abstract description 60
- 238000006243 chemical reaction Methods 0.000 claims abstract description 42
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- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 description 15
- 229910001416 lithium ion Inorganic materials 0.000 description 15
- 239000007790 solid phase Substances 0.000 description 12
- 238000010586 diagram Methods 0.000 description 10
- 239000007791 liquid phase Substances 0.000 description 10
- 238000009830 intercalation Methods 0.000 description 9
- 230000002687 intercalation Effects 0.000 description 9
- 210000001787 dendrite Anatomy 0.000 description 8
- 229910052751 metal Inorganic materials 0.000 description 8
- 239000002184 metal Substances 0.000 description 8
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- QSNQXZYQEIKDPU-UHFFFAOYSA-N [Li].[Fe] Chemical compound [Li].[Fe] QSNQXZYQEIKDPU-UHFFFAOYSA-N 0.000 description 1
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Abstract
The application discloses a lithium battery nucleation overpotential prediction method, a device, a vehicle and a medium, wherein the method comprises the following steps: obtaining simulation working condition parameters of a lithium battery; and inputting the simulation working condition parameters into a prediction model to obtain nucleation overpotential of the lithium battery after lithium precipitation reaction, wherein the prediction model is obtained by correcting a basic model through an electrochemical deposition theory, and the basic model is constructed based on a Newman model. In the embodiment of the application, the prediction model is constructed according to the Newman model and the electrochemical deposition theory, and the electrochemical deposition theory is considered when the prediction model predicts the lithium precipitation nucleation overpotential of the battery, so that the accuracy of lithium battery nucleation overpotential prediction can be improved.
Description
Technical Field
The application relates to the technical field of batteries, in particular to a lithium battery nucleation overpotential prediction method, a lithium battery nucleation overpotential prediction device, a lithium battery nucleation overpotential prediction vehicle and a lithium battery nucleation overpotential prediction medium.
Background
The most common problem in lithium battery life safety is the problem of lithium precipitation, which induced dendrite growth can lead to shorting of the lithium ion battery. The growth of lithium dendrites is closely related to nucleation overpotential, which determines the morphology rule of dendrite growth, so that it is necessary to rapidly predict the lithium precipitation nucleation overpotential of a lithium battery.
At present, in a prediction mode of lithium precipitation nucleation overpotential of a lithium battery, the influence of lithium precipitation reaction on the lithium precipitation nucleation overpotential of the lithium battery is not considered, so that the prediction accuracy is lower when the nucleation overpotential of the lithium battery is predicted.
Disclosure of Invention
The application provides a lithium battery nucleation overpotential prediction method, device, vehicle and medium, which are used for solving the problem of low prediction accuracy when lithium battery nucleation overpotential is predicted.
In a first aspect, the present application provides a method for predicting nucleation overpotential of a lithium battery, including:
obtaining simulation working condition parameters of a lithium battery;
and inputting the simulation working condition parameters into a prediction model to obtain nucleation overpotential of the lithium battery after lithium precipitation reaction, wherein the prediction model is obtained by correcting a basic model through an electrochemical deposition theory, and the basic model is constructed based on the Newman model.
In the embodiment of the application, the prediction model is constructed according to the Newman model and the electrochemical deposition theory, so that the accuracy of lithium battery nucleation overpotential prediction can be improved by considering the electrochemical deposition theory when the prediction model predicts the lithium precipitation nucleation overpotential of the battery.
In an embodiment of the present application, before the step of inputting the simulation working condition parameters into a prediction model to obtain the nucleation overpotential after the lithium battery performs the lithium precipitation reaction, the method further includes:
constructing a basic model through the Newman model;
and adding a lithium-precipitation nucleation equation in the basic model to obtain the prediction model, wherein the lithium-precipitation nucleation equation is obtained based on the electrochemical deposition theory, and the lithium-precipitation nucleation equation is used for calculating the current density of the lithium battery in a lithium-precipitation reaction.
In the embodiment of the application, the lithium analysis nucleation equation is added in the basic model, so that the prediction model can more accurately predict the nucleation overpotential of the lithium battery.
In an embodiment of the present application, the lithium-analysis nucleation equation is obtained by correcting the butler Fu Erma equation by using a correction parameter, where the correction parameter includes a first parameter and the second parameter, the first parameter is determined according to an Arrhenius formula, and the second parameter is a constant.
In the embodiment of the application, the lithium nucleation equation is analyzed, so that the prediction model can more accurately predict the nucleation overpotential of the lithium battery.
In an embodiment of the present application, the second parameter is determined by:
under the condition that the nucleation radius is smaller than or equal to the critical nucleation radius, correcting the total energy change of the nucleation process system by adopting a first coefficient to obtain the second parameter;
or,
and under the condition that the nucleation radius is larger than the critical nucleation radius, correcting the total energy change of the nucleation process system by adopting a second coefficient to obtain the second parameter.
In this embodiment, according to the metal electrodeposition theory, a "critical dimension" exists when the metal is deposited, and this dimension affects the overpotential of the last nucleation, and in this embodiment, two correction methods are adopted to obtain the second parameters respectively, so that the prediction model can more accurately predict the nucleation overpotential of the lithium battery.
In an embodiment of the present application, after the obtaining the prediction model, before the inputting the simulation working condition parameters into the prediction model to obtain the nucleation overpotential after the lithium-ion reaction of the lithium battery, the method further includes:
acquiring an anode potential curve and a charge-discharge curve of the lithium battery at a first ambient temperature;
inputting the anode potential curve and the charge-discharge curve into the prediction model, so that the prediction model calibrates the value of the coefficient to be calibrated based on the anode potential curve and the charge-discharge curve, wherein the coefficient to be calibrated comprises the nucleation radius, the total energy change of the nucleation process system, the first coefficient and the second coefficient.
In the embodiment of the application, the value of the coefficient to be calibrated is calibrated through the existing anode potential curve and the charging and discharging curve of the lithium battery, so that the accuracy of calibrating the value of the coefficient to be calibrated can be improved, and the accuracy of predicting the nucleation overpotential of the lithium battery by the prediction model is improved.
In an embodiment of the present application, the simulation working condition parameters include a charging rate of the lithium battery and a second ambient temperature during charging.
In the embodiment of the application, the lithium ion battery can be rapidly predicted to form the nuclear overpotential at different multiplying powers and temperatures by inputting the charging multiplying power and the second environment temperature during charging of the lithium ion battery to the prediction model.
In a second aspect, an embodiment of the present application provides a lithium battery nucleation overpotential prediction apparatus, including:
the first acquisition module is used for acquiring simulation working condition parameters of the lithium battery;
the prediction module is used for inputting the simulation working condition parameters into a prediction model to obtain nucleation overpotential of the lithium battery after lithium precipitation reaction, the prediction model is obtained by correcting a basic model through an electrochemical deposition theory, and the basic model is constructed based on the Newman model.
In the embodiment of the application, the prediction model is constructed according to the Newman model and the electrochemical deposition theory, so that the accuracy of lithium battery nucleation overpotential prediction can be improved by considering the electrochemical deposition theory when the prediction model predicts the lithium precipitation nucleation overpotential of the battery.
In an embodiment of the present application, the apparatus further includes:
the construction module is used for constructing a basic model through the Newman model;
the second acquisition module is used for adding a lithium-precipitation nucleation equation in the basic model to obtain the prediction model, wherein the lithium-precipitation nucleation equation is obtained based on the electrochemical deposition theory, and the lithium-precipitation nucleation equation is used for calculating the current density of the lithium battery in a lithium-precipitation reaction.
In the embodiment of the application, the lithium analysis nucleation equation is added in the basic model, so that the prediction model can more accurately predict the nucleation overpotential of the lithium battery.
In an embodiment of the present application, the lithium-analysis nucleation equation is obtained by correcting the butler Fu Erma equation by using a correction parameter, where the correction parameter includes a first parameter and the second parameter, the first parameter is determined according to an Arrhenius formula, and the second parameter is a constant.
In the embodiment of the application, the lithium nucleation equation is analyzed, so that the prediction model can more accurately predict the nucleation overpotential of the lithium battery.
In an embodiment of the present application, the second parameter is determined by:
under the condition that the nucleation radius is smaller than or equal to the critical nucleation radius, correcting the total energy change of the nucleation process system by adopting a first coefficient to obtain the second parameter;
or,
and under the condition that the nucleation radius is larger than the critical nucleation radius, correcting the total energy change of the nucleation process system by adopting a second coefficient to obtain the second parameter.
In this embodiment, as known from the metal electrodeposition theory, a "critical dimension" exists when the metal is deposited, and this dimension affects the overpotential of the last nucleation, and in this embodiment, two ways are used to determine the second parameter, so that the prediction model can more accurately predict the nucleation overpotential of the lithium battery.
In an embodiment of the present application, the apparatus further includes:
the third acquisition module is used for acquiring an anode potential curve and a charge-discharge curve of the lithium battery at the first ambient temperature;
the calibration module is used for inputting the anode potential curve and the charge-discharge curve into the prediction model, so that the prediction model calibrates the value of the coefficient to be calibrated based on the anode potential curve and the charge-discharge curve, and the coefficient to be calibrated comprises the nucleation radius, the total energy change of the nucleation process system, the first coefficient and the second coefficient.
In the embodiment of the application, the value of the coefficient to be calibrated is calibrated through the existing anode potential curve and the charging and discharging curve of the lithium battery, so that the accuracy of calibrating the value of the coefficient to be calibrated can be improved, and the accuracy of predicting the nucleation overpotential of the lithium battery by the prediction model is improved.
In an embodiment of the present application, the simulation working condition parameters include a charging rate of the lithium battery and a second ambient temperature during charging.
In the embodiment of the application, the lithium ion battery can be rapidly predicted to form the nuclear overpotential at different multiplying powers and temperatures by inputting the charging multiplying power and the second environment temperature during charging of the lithium ion battery to the prediction model.
In a third aspect, embodiments of the present application provide a vehicle, including a processor, a memory, and a program or instructions stored on the memory and executable on the processor, the program or instructions, when executed by the processor, implementing the steps of the lithium battery nucleation overpotential prediction method according to the first aspect.
In a fourth aspect, embodiments of the present application provide a readable storage medium having stored thereon a program or instructions that when executed by a processor implement the steps of the lithium battery nucleation overpotential prediction method according to the first aspect.
The foregoing description is only an overview of the technical solutions of the present application, and may be implemented according to the content of the specification in order to make the technical means of the present application more clearly understood, and in order to make the above-mentioned and other objects, features and advantages of the present application more clearly understood, the following detailed description of the present application will be given.
Drawings
Features, advantages, and technical effects of exemplary embodiments of the present application will be described below with reference to the accompanying drawings.
Fig. 1 is a schematic flow chart of a lithium battery nucleation overpotential prediction method according to an embodiment of the present application;
fig. 2 is a schematic diagram of an anode potential prediction curve after lithium precipitation according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of a lithium battery nucleation overpotential prediction device according to an embodiment of the present application;
fig. 4 is a schematic hardware structure of a vehicle according to an embodiment of the present application.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, based on the embodiments herein, which would be apparent to one of ordinary skill in the art without making any inventive effort, are intended to be within the scope of the present application.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having" and any variations thereof in the description and claims of the present application and in the description of the figures above are intended to cover non-exclusive inclusions. The terms first, second and the like in the description and in the claims or in the above-described figures, are used for distinguishing between different objects and not necessarily for describing a particular sequential or chronological order.
The most common problem in lithium battery life safety is the problem of lithium precipitation, which induced dendrite growth can lead to shorting of the lithium ion battery. Lithium dendrite growth is in turn closely related to nucleation overpotential, which determines the morphology of dendrite growth, and therefore it is necessary to be able to predict nucleation potential rapidly.
The following describes in detail the lithium battery nucleation overpotential prediction method, device, vehicle and medium provided in the embodiments of the present application with reference to the accompanying drawings.
Fig. 1 is a schematic flow chart of a method for predicting nucleation overpotential of a lithium battery according to an embodiment of the present application, as shown in fig. 1, the method includes steps 101 to 102, where:
and 101, acquiring simulation working condition parameters of the lithium battery.
Exemplary simulation operating parameters include the charge rate of the lithium battery and the temperature at the time of charging.
And 102, inputting the simulation working condition parameters into a prediction model to obtain nucleation overpotential of the lithium battery after lithium precipitation reaction, wherein the prediction model is obtained by correcting a basic model through an electrochemical deposition theory, and the basic model is constructed based on a Newman model. The Newman model is also known as a Pseudo-2-Dimensional (P2D) model.
The basic chemical reactions that occur at the negative electrode during the charging phase of a lithium ion battery include: the normal lithium intercalation reaction is generated, and the lithium precipitation reaction is generated when the potential reaches below 0VAnd (3) deposition:
。
。
。
from the above formula, it can be seen that the total reaction current before the lithium precipitation reaction occursEqual to the lithium intercalation circuit->. After the lithium separation reaction, the total reaction current is changed from the lithium intercalation current>And lithium precipitation current->And the components are combined together. The B-V equation in P2D theory describes +.>Relationship with lithium intercalation overpotential. Electrochemical deposition theory was used to calculate->。
In this embodiment, the prediction model is constructed according to the newman model and the electrochemical deposition theory, so that when the prediction model predicts the lithium precipitation nucleation overpotential of the battery, the electrochemical deposition theory is considered, that is, the lithium intercalation current and the lithium precipitation current after the lithium precipitation reaction of the lithium battery are predicted, and the accuracy of the lithium battery nucleation overpotential prediction can be improved.
In some embodiments of the present application, before the step of inputting the simulation working condition parameters into a prediction model to obtain the nucleation overpotential after the lithium battery performs the lithium precipitation reaction, the method further includes:
constructing a basic model through a Newman model;
and adding a lithium-precipitation nucleation equation in the basic model to obtain the prediction model, wherein the lithium-precipitation nucleation equation is obtained based on the electrochemical deposition theory, and the lithium-precipitation nucleation equation is used for calculating the current density of the lithium battery in a lithium-precipitation reaction.
The lithium analysis nucleation equation is obtained by correcting a Butler Fu Erma (ButlerVolmer, B-V) equation by adopting correction parameters, wherein the correction parameters comprise a first parameter and a second parameter, the first parameter is determined according to an Arrhenius formula, and the second parameter is a constant.
The correction parameter may be a value obtained by multiplying the first parameter and the second parameter.
Specifically, the P2D model is constructed based on a P2D theory, and a P2D equation in the P2D theory describes the transmission of ions at a solid phase, a liquid phase and a solid-liquid interface in the charge and discharge process of the lithium ion battery, and a control equation is described below.
Diffusion portion:
solid phase diffusion is described by the philosophy, the molar flux caused by diffusion is proportional to the concentration gradient, and the rate of change of concentration at a point in space is proportional to the spatial second derivative of concentration. The formula is expressed as follows:
。
。
wherein the method comprises the steps ofFor the solid phase intercalation lithium amount, < >>Is a solid phase diffusion coefficient>Is the position of solid phase particle in radial direction, +.>Is surface current density>Is Faraday constant, +.>Is the radius of the solid phase particles.
The liquid phase diffusion process is calculated by the following formula, and the diffusion process and the electromigration process of lithium ions in the thickness direction of the battery level sheet are considered:
。
。
wherein,for the porosity of the corresponding region>Is the lithium ion concentration in the liquid phase component, +.>Is the thickness direction coordinate of the grade sheet>Is effective liquid phase diffusion coefficient->Is the ion transfer number of lithium ions in the electrolyte, < >>Is solid phase particle specific surface area->The total thickness of the pole piece is obtained.
Potential portion:
the solid phase potential and the liquid phase potential calculate the potential and the current of each position in the battery system according to ohm's law and kirchhoff's law, and the method mainly comprises the following control equations:
。
。
wherein the method comprises the steps ofAnd->Effective in solid and liquid phases respectivelyConductivity (F)>And->The potentials of the solid and liquid phases, respectively.
Interface reaction part:
the electrode surface current densities are all obtained by the equation B-V:
。
。
wherein,exchange current density for lithium intercalation reaction, +.>And->Anode and cathode reaction rate constants, respectively, +.>And->The transfer coefficients of the basic lithium removing and inserting reactions corresponding to the anode and the cathode respectively are +.>For electrode reaction overpotential->Is a gas constant->For temperature, < >>For reference temperature->Is the activation energy of the lithium intercalation reaction. />Is a solid phase potential->Is of liquid phase potential->Is the balance potential of each cathode and anode, and belongs to the intrinsic property of materials.
And adding a lithium analysis nucleation equation in the B-V equation reaction module. The metal deposition process needs to satisfy:
1. the precipitation of metal requires a certain cathode overpotential, and can not form nuclei under the equilibrium potential (nucleation thermodynamic criterion);
2. the crystal nucleus reaching a certain critical dimension can exist stably, otherwise, the crystal nucleus can be dissolved again;
3. the critical dimension size depends on the energy of the system;
4. the energy required for growth after nucleation is less than that required for nucleation.
The lithium-precipitation reaction is also electrochemical in nature, and a current density calculation equation is given by referring to the B-V equation, namely, a lithium-precipitation nucleation equation is:
。
in the above, the first step of,is the first parameter, ++>Is a second parameter, also known as nucleation factor.
Wherein,exchange current density for lithium evolution reaction, +.>For the rate constant of the anodic lithium evolution reaction,the transfer coefficients of anode and cathode of lithium separation reaction respectively, < >>Is a gas constant->Is the temperature. />Is a solid phase potential->Is of liquid phase potential->Is the equilibrium potential of the lithium evolution reaction, +.>Is the maximum lithium intercalation amount of the particles, +.>Is the amount of lithium embedded on the surface of the particles, which is->Is the concentration of liquid-phase lithium ion, ">Is the reference temperature, +.>Is the activation energy of lithium precipitation reaction,/->Is the faraday constant.
Current density of lithium evolution reaction。
It is known from metal electrodeposition theory that metals have a "critical dimension" when deposited, which affects the overpotential of the final nucleation. This process is depicted as a piecewise function. That is, the second parameter is determined by:
under the condition that the nucleation radius is smaller than or equal to the critical nucleation radius, correcting the total energy change of the nucleation process system by adopting a first coefficient to obtain the second parameter;
or under the condition that the nucleation radius is larger than the critical nucleation radius, correcting the total energy change of the nucleation process system by adopting a second coefficient to obtain the second parameter.
The above description may be determined using the following expression:
。
wherein,。
。
。
the first coefficient and the second coefficient respectively belong to the coefficient to be calibrated, determine the shape of the anode potential curve after lithium precipitation, and are +.>Is the nucleation radius, also the coefficient to be calibrated, < ->Is shaped and passedThe total energy change of the program system is also the coefficient to be calibrated,>is the molar mass of lithium,/->Is the specific surface area->Is lithium density->Is Faraday constant, +.>For electrode reaction overpotential->Is critical nucleation radius, h is an atomic height, n is lithium valence number, ++>Is the atomic weight of lithium metal and is used for preparing the lithium battery,the interfacial tension between the crystal nucleus and the solution, the interfacial tension between the crystal nucleus and the electrode, and the interfacial tension between the solution and the electrode, respectively,/->Is time.
Dendrites need to cross a nucleation energy barrier before lithium nucleation can occur, so that the nucleation coefficient is proportional to Δg. After nucleation, dendrites complete the crossing of the energy barrier and require a reduction in energy, so that the nucleation coefficient is proportional to the lithium-out film thickness. This equation can satisfy the assumptions in the nucleation theory introduced above. I.e. the nucleation process requires a sufficiently large anodic overpotential, the anodic potential will gradually decrease during the growth process after nucleation.
In another embodiment of the present application, after the obtaining the prediction model, before the inputting the simulation working condition parameters into the prediction model to obtain the nucleation overpotential after the lithium-ion reaction of the lithium battery, the method further includes:
acquiring an anode potential curve and a charge-discharge curve of the lithium battery at a first ambient temperature;
and inputting the anode potential curve and the charge-discharge curve into the prediction model, so that the prediction model calibrates the value of the coefficient to be calibrated based on the anode potential curve and the charge-discharge curve.
Specifically, before the prediction model is used for prediction, the value of the coefficient to be calibrated in the prediction model needs to be calibrated. If the user inputs the anode potential curve and the charge-discharge curve at the first ambient temperature into the prediction model, the prediction model calibrates the value of the coefficient to be calibrated by utilizing the anode potential curve and the charge-discharge curve at the first ambient temperature and combining a machine learning theory (such as a genetic algorithm), wherein the coefficient to be calibrated comprises、/>And->. The first ambient temperature may be a temperature of an environment in which the lithium battery is charged, for example, the first ambient temperature may be-10 ℃, 0 ℃, 25 ℃, 45 ℃, 65 ℃ or the like, which is not limited in this embodiment.
If the user does not input the anode potential curve and the charge-discharge curve into the prediction model, the prediction model adopts a default value of the coefficient to be calibrated to conduct subsequent prediction.
The prediction mode provided by the embodiment of the application can be used for nuclear overpotential prediction after lithium precipitation of common lithium battery products in markets of ternary, iron lithium and the like.
The following experiments are performed on the prediction method provided by the embodiment of the application, and the obtained anode potential prediction after lithium precipitation is shown in fig. 2, and as can be seen from fig. 2, the method provided by the embodiment of the application can accurately predict the overpotential change caused by 'nucleation-growth' of metal lithium after lithium precipitation reaction occurs, and the potential after lithium ion battery lithium precipitation is consistent with the actual measurement result, and shows the trend of 'descending before ascending'.
Referring to fig. 3, a schematic structural diagram of a lithium battery nucleation overpotential prediction device according to an embodiment of the present application is shown in fig. 3, and the lithium battery nucleation overpotential prediction device 300 includes:
the embodiment of the application provides a lithium battery nucleation overpotential prediction device, which comprises:
the first obtaining module 301 is configured to obtain a simulation working condition parameter of the lithium battery;
the prediction module 302 is configured to input the simulation working condition parameters to a prediction model, so as to obtain a nucleation overpotential after the lithium battery generates a lithium precipitation reaction, the prediction model is obtained by correcting a basic model through an electrochemical deposition theory, and the basic model is constructed based on the newman model.
In an embodiment of the present application, the apparatus 300 further includes:
the construction module is used for constructing a basic model through the Newman model;
the second acquisition module is used for adding a lithium-precipitation nucleation equation in the basic model to obtain the prediction model, wherein the lithium-precipitation nucleation equation is obtained based on the electrochemical deposition theory, and the lithium-precipitation nucleation equation is used for calculating the current density of the lithium battery in a lithium-precipitation reaction.
In an embodiment of the present application, the lithium-analysis nucleation equation is obtained by correcting the butler Fu Erma equation by using a correction parameter, where the correction parameter includes a first parameter and the second parameter, the first parameter is determined according to an Arrhenius formula, and the second parameter is a constant.
In an embodiment of the present application, the second parameter is determined by:
under the condition that the nucleation radius is smaller than or equal to the critical nucleation radius, correcting the total energy change of the nucleation process system by adopting a first coefficient to obtain the second parameter;
or,
and under the condition that the nucleation radius is larger than the critical nucleation radius, correcting the total energy change of the nucleation process system by adopting a second coefficient to obtain the second parameter.
In an embodiment of the present application, the apparatus 300 further includes:
the third acquisition module is used for acquiring an anode potential curve and a charge-discharge curve of the lithium battery at the first ambient temperature;
the calibration module is used for inputting the anode potential curve and the charge-discharge curve into the prediction model, so that the prediction model calibrates the value of the coefficient to be calibrated based on the anode potential curve and the charge-discharge curve, and the coefficient to be calibrated comprises the nucleation radius, the total energy change of the nucleation process system, the first coefficient and the second coefficient.
In an embodiment of the present application, the simulation working condition parameters include a charging rate of the lithium battery and a second environmental temperature during charging, for example, the second environmental temperature may be-10 ℃, 0 ℃, 25 ℃, 45 ℃ or 65 ℃, and the second environmental temperature may be selected according to practical situations, which is not limited herein.
The lithium battery nucleation overpotential prediction device 300 provided in the embodiment of the present application can implement each process implemented by the foregoing method embodiment, and can achieve the same technical effect, so that repetition is avoided, and no further description is provided herein.
Fig. 4 shows a schematic hardware structure of a vehicle according to an embodiment of the present application.
The vehicle may include a processor 501 and a memory 502 storing computer program instructions.
In particular, the processor 501 may include a central processing unit (Central Processing Unit, CPU), or an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), or may be configured to implement one or more integrated circuits of embodiments of the present application.
Memory 502 may include mass storage for data or instructions. By way of example, and not limitation, memory 502 may comprise a Hard Disk Drive (HDD), floppy Disk Drive, flash memory, optical Disk, magneto-optical Disk, magnetic tape, or universal serial bus (Universal Serial Bus, USB) Drive, or a combination of two or more of the foregoing. In some examples, memory 402 may include removable or non-removable (or fixed) media, or memory 502 may be a non-volatile solid state memory. In some embodiments, the memory 502 may be internal or external to the battery device.
In some examples, memory 502 may be Read Only Memory (ROM). In one example, the ROM may be mask-programmed ROM, programmable ROM (PROM), erasable PROM (EPROM), electrically Erasable PROM (EEPROM), electrically rewritable ROM (EAROM), or flash memory, or a combination of two or more of these.
Memory 502 may include Read Only Memory (ROM), random Access Memory (RAM), magnetic disk storage media devices, optical storage media devices, flash memory devices, electrical, optical, or other physical/tangible memory storage devices. Thus, in general, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software comprising computer-executable instructions and when the software is executed (e.g., by one or more processors) it is operable to perform the operations described with reference to methods in accordance with aspects of the present disclosure.
The processor 501 reads and executes the computer program instructions stored in the memory 502 to implement the method in the embodiment shown in fig. 1, and achieves the corresponding technical effects achieved by executing the method/steps in the embodiment shown in fig. 1, which are not described herein for brevity.
In one example, the vehicle may also include a communication interface 503 and a bus 504. As shown in fig. 4, the processor 501, the memory 502, and the communication interface 503 are connected to each other via a bus 504 and perform communication with each other.
The communication interface 503 is mainly used to implement communication between each module, apparatus, unit and/or device in the embodiments of the present application.
Bus 504 includes hardware, software, or both, that couple the components of the online data flow billing device to each other. By way of example, and not limitation, the buses may include an accelerated graphics port (Accelerated Graphics Port, AGP) or other graphics Bus, an enhanced industry standard architecture (Extended Industry Standard Architecture, EISA) Bus, a Front Side Bus (FSB), a HyperTransport (HT) interconnect, an industry standard architecture (Industry Standard Architecture, ISA) Bus, an infiniband interconnect, a Low Pin Count (LPC) Bus, a memory Bus, a micro channel architecture (MCa) Bus, a Peripheral Component Interconnect (PCI) Bus, a PCI-Express (PCI-X) Bus, a Serial Advanced Technology Attachment (SATA) Bus, a video electronics standards association local (VLB) Bus, or other suitable Bus, or a combination of two or more of the above. Bus 504 may include one or more buses, where appropriate. Although embodiments of the present application describe and illustrate a particular bus, the present application contemplates any suitable bus or interconnect.
In addition, embodiments of the present application may be implemented by providing a computer storage medium. The computer storage medium has stored thereon computer program instructions; the computer program instructions, when executed by a processor, implement any of the lithium battery nucleation overpotential prediction methods of the above embodiments.
It should be clear that the present application is not limited to the particular arrangements and processes described above and illustrated in the drawings. For the sake of brevity, a detailed description of known methods is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method processes of the present application are not limited to the specific steps described and illustrated, and those skilled in the art can make various changes, modifications, and additions, or change the order between steps, after appreciating the spirit of the present application.
The functional blocks shown in the above block diagrams may be implemented in hardware, software, firmware, or a combination thereof. When implemented in hardware, it may be, for example, an electronic circuit, an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), suitable firmware, a plug-in, a function card, or the like. When implemented in software, the elements of the present application are the programs or code segments used to perform the required tasks. The program or code segments may be stored in a machine readable medium or transmitted over transmission media or communication links by a data signal carried in a carrier wave. A "machine-readable medium" may include any medium that can store or transfer information. Examples of machine-readable media include electronic circuitry, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio Frequency (RF) links, and the like. The code segments may be downloaded via computer networks such as the internet, intranets, etc.
It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, the present application is not limited to the order of the above-described steps, that is, the steps may be performed in the order mentioned in the embodiments, may be different from the order in the embodiments, or several steps may be performed simultaneously.
Aspects of the present disclosure are described above with reference to flowchart illustrations and/or block diagrams of methods, apparatus, devices, and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions/acts specified in the flowchart and/or block diagram block or blocks. Such a processor may be, but is not limited to being, a general purpose processor, a special purpose processor, an application specific processor, or a field programmable logic circuit. It will also be understood that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware which performs the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the embodiments, and are intended to be included within the scope of the claims and description. In particular, the technical features mentioned in the respective embodiments may be combined in any manner as long as there is no structural conflict. The present application is not limited to the specific embodiments disclosed herein, but encompasses all technical solutions falling within the scope of the claims.
Claims (7)
1. The lithium battery nucleation overpotential prediction method is characterized by comprising the following steps of:
obtaining simulation working condition parameters of a lithium battery;
inputting the simulation working condition parameters into a prediction model to obtain nucleation overpotential of the lithium battery after lithium precipitation reaction, wherein the prediction model is obtained by correcting a basic model through an electrochemical deposition theory, and the basic model is constructed based on a Newman model;
before the simulation working condition parameters are input into a prediction model to obtain the nucleation overpotential of the lithium battery after the lithium precipitation reaction, the method further comprises the following steps:
constructing a basic model through the Newman model;
adding a lithium-precipitation nucleation equation in the basic model to obtain the prediction model, wherein the lithium-precipitation nucleation equation is obtained based on the electrochemical deposition theory, and the lithium-precipitation nucleation equation is used for calculating the current density of the lithium battery in a lithium-precipitation reaction;
the lithium analysis nucleation equation is obtained by correcting the Butler Fu Erma equation by adopting correction parameters, wherein the correction parameters comprise a first parameter and a second parameter, the first parameter is determined according to an Arrhenius formula, and the second parameter is a constant.
2. The lithium battery nucleation overpotential prediction method of claim 1, wherein the second parameter is determined by:
under the condition that the nucleation radius is smaller than or equal to the critical nucleation radius, correcting the total energy change of the nucleation process system by adopting a first coefficient to obtain the second parameter;
or,
and under the condition that the nucleation radius is larger than the critical nucleation radius, correcting the total energy change of the nucleation process system by adopting a second coefficient to obtain the second parameter.
3. The method for predicting the nucleation overpotential of a lithium battery according to claim 2, wherein after obtaining the prediction model, before inputting the simulation working condition parameters into the prediction model to obtain the nucleation overpotential of the lithium battery after the lithium precipitation reaction, the method further comprises:
acquiring an anode potential curve and a charge-discharge curve of the lithium battery at a first ambient temperature;
inputting the anode potential curve and the charge-discharge curve into the prediction model, so that the prediction model calibrates the value of a coefficient to be calibrated based on the anode potential curve and the charge-discharge curve, wherein the coefficient to be calibrated comprises the nucleation radius, the total energy change of the nucleation process system, the first coefficient and the second coefficient.
4. The lithium battery nucleation overpotential prediction method of any one of claims 1 to 3, wherein the simulation operating parameters include a charge rate of the lithium battery and a second ambient temperature at the time of charging.
5. A lithium battery nucleation overpotential prediction apparatus, comprising:
the first acquisition module is used for acquiring simulation working condition parameters of the lithium battery;
the prediction module is used for inputting the simulation working condition parameters into a prediction model to obtain nucleation overpotential of the lithium battery after lithium precipitation reaction, the prediction model is obtained by correcting a basic model through an electrochemical deposition theory, and the basic model is constructed based on a Newman model;
the apparatus further comprises:
the construction module is used for constructing a basic model through the Newman model;
the second acquisition module is used for adding a lithium-precipitation nucleation equation in the basic model to obtain the prediction model, wherein the lithium-precipitation nucleation equation is obtained based on the electrochemical deposition theory, and the lithium-precipitation nucleation equation is used for calculating the current density of the lithium battery in a lithium-precipitation reaction;
the lithium analysis nucleation equation is obtained by correcting the Butler Fu Erma equation by adopting correction parameters, wherein the correction parameters comprise a first parameter and a second parameter, the first parameter is determined according to an Arrhenius formula, and the second parameter is a constant.
6. A vehicle comprising a processor, a memory and a program or instruction stored on the memory and executable on the processor, which when executed by the processor, implements the steps of the lithium battery nucleation overpotential prediction method of any one of claims 1 to 4.
7. A readable storage medium, characterized in that it stores thereon a program or instructions that, when executed by a processor, implement the steps of the lithium battery nucleation overpotential prediction method according to any one of claims 1 to 4.
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