CN115951236A - Lithium battery state monitoring method, system, device and storage medium - Google Patents
Lithium battery state monitoring method, system, device and storage medium Download PDFInfo
- Publication number
- CN115951236A CN115951236A CN202310173558.9A CN202310173558A CN115951236A CN 115951236 A CN115951236 A CN 115951236A CN 202310173558 A CN202310173558 A CN 202310173558A CN 115951236 A CN115951236 A CN 115951236A
- Authority
- CN
- China
- Prior art keywords
- lithium battery
- solid phase
- electrode
- phase surface
- lithium
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 title claims abstract description 111
- 229910052744 lithium Inorganic materials 0.000 title claims abstract description 111
- 238000000034 method Methods 0.000 title claims abstract description 61
- 238000012544 monitoring process Methods 0.000 title claims abstract description 43
- 239000007790 solid phase Substances 0.000 claims abstract description 103
- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 claims abstract description 58
- 229910001416 lithium ion Inorganic materials 0.000 claims abstract description 58
- 239000007791 liquid phase Substances 0.000 claims abstract description 19
- 230000005684 electric field Effects 0.000 claims abstract description 18
- 238000012806 monitoring device Methods 0.000 claims abstract description 9
- 230000006870 function Effects 0.000 claims description 23
- 238000004590 computer program Methods 0.000 claims description 13
- 238000009792 diffusion process Methods 0.000 claims description 11
- 238000005457 optimization Methods 0.000 claims description 11
- 239000002245 particle Substances 0.000 claims description 11
- 238000006243 chemical reaction Methods 0.000 claims description 9
- 230000004907 flux Effects 0.000 claims description 3
- 230000036541 health Effects 0.000 abstract description 5
- 238000004364 calculation method Methods 0.000 description 22
- 230000008569 process Effects 0.000 description 8
- 230000008859 change Effects 0.000 description 6
- 238000012545 processing Methods 0.000 description 5
- 230000008878 coupling Effects 0.000 description 4
- 238000010168 coupling process Methods 0.000 description 4
- 238000005859 coupling reaction Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- 230000003287 optical effect Effects 0.000 description 4
- 238000001914 filtration Methods 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 238000007726 management method Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000004088 simulation Methods 0.000 description 2
- 239000007787 solid Substances 0.000 description 2
- 238000004422 calculation algorithm Methods 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 239000000835 fiber Substances 0.000 description 1
- 230000003446 memory effect Effects 0.000 description 1
- 230000002035 prolonged effect Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
Images
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02E60/10—Energy storage using batteries
Landscapes
- Secondary Cells (AREA)
Abstract
The application provides a lithium battery state monitoring method, a lithium battery state monitoring system, a lithium battery state monitoring device and a storage medium. The lithium battery state monitoring method comprises the following steps: acquiring a solid phase surface concentration estimation model, and respectively estimating the lithium ion solid phase surface concentrations of a positive electrode and a negative electrode of a lithium battery based on the solid phase surface concentration estimation model; estimating electrode equilibrium potentials of the anode and the cathode respectively based on the lithium ion solid phase surface concentrations of the anode and the cathode; inputting electrode balance potentials of the anode and the cathode and liquid phase field parameters into an electric field equation to perform electric field decoupling so as to estimate the terminal voltage of the lithium battery; and estimating the state of charge of the lithium battery based on the terminal voltage of the lithium battery. According to the method and the device, the calculated amount is reduced, the resources needed by caching are reduced, the calculating speed is increased, and the internal health condition of the battery can be accurately estimated on line in real time.
Description
Technical Field
The application belongs to the technical field of lithium batteries, and relates to a lithium battery state monitoring method, a lithium battery state monitoring system, a lithium battery state monitoring device and a storage medium.
Background
At present, due to energy crisis and environmental problems, electric vehicles develop rapidly, especially pure electric vehicles, and batteries are one of the most critical components. The lithium ion battery has the advantages of high energy density and power density, long service life, low self-discharge rate, no memory effect and the like, and is considered as a first-choice candidate of the electric automobile at present. In order to better utilize the battery, a Battery Management System (BMS) needs to be utilized. On-line estimation of battery status is required in Battery Management Systems (BMS), for example: battery state of charge (SOC), state of health (SOH), and state of function (SOF), so that early warning of the battery condition can be performed in advance.
Electrochemical models that are commonly used at present include equivalent circuit models, empirical models, and pseudo-two-dimensional electrochemical models based on actual physical conditions. The pseudo two-dimensional electrochemical model (P2D) is obtained by coupling a plurality of partial differential equations, the precision of the P2D model is high, and various electrochemical processes in the battery can be simulated. Due to its complex coupled nonlinear Partial Differential Equations (PDEs), a rigorous physics-based P2D model requires a large amount of computation and memory. Therefore, the P2D model cannot be directly applied to online estimation and real-time control of the battery state in the real BMS.
Disclosure of Invention
The present application aims to provide a method, a system, a device, and a storage medium for monitoring a lithium battery state, which are used to solve the problems in the prior art.
In a first aspect, the present application provides a method for monitoring a state of a lithium battery, the method including: acquiring a solid phase surface concentration estimation model, and respectively estimating the lithium ion solid phase surface concentrations of a positive electrode and a negative electrode of a lithium battery based on the solid phase surface concentration estimation model; estimating electrode equilibrium potentials of the anode and the cathode respectively based on the lithium ion solid phase surface concentrations of the anode and the cathode; inputting electrode balance potentials of the anode and the cathode and liquid phase field parameters into an electric field equation to perform electric field decoupling so as to estimate the terminal voltage of the lithium battery; and estimating the state of charge of the lithium battery based on the terminal voltage of the lithium battery.
In one implementation form of the first aspect, the solid phase surface concentration estimation model includes:
Δt＝t k+1 -t k
wherein, t k 、t k+1 For estimating the time of day, Δ t is the time step, c surf Is the solid phase surface concentration of lithium ions, c mean Is the mean concentration of lithium ions, λ n 、τ n For a given coefficient, ω n Is a basis function, j n Represents the molar flux of lithium ions, R s Denotes the radius of the reaction particle, D s Represents a solid phase diffusion coefficient, c 0 Represents the initial concentration of lithium ions, and N is a positive integer greater than 1.
In one implementation manner of the first aspect, the estimating of the electrode equilibrium potentials of the positive electrode and the negative electrode based on the lithium ion solid phase surface concentrations of the positive electrode and the negative electrode respectively includes: respectively obtaining a fitting function of the solid-phase surface concentration of lithium ions and the balance potential of an electrode for the positive electrode and the negative electrode of the lithium battery; and respectively estimating the electrode equilibrium potentials of the anode and the cathode based on the fitting function and the lithium ion solid phase surface concentrations of the anode and the cathode.
In an implementation manner of the first aspect, the estimating manner of the terminal voltage of the lithium battery includes: calculating solid-phase overpotentials of the positive electrode and the negative electrode based on the electrode balance potential of the positive electrode and the negative electrode, the overpotentials of the positive electrode and the negative electrode and the liquid-phase overpotentials of the positive electrode and the negative electrode; and subtracting the solid-phase overpotential of the anode and the cathode to obtain the terminal voltage of the lithium battery.
In an implementation manner of the first aspect, the method further includes performing real-time early warning based on a state of charge of the lithium battery.
In one implementation form of the first aspect, the given coefficient λ n 、τ n Obtained by optimization, the optimization comprising:
establishing an optimization objective function:
wherein MIN represents minimization, and Δ c is a theoretical difference between the surface concentration of the lithium ion solid phase and the average concentration of the lithium ion, c' surf Is the theoretical value of the solid phase surface concentration of the lithium ions, and y (tau) is the solid phase surface concentration of the lithium ions and the lithium ion level when the solid phase surface concentration estimation model carries out estimationActual estimated differences between mean concentrations;
solving an optimized objective function to obtain lambda n 、τ n The optimum value of (c).
In a second aspect, the present application provides a lithium battery condition monitoring system, the system comprising: a solid phase surface concentration estimation module configured to obtain a solid phase surface concentration estimation model and estimate lithium ion solid phase surface concentrations of a positive electrode and a negative electrode of a lithium battery, respectively, based on the solid phase surface concentration estimation model; an electrode equilibrium potential estimation module configured to estimate electrode equilibrium potentials of the positive electrode and the negative electrode based on lithium ion solid phase surface concentrations of the positive electrode and the negative electrode, respectively; the terminal voltage estimation module is configured to input electrode balance potentials of the anode and the cathode and liquid phase field parameters into an electric field equation to perform electric field decoupling so as to estimate the terminal voltage of the lithium battery; a state parameter estimation module configured to estimate a state of charge of the lithium battery based on the terminal voltage of the lithium battery.
In one implementation of the second aspect, the system includes an early warning module configured to implement an early warning based on a state of charge of the lithium battery.
In a third aspect, the present application provides a lithium battery status monitoring device, the device comprising: a memory configured to store a computer program; and a processor configured to invoke the computer program to perform the lithium battery status monitoring method according to the first aspect of the application.
In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, the computer program being executed to implement the lithium battery state monitoring method according to the first aspect of the present application.
As described above, the lithium battery state monitoring method, system, device and storage medium provided by the present application have the following beneficial effects: the solid phase diffusion equation in the pseudo-two-dimensional model of the lithium battery is simplified by the method, the solid phase surface concentration of the lithium battery is approximately processed by the solid phase surface concentration estimation model, the condition of solving the concentration change inside reaction particles is avoided, not only is the calculation amount reduced, but also resources needed by cache are reduced, the calculation speed is improved, the result of the approximate processing is very close to the result of directly solving the partial differential equation, but the calculation amount is less, and the real-time online estimation of the internal health condition of the battery can be realized.
Drawings
Fig. 1 is a block diagram illustrating an overall flow of a lithium battery state monitoring method according to an embodiment of the present disclosure.
Fig. 2 is a block diagram illustrating a specific process of a lithium battery state monitoring method in an embodiment of the present application.
Fig. 3A is a graph showing a fitted curve of the solid-phase surface concentration of the lithium ions in the positive electrode and the equilibrium potential of the positive electrode in the example of the present application.
Fig. 3B shows a curve fitting the solid-phase surface concentration of the negative electrode lithium ions to the equilibrium potential of the negative electrode in the present example.
Fig. 4 is a schematic structural diagram of a lithium battery state monitoring system in an embodiment of the present application.
Fig. 5 is a schematic structural diagram of a lithium battery state monitoring device in an embodiment of the present application.
Description of the element reference
4. Lithium battery state monitoring system
41. Solid phase surface concentration estimation module
42. Electrode balance potential estimation module
43. Terminal voltage estimation module
44. State parameter estimation module
5. Lithium battery state monitoring device
51. Memory device
52. Processor with a memory for storing a plurality of data
S1-S4 steps
S21 to S22
S31 to S32
Detailed Description
The following description of the embodiments of the present application is provided by way of specific examples, and other advantages and effects of the present application will be readily apparent to those skilled in the art from the disclosure herein. The present application is capable of other and different embodiments and its several details are capable of modifications and/or changes in various respects, all without departing from the spirit of the present application. It should be noted that the features in the following embodiments and examples may be combined with each other without conflict.
It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present application, and the drawings only show the components related to the present application and are not drawn according to the number, shape and size of the components in actual implementation, and the type, number and proportion of the components in actual implementation may be changed freely, and the layout of the components may be more complicated.
The application provides a lithium battery state monitoring method, a lithium battery state monitoring system, a lithium battery state monitoring device and a storage medium. The method for simplifying the solid phase diffusion calculation in the pseudo-two-dimensional model is provided on the basis of the traditional pseudo-two-dimensional model, and the solid phase surface concentration of reaction particles in the battery can be estimated in real time, so that the residual charge state of the battery can be predicted more quickly and accurately, and early warning can be performed on the internal condition of the battery.
The pseudo two-dimensional model (P2D model) comprises four partial differential equations and an algebraic equation; the four partial differential equations represent solid-phase potential, a solid-phase diffusion process, a liquid-phase potential and a liquid-phase diffusion process in sequence, and the algebraic equation is a Butler-Volmer equation. The simulation solving of the P2D model can be divided into two steps, firstly, the solving of a concentration field comprises solid-phase concentration and liquid-phase concentration, and after the solving of the concentration field is completed, the solid-phase concentration and the liquid-phase concentration are input into the electric field together for electric field decoupling, so that the real-time change condition in the battery, particularly the voltage condition needing real-time monitoring, is obtained.
The solid phase diffusion process describes the concentration change condition of the interior of reaction particles in the positive and negative electrode regions, the existing solving method is to use a numerical method to solve the partial differential equation, the radius r and the time t need to be subjected to discrete processing, the discrete dimension is usually very large, and the gradual calculation from r =0 according to a discrete grid is needed to obtain the solid phase surface concentrationTo
The concentration of (b). When the solid phase process is finished, the solid phase surface concentration is only needed to calculate the electrode equilibrium potential ocv in the electric field, i.e. at
The concentration of (b). Wherein r denotes the radius of the reaction particle and varies in the range from 0 to +>
Is positively responsive to the radius of the particle>
The radius of the anode reaction particle. Therefore, the electrode equilibrium potential only depends on the solid phase surface concentration of the particles, and therefore calculation resources are greatly consumed by utilizing the solution of the solid phase diffusion equation in the pseudo two-dimensional model, so that the method for simplifying the calculation of the solid phase diffusion field is provided, the calculation process is simplified by utilizing an approximation method for calculating by a system consisting of a plurality of first-order processes, the calculation speed is increased, and the state change in the battery is reflected in time.
The technical solutions in the embodiments of the present application will be described in detail below with reference to the drawings in the embodiments of the present application.
As shown in fig. 1, the present embodiment provides a method for monitoring a state of a lithium battery, which includes the following steps S1 to S4.
In step S1, a solid phase surface concentration estimation model is obtained, and lithium ion solid phase surface concentrations of a positive electrode and a negative electrode of a lithium battery are estimated based on the solid phase surface concentration estimation model, respectively.
Specifically, the solid phase surface concentration estimation model includes:
Δt＝t k+1 -t k
wherein, t k 、t k+1 For estimating the time of day, Δ t is the time step, c surf Is the solid phase surface concentration of lithium ions, c mean Is the average concentration of lithium ions, λ n 、τ n For a given coefficient, ω n Is a basis function, j n Represents the molar flux of lithium ions, R s Denotes the radius of the reaction particle, D s Represents a solid phase diffusion coefficient, c 0 Represents the initial concentration of lithium ions, and N is a positive integer greater than 1.
In the above solid phase surface concentration estimation model, λ n 、τ n For a given coefficient, the coefficient is obtained by optimization calculation, and the optimization calculation process comprises the following steps:
establishing an optimization objective function:
wherein MIN represents minimization, and Δ c is a theoretical difference between the surface concentration of the lithium ion solid phase and the average concentration of the lithium ion, c' surf The theoretical value of the lithium ion solid phase surface concentration is used, and y (tau) is the actual estimation difference between the lithium ion solid phase surface concentration and the lithium ion average concentration when the solid phase surface concentration estimation model carries out estimation;
solving an optimized objective function to obtain lambda n 、τ n The optimum value of (c).
In the above concentration estimation model, the larger N is, the higher the accuracy of the calculation of the lithium ion solid phase surface concentration is, but the calculation resource consumption will increase, and in order to balance the calculation accuracy and the calculation speed, in some embodiments, N is 2, and further, in these embodiments, the solid phase surface concentration estimation model is simplified as follows:
Δt＝t k+1 -t k
furthermore, in the solid-phase surface concentration estimation model of the present embodiment, λ needs to be obtained by the optimization calculation using the above-mentioned optimization calculation method 1 、λ 2 、τ 1 、τ 2 。
In step S2, the electrode equilibrium potentials of the positive electrode and the negative electrode are estimated based on the lithium ion solid phase surface concentrations of the positive electrode and the negative electrode, respectively.
Referring to fig. 2, step S2 specifically includes step S21 and step S22:
in step S21, fitting functions of the lithium ion solid phase surface concentration and the electrode equilibrium potential are obtained for the positive electrode and the negative electrode of the lithium battery, respectively.
Here, a large number of experimental samples need to be obtained to obtain a fitting function of the lithium ion solid phase surface concentration and the electrode equilibrium potential.
It should be noted that: for the positive electrode and the negative electrode of the lithium battery, curve fitting needs to be performed respectively to obtain corresponding fitting functions.
In some embodiments, for better curve fitting, c is fitted surf /c max Curve fitting is carried out as independent variable of the fitting function with the electrode equilibrium potential ocv as dependent variable, where c max C is given as the maximum value of the solid-phase concentration of the lithium ions and corresponds to the positive electrode and the negative electrode respectively max The magnitude of the values, and thus the positive and negative electrodes are fitted in conjunction with the sample data. Fig. 3A is a graph showing a curve of the surface concentration of the solid phase of the positive lithium ion and the equilibrium potential of the positive electrode according to an embodiment, and fig. 3B is a graph showing a curve of the surface concentration of the solid phase of the negative lithium ion and the equilibrium potential of the negative electrode according to an embodiment. In FIGS. 3A and 3B, the horizontal axis is c surf /c max And the vertical axis is the electrode equilibrium potential.
In step S22, the electrode equilibrium potentials of the positive electrode and the negative electrode are estimated based on the fitting function and the lithium ion solid phase surface concentrations of the positive electrode and the negative electrode, respectively.
In the step, a fitting function of each calculation domain (a positive calculation domain and a negative calculation domain) of the lithium battery is directly called, and the lithium ion solid phase surface concentrations of the positive electrode and the negative electrode are substituted into the fitting function to obtain the electrode equilibrium potentials of the positive electrode and the negative electrode.
In step S3, the electrode equilibrium potentials of the positive electrode and the negative electrode and the liquid phase field parameters are input to an electric field equation to perform electric field decoupling so as to estimate the terminal voltage of the lithium battery.
With further reference to fig. 2, the estimation of the terminal voltage of the lithium battery in step S3 specifically includes step S31 and step S32.
In step S31, the solid-phase overpotential of the positive electrode and the negative electrode is calculated based on the electrode equilibrium potential of the positive electrode and the negative electrode, the overpotential of the positive electrode and the negative electrode, and the electrode liquid-phase overpotential of the positive electrode and the negative electrode.
Specifically, the solid-phase overpotential of the positive electrode and the negative electrode is calculated by the following formula:
φ s,+ ＝η + +φ e,+ +ocv +
φ s,- ＝η - +φ e,- +ocv -
wherein phi is s,+ Is the positive electrode over-potential of solid phase phi s,- Is a negative electrode solid phase overpotential eta + Is positive overpotential, η - Is a negative over-potential of phi e,+ Is the positive electrode liquid phase overpotential, phi e,- Is the overpotential of the liquid phase of the negative electrode, ocv + To the positive equilibrium potential, ocv - The negative electrode is at equilibrium potential.
Based on the above calculation formula, the positive balance potential ocv + Negative electrode equilibrium potential ocv - Estimated in step S2, positive overpotential η + Negative electrode overpotential η - Positive electrode liquid phase overpotential phi e,+ And the liquid phase overpotential of the negative electrode phi e,- The method needs to be obtained by calculating a liquid phase field and an electric field of a pseudo-two-dimensional model, and particularly, by solving a partial differential equation by a finite difference or finite element method, which belongs to a conventional simulation calculation method in the field and is not described in detail herein.
In step S32, subtracting the solid-phase overpotential of the positive electrode and the negative electrode to obtain a terminal voltage of the lithium battery, which is specifically represented as: v = phi s,+ -φ s,- 。
In step S4, the state of charge of the lithium battery is estimated based on the terminal voltage of the lithium battery.
The state of charge (SOC) of a battery measures the available state of the remaining charge in the battery, generally expressed as a percentage, and can be equivalent to the percentage of the battery capacity in a mobile phone. At present, when the remaining battery capacity SOC in a lithium battery is estimated, a kalman filtering method is most commonly used, wherein an observed value in the kalman filtering method is the voltage of a lithium battery terminal. The faster the lithium battery terminal voltage V is calculated, the more the provided data quantity is, the more beneficial the estimation of the lithium battery SOC is, and the more accurate the health state of the battery can be estimated, so that the early warning is carried out on the battery condition. Since the method for estimating the state of charge of the lithium battery by using the kalman filtering method belongs to a conventional method, the description of the embodiment is omitted.
In other embodiments, after the state of charge of the lithium battery is estimated based on the steps S1 to S4 in the above embodiments, the method further includes step S5: and carrying out real-time early warning based on the charge state of the lithium battery. For example, when the state of charge of the lithium battery is lower than a set threshold, if the SOC is less than 30%, it is suggested that the electric quantity is insufficient, and when the SOC is less than 20%, it is suggested that the electric quantity is seriously insufficient, so that charging can be performed in time, the lithium battery can be maintained better, and the service life of the lithium battery can be prolonged.
According to the method for monitoring the battery state of the lithium battery, the solid phase diffusion equation in the pseudo two-dimensional model of the lithium battery is simplified, the solid phase surface concentration of the lithium battery is approximately processed through the solid phase surface concentration estimation model, the condition of solving the concentration change inside reaction particles is avoided, the calculated amount is reduced, resources needed by cache are reduced, the calculating speed is improved, meanwhile, the result of the approximate processing is very close to the result of directly solving the partial differential equation, but the calculated amount is less, and the method can be used for estimating the internal health condition of the battery on line in real time.
The protection range of the lithium battery state monitoring method according to the embodiment of the present application is not limited to the execution sequence of the steps listed in the embodiment, and all the solutions implemented by the steps addition, subtraction, and step replacement in the prior art according to the principle of the present application are included in the protection range of the present application.
The embodiment of the present application further provides a lithium battery state monitoring system, where the lithium battery state monitoring system can implement the lithium battery state monitoring method of the present application, but the implementation apparatus of the lithium battery state monitoring method of the present application includes but is not limited to the structure of the lithium battery state monitoring system listed in this embodiment, and all structural deformation and replacement in the prior art made according to the principle of the present application are included in the protection scope of the present application.
As shown in fig. 4, this embodiment provides a lithium battery state monitoring system, where the lithium battery state monitoring system 4 includes: a solid phase surface concentration estimation module 41, an electrode equilibrium potential estimation module 42, a terminal voltage estimation module 43, and a state parameter estimation module 44.
The solid phase surface concentration estimation module 41 is configured to obtain a solid phase surface concentration estimation model, and estimate lithium ion solid phase surface concentrations of a positive electrode and a negative electrode of a lithium battery, respectively, based on the solid phase surface concentration estimation model. The electrode equilibrium potential estimation module 42 is configured to estimate the electrode equilibrium potentials of the positive electrode and the negative electrode, respectively, based on the lithium ion solid phase surface concentrations of the positive electrode and the negative electrode. The terminal voltage estimation module 43 is configured to input the electrode equilibrium potentials of the positive and negative electrodes and the liquid phase field parameters to an electric field equation for electric field decoupling to estimate the lithium battery terminal voltage. The state parameter estimation module 44 is configured to estimate the lithium battery state of charge based on the lithium battery terminal voltage.
The specific implementation processes of the method for estimating the solid-phase surface concentration by the solid-phase surface concentration estimation module 41, the method for estimating the electrode equilibrium potential by the electrode equilibrium potential estimation module 42, the method for estimating the battery terminal voltage by the terminal voltage estimation module 43, and the method for estimating the state of charge of the lithium battery by the state parameter estimation module 44 in the lithium battery state monitoring system 4 in the foregoing embodiments are specifically described in some of the foregoing embodiments, and are not described herein again.
In some other embodiments, the lithium battery state monitoring system further comprises an early warning module configured to implement an early warning based on the state of charge of the lithium battery.
As shown in fig. 5, the present embodiment provides a lithium battery state monitoring device, where the lithium battery state monitoring device 5 includes: a memory 51 configured to store a computer program; and a processor 52 configured to invoke the computer program to perform the above-described lithium battery state monitoring method.
Preferably, the memory 51 comprises: various media that can store program codes, such as ROM, RAM, magnetic disk, U-disk, memory card, or optical disk.
Preferably, the Processor 52 may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; the Integrated Circuit may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, or discrete hardware components.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus, or method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, a division of modules/units is only one type of division of logical functions, and other divisions may be realized in practice, for example, a plurality of modules or units may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or modules or units, and may be in an electrical, mechanical or other form.
Modules/units described as separate parts may or may not be physically separate, and parts displayed as modules/units may or may not be physical modules, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules/units can be selected according to actual needs to achieve the purposes of the embodiments of the present application. For example, each functional module/unit in the embodiments of the present application may be integrated into one processing module, or each module/unit may exist alone physically, or two or more modules/units may be integrated into one module/unit.
It will be further appreciated by those of ordinary skill in the art that the elements and algorithm steps of the various examples described in connection with the embodiments disclosed herein may be embodied in electronic hardware, computer software, or combinations of both, and that the components and steps of the various examples have been described in a functional generic sense in the foregoing description for the purpose of clearly illustrating the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
The embodiment of the application also provides a computer readable storage medium. It will be understood by those of ordinary skill in the art that all or part of the steps in the method for implementing the above embodiments may be implemented by a program instructing a processor, and the program may be stored in a computer-readable storage medium, which is a non-transitory (non-transitory) medium, such as a random access memory, a read only memory, a flash memory, a hard disk, a solid state drive, a magnetic tape (magnetic tape), a floppy disk (floppy disk), an optical disk (optical disk), and any combination thereof. The storage medium may be any available medium that can be accessed by a computer or a data storage device including one or more available media integrated servers, data centers, and the like. The usable medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a magnetic tape), an optical medium (e.g., a Digital Video Disk (DVD)), or a semiconductor medium (e.g., a Solid State Disk (SSD)), among others.
Embodiments of the present application may also provide a computer program product comprising one or more computer instructions. When loaded and executed on a computing device, cause the processes or functions described in accordance with embodiments of the application to occur, in whole or in part. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, the computer instructions may be transmitted from one website site, computer, or data center to another website site, computer, or data center by wire (e.g., coaxial cable, fiber optic, digital Subscriber Line (DSL)) or wirelessly (e.g., infrared, wireless, microwave, etc.).
When the computer program product is executed by a computer, the computer executes the method of the previous method embodiment. The computer program product may be a software installation package, which may be downloaded and executed on a computer in case it is desired to use the method as described above.
The description of the flow or structure corresponding to each of the above drawings has emphasis, and a part not described in detail in a certain flow or structure may refer to the related description of other flows or structures.
The above embodiments are merely illustrative of the principles and utilities of the present application and are not intended to limit the application. Any person skilled in the art can modify or change the above-described embodiments without departing from the spirit and scope of the present application. Accordingly, it is intended that all equivalent modifications or changes which may be made by those skilled in the art without departing from the spirit and technical spirit of the present disclosure be covered by the claims of the present application.
Claims (10)
1. A lithium battery state monitoring method is characterized by comprising the following steps:
acquiring a solid phase surface concentration estimation model, and respectively estimating the lithium ion solid phase surface concentrations of a positive electrode and a negative electrode of a lithium battery based on the solid phase surface concentration estimation model;
respectively estimating the electrode equilibrium potentials of the anode and the cathode based on the lithium ion solid phase surface concentrations of the anode and the cathode;
inputting electrode balance potentials of the anode and the cathode and liquid phase field parameters into an electric field equation to perform electric field decoupling so as to estimate the terminal voltage of the lithium battery;
and estimating the state of charge of the lithium battery based on the terminal voltage of the lithium battery.
2. The lithium battery state monitoring method of claim 1, wherein the solid-phase surface concentration estimation model comprises:
Δt＝t k+1 -t k
wherein, t k 、t k+1 For estimating the time of day, Δ t is the time step, c surf Is the solid phase surface concentration of lithium ions, c mean Is the average concentration of lithium ions, λ n 、τ n For a given coefficient, ω n Is a basis function, j n Represents the molar flux of lithium ions, R s Denotes the radius of the reaction particle, D s Represents a solid phase diffusion coefficient, c 0 Represents the initial concentration of lithium ions, and N is a positive integer greater than 1.
3. The lithium battery state monitoring method of claim 1, wherein the estimating the electrode equilibrium potentials of the positive electrode and the negative electrode based on the lithium ion solid phase surface concentrations of the positive electrode and the negative electrode, respectively, comprises:
respectively obtaining a fitting function of the solid-phase surface concentration of lithium ions and the balance potential of the electrode for the anode and the cathode of the lithium battery;
and respectively estimating the electrode equilibrium potentials of the anode and the cathode based on the fitting function and the lithium ion solid phase surface concentrations of the anode and the cathode.
4. The method for monitoring the state of a lithium battery as claimed in claim 1, wherein the estimating of the terminal voltage of the lithium battery comprises:
calculating solid-phase overpotentials of the positive electrode and the negative electrode based on the electrode balance potential of the positive electrode and the negative electrode, the overpotentials of the positive electrode and the negative electrode and the liquid-phase overpotentials of the positive electrode and the negative electrode;
and subtracting the solid-phase overpotential of the anode and the cathode to obtain the terminal voltage of the lithium battery.
5. The lithium battery state monitoring method of claim 1, further comprising performing real-time pre-warning based on the state of charge of the lithium battery.
6. The method for monitoring the state of a lithium battery as claimed in claim 2, characterized in that the given coefficient λ n 、τ n Obtained by optimization, the optimization comprising:
establishing an optimization objective function:
wherein MIN represents minimization, and Δ c is a theoretical difference between the surface concentration of the lithium ion solid phase and the average concentration of the lithium ion, c' surf The theoretical value of the lithium ion solid phase surface concentration is used, and y (tau) is the actual estimation difference between the lithium ion solid phase surface concentration and the lithium ion average concentration when the solid phase surface concentration estimation model carries out estimation;
solving an optimized objective function to obtain lambda n 、τ n The optimum value of (c).
7. A lithium battery condition monitoring system, the system comprising:
a solid phase surface concentration estimation module configured to obtain a solid phase surface concentration estimation model and estimate lithium ion solid phase surface concentrations of a positive electrode and a negative electrode of a lithium battery, respectively, based on the solid phase surface concentration estimation model;
an electrode balance potential estimation module configured to estimate electrode balance potentials of the positive electrode and the negative electrode based on lithium ion solid phase surface concentrations of the positive electrode and the negative electrode, respectively;
the terminal voltage estimation module is configured to input electrode balance potentials of the anode and the cathode and liquid phase field parameters into an electric field equation to perform electric field decoupling so as to estimate the terminal voltage of the lithium battery;
a state parameter estimation module configured to estimate a state of charge of the lithium battery based on the terminal voltage of the lithium battery.
8. The lithium battery state monitoring system of claim 7, comprising an early warning module configured to implement an early warning based on a state of charge of the lithium battery.
9. A lithium battery condition monitoring device, the device comprising:
a memory configured to store a computer program; and
a processor configured to invoke the computer program to perform the lithium battery condition monitoring method according to any one of claims 1 to 6.
10. A computer-readable storage medium on which a computer program is stored, characterized in that the computer program is executed to implement the lithium battery condition monitoring method according to any one of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310173558.9A CN115951236B (en) | 2023-02-27 | 2023-02-27 | Lithium battery state monitoring method, system, device and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310173558.9A CN115951236B (en) | 2023-02-27 | 2023-02-27 | Lithium battery state monitoring method, system, device and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115951236A true CN115951236A (en) | 2023-04-11 |
| CN115951236B CN115951236B (en) | 2024-03-22 |
Family
ID=87291413
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310173558.9A Active CN115951236B (en) | 2023-02-27 | 2023-02-27 | Lithium battery state monitoring method, system, device and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115951236B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116381512A (en) * | 2023-06-06 | 2023-07-04 | 宁德时代新能源科技股份有限公司 | Battery voltage calculation method, battery voltage calculation device, electronic equipment and readable storage medium |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105550452A (en) * | 2015-12-18 | 2016-05-04 | 中国科学院上海高等研究院 | Lithium ion battery P2D model parameter recognizing method based on heuristic algorithm |
| CN111239610A (en) * | 2020-03-16 | 2020-06-05 | 上海交通大学 | Power lithium battery state estimation construction system and method based on electrochemical model |
| WO2020116853A1 (en) * | 2018-12-06 | 2020-06-11 | 주식회사 엘지화학 | Device and method for charging secondary battery |
| CN114545265A (en) * | 2022-03-01 | 2022-05-27 | 上海玫克生储能科技有限公司 | Electrochemical model-based battery state of charge estimation method and device |
| CN115116555A (en) * | 2022-06-29 | 2022-09-27 | 上海玫克生储能科技有限公司 | Electric field decoupling method and device of electrochemical model based on target practice |
| CN115632181A (en) * | 2022-10-28 | 2023-01-20 | 上海玫克生储能科技有限公司 | Lithium battery temperature early warning method and system, storage medium and terminal |
-
2023
- 2023-02-27 CN CN202310173558.9A patent/CN115951236B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105550452A (en) * | 2015-12-18 | 2016-05-04 | 中国科学院上海高等研究院 | Lithium ion battery P2D model parameter recognizing method based on heuristic algorithm |
| WO2020116853A1 (en) * | 2018-12-06 | 2020-06-11 | 주식회사 엘지화학 | Device and method for charging secondary battery |
| CN111239610A (en) * | 2020-03-16 | 2020-06-05 | 上海交通大学 | Power lithium battery state estimation construction system and method based on electrochemical model |
| CN114545265A (en) * | 2022-03-01 | 2022-05-27 | 上海玫克生储能科技有限公司 | Electrochemical model-based battery state of charge estimation method and device |
| CN115116555A (en) * | 2022-06-29 | 2022-09-27 | 上海玫克生储能科技有限公司 | Electric field decoupling method and device of electrochemical model based on target practice |
| CN115632181A (en) * | 2022-10-28 | 2023-01-20 | 上海玫克生储能科技有限公司 | Lithium battery temperature early warning method and system, storage medium and terminal |
Non-Patent Citations (2)
| Title |
|---|
| XINHUA GAO: "A Simplified Electrochemical Model of Lithium-ion Battery Considering Liquid Overpotential", 《2021 IEEE INTERNATIONAL CONFERENCE ON SENSING, DIAGNOSTICS, PROGNOSTICS, AND CONTROL (SDPC)》, pages 275 - 281 * |
| 李光远: "面向 SOC 估计的锂离子电池电化学-热耦合建模", 《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》, pages 035 - 1115 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116381512A (en) * | 2023-06-06 | 2023-07-04 | 宁德时代新能源科技股份有限公司 | Battery voltage calculation method, battery voltage calculation device, electronic equipment and readable storage medium |
| CN116381512B (en) * | 2023-06-06 | 2023-10-27 | 宁德时代新能源科技股份有限公司 | Battery voltage calculation method, battery voltage calculation device, electronic equipment and readable storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115951236B (en) | 2024-03-22 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| KR102219397B1 (en) | Secondary battery management system with remote parameter estimation | |
| CN108805217B (en) | Lithium ion battery health state estimation method and system based on support vector machine | |
| Li et al. | Battery degradation diagnosis with field data, impedance-based modeling and artificial intelligence | |
| JP6449303B2 (en) | Method, apparatus and system for estimating the state of charge of a battery | |
| CN108445402A (en) | A kind of lithium-ion-power cell state-of-charge method of estimation and system | |
| CN107066722A (en) | A kind of combined estimation method of electrokinetic cell system state-of-charge and health status based on electrochemical model | |
| Li et al. | On-line estimation method of lithium-ion battery health status based on PSO-SVM | |
| CN113671381B (en) | Lithium ion power battery estimation method based on time convolution network | |
| US10471847B1 (en) | Multi-particle reduced order Li-ion battery controller | |
| CN113189495B (en) | Battery health state prediction method and device and electronic equipment | |
| JP2023139227A (en) | Battery health state prediction method, device, and electronic instrument and readable storage medium | |
| CN114545262A (en) | Lithium ion battery parameter identification and SOC estimation method aiming at loss data | |
| WO2016151336A1 (en) | Battery model comprising plurality of equivalent circuit networks and interface element coupling them | |
| CN115221795A (en) | Training method, prediction method, device, equipment and medium of capacity prediction model | |
| CN112881921A (en) | Battery equivalent circuit model parameter identification method, device, equipment and storage medium | |
| CN115951236A (en) | Lithium battery state monitoring method, system, device and storage medium | |
| CN117110891A (en) | Calculation method and calculation device for lithium ion battery state of charge estimation value | |
| CN112946481A (en) | Based on federation H∞Filtering sliding-mode observer lithium ion battery SOC estimation method and battery management system | |
| Chen et al. | State of health estimation of lithium-ion batteries based on equivalent circuit model and data-driven method | |
| CN105911480A (en) | Power battery SOC estimation method | |
| CN114624600A (en) | Power battery cell capacity difference calculation method and computer readable storage medium | |
| CN114879070A (en) | Battery state evaluation method and related equipment | |
| CN116068408B (en) | Battery aging data determining method, device, computer equipment and storage medium | |
| CN112946480A (en) | Lithium battery circuit model simplification method for improving SOC estimation real-time performance | |
| CN117031292A (en) | Battery health state prediction method, device, computer equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |