[go: up one dir, main page]

CN113779794A - Method and system for SOP estimation of lithium-ion battery considering microscopic constraints - Google Patents

Method and system for SOP estimation of lithium-ion battery considering microscopic constraints Download PDF

Info

Publication number
CN113779794A
CN113779794A CN202111067889.1A CN202111067889A CN113779794A CN 113779794 A CN113779794 A CN 113779794A CN 202111067889 A CN202111067889 A CN 202111067889A CN 113779794 A CN113779794 A CN 113779794A
Authority
CN
China
Prior art keywords
lithium ion
constraint
ion battery
constraints
electrochemical
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
Application number
CN202111067889.1A
Other languages
Chinese (zh)
Other versions
CN113779794B (en
Inventor
崔纳新
李长龙
王春雨
王光峰
张承慧
王光臣
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Shandong University
Original Assignee
Shandong University
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Shandong University filed Critical Shandong University
Priority to CN202111067889.1A priority Critical patent/CN113779794B/en
Publication of CN113779794A publication Critical patent/CN113779794A/en
Application granted granted Critical
Publication of CN113779794B publication Critical patent/CN113779794B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/04—Constraint-based CAD
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/06—Power analysis or power optimisation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/08—Thermal analysis or thermal optimisation

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Computer Hardware Design (AREA)
  • Evolutionary Computation (AREA)
  • Geometry (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Secondary Cells (AREA)

Abstract

The invention belongs to the field of lithium ion battery SOP estimation, and particularly relates to a lithium ion battery SOP estimation method and system considering microcosmic constraints. The method comprises the steps of constructing an electrochemical-thermal coupling model of the lithium ion battery, and obtaining parameters of the electrochemical-thermal coupling model of the lithium ion battery; estimating the power state of the lithium ion battery based on the lithium ion battery electrochemical-thermal coupling model under the condition of considering macroscopic constraint and microscopic constraint to generate a power state characteristic diagram of the lithium ion battery; the macro-constraint comprises a current constraint, a voltage constraint, a charge state constraint and a temperature constraint, and the micro-constraint comprises a liquid phase concentration constraint, a solid phase concentration constraint and a lithium precipitation constraint.

Description

Lithium ion battery SOP estimation method and system considering microcosmic constraint
Technical Field
The invention belongs to the field of lithium ion battery SOP estimation, and particularly relates to a lithium ion battery SOP estimation method and system considering microcosmic constraints.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
Lithium ion batteries are widely used in electric vehicles due to their advantages of high energy density, high power density, long cycle life, and the like. In order to ensure safe and efficient operation of the lithium ion battery, a battery management system is indispensable. The State estimation is one of the important functions of the battery management system, and realizes real-time accurate estimation of State of Charge (SOC), State of Power (SOP), State of Health (SOH), and the like.
The SOP is the maximum available input or output power of the battery in the next few seconds to several tens of seconds, is an important decision basis for battery charge and discharge control, and determines the performances of vehicle starting, accelerating, climbing and the like and the regenerative braking energy recovery capacity. In the SOP estimation algorithm, multiple constraints of temperature, SOC, current, and terminal voltage are typically imposed to ensure that the battery operates in a safe region.
The existing SOP estimation methods are classified as follows: (1) interpolation. The method uses a set of incremental charge-discharge pulse tests to determine the peak power capability of the battery. The testing can be carried out under different temperatures and SOC, the charging and discharging power values under different states are obtained, and then an SOP characteristic diagram is manufactured. (2) And (4) modeling. A battery parameter model is established, and the estimation of the SOP is realized by limiting the state or output of the battery parameter model. On the basis of the dynamic model, the model can be more accurate and the estimated SOP of the battery is more reliable by increasing the temperature and the change of the model parameters caused by aging. (3) A data driving method. The method takes the battery as a 'black box', does not consider the reaction mechanism and the characteristics in the battery, utilizes a data analysis and machine learning method, takes the SOP to be estimated as the output quantity of a model and the influence factors as the input quantity, and realizes the estimation of the SOP of the battery by testing a large amount of data and utilizing the model to carry out learning training.
The inventors have found that the existing SOP estimation method has the following disadvantages:
(1) the temperature range is conserved. Existing studies typically estimate cell SOP over a conserved temperature range (about 15-40 c), even under fixed temperature conditions. For the regions with large latitude and longitude span, particularly at low temperature, the power performance of the battery is obviously reduced, and the safety and the durability of the battery can be ensured only by accurately estimating the SOP.
(2) Microscopic constraints are not considered. At present, macroscopic physical quantities such as current, voltage, temperature or SOC are mostly used as constraints. However, the microscopic state inside the battery can directly reflect the aging and failure state of the battery, and if the microscopic constraint cannot be considered, an aggressive SOP estimation result is caused, and the battery is damaged.
Disclosure of Invention
In order to solve the technical problems in the background art, the invention provides a lithium ion battery SOP estimation method and system considering microcosmic constraints, which are suitable for estimating the SOP of a lithium ion battery in a wide temperature range and can ensure the safe and efficient operation of the lithium ion battery.
In order to achieve the purpose, the invention adopts the following technical scheme:
the first aspect of the present invention provides a lithium ion battery SOP estimation method considering microcosmic constraints, which includes:
constructing an electrochemical-thermal coupling model of the lithium ion battery, and obtaining parameters of the electrochemical-thermal coupling model of the lithium ion battery;
estimating the power state of the lithium ion battery based on the lithium ion battery electrochemical-thermal coupling model under the condition of considering macroscopic constraint and microscopic constraint to generate a power state characteristic diagram of the lithium ion battery;
the macro-constraint comprises a current constraint, a voltage constraint, a charge state constraint and a temperature constraint, and the micro-constraint comprises a liquid phase concentration constraint, a solid phase concentration constraint and a lithium precipitation constraint.
Further, in the lithium ion battery electrochemical-thermal coupling model, the temperature counter electrode open circuit potential U is considerediAccording to Nernst equation UiCharacterized in that:
Figure BDA0003259081080000031
in the formula, i ═p, n represents the positive or negative electrode of the battery; t isrefIs a reference temperature;
Figure BDA0003259081080000032
is the electrode open circuit potential at the reference temperature; t isbIs the battery temperature.
Further, in the lithium ion battery electrochemical-thermal coupling model, the ohmic effect, the charge transfer reaction and the lithium ion diffusion are also influenced by the temperature, and the variation relationship of relevant parameters of the ohmic effect, the charge transfer reaction and the lithium ion diffusion with the temperature is characterized by an Arrhenius equation:
Figure BDA0003259081080000033
in the formula, X represents parameters with Arrhenius characteristics, including a solid phase diffusion time constant, a liquid phase diffusion time constant, an electrode reaction constant and ohmic internal resistance; kpreAnd EaPre-exponential factor and activation energy respectively; rgIs an ideal gas constant; t isbIs the battery temperature.
Further, the lithium ion battery electrochemical-thermal coupling model parameters are obtained through experiments.
Further, based on the lithium ion battery electrochemical-thermal coupling model and the bisection method, the power state of the lithium ion battery is estimated under the condition that macroscopic constraint and microscopic constraint are considered.
Further, in the process of estimating the power state of the lithium ion battery, for a discharge scene, the discharge current is a positive value, the continuous discharge current is used as the input of an electrochemical-thermal coupling model, and under the given initial temperature and the given charge state, a result within the prediction duration is obtained through simulation; and if the simulation result simultaneously meets all macroscopic constraints and microscopic constraints, updating the lower limit of the search current in the dichotomy, otherwise, updating the upper limit of the search current until the difference between the upper limit and the lower limit of the search current in the dichotomy meets the tolerance requirement, and calculating the discharge peak power.
Further, in the process of estimating the power state of the lithium ion battery, for a charging scene, the charging current is a negative value, the continuous charging current is used as the input of an electrochemical-thermal coupling model, and under the given initial temperature and the given charge state, a result within the prediction duration is obtained through simulation; and if the simulation result simultaneously meets all macroscopic constraints and microscopic constraints, updating the upper limit of the search current in the bisection method, otherwise, updating the lower limit of the search current until the difference between the upper limit and the lower limit of the search current in the bisection method meets the tolerance requirement, and calculating the peak charging power.
A second aspect of the present invention provides a lithium ion battery SOP estimation system taking into account microscopic constraints, comprising:
the model building module is used for building an electrochemical-thermal coupling model of the lithium ion battery and obtaining parameters of the electrochemical-thermal coupling model of the lithium ion battery;
the power state estimation module is used for estimating the power state of the lithium ion battery and generating a power state characteristic diagram of the lithium ion battery under the condition of considering macroscopic constraint and microscopic constraint based on the lithium ion battery electrochemical-thermal coupling model;
the macro-constraint comprises a current constraint, a voltage constraint, a charge state constraint and a temperature constraint, and the micro-constraint comprises a liquid phase concentration constraint, a solid phase concentration constraint and a lithium precipitation constraint.
A third aspect of the invention provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps in the method for estimating SOP of a lithium ion battery taking into account micro-constraints as described above.
A fourth aspect of the present invention provides an electronic device, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to implement the steps of the method for estimating SOP of a lithium ion battery considering microcosmic constraints as described above.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides a lithium ion battery SOP estimation method considering microcosmic constraints and applicable to a wide temperature range.
Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
FIG. 1(a) is a simplified electrochemical model of a lithium-ion battery;
FIG. 1(b) is a lumped thermal model of a lithium ion battery;
FIG. 2 is a discharge/charge peak power estimation flow diagram;
FIG. 3(a) is a discharge peak power considering macroscopic and microscopic constraints;
FIG. 3(b) is a discharge peak power considering only macroscopic constraints;
FIG. 3(c) is a peak power of charge taking into account macroscopic and microscopic constraints;
fig. 3(d) is a charging peak power considering only macroscopic constraints.
Detailed Description
The invention is further described with reference to the following figures and examples.
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. 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 invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
Example one
Referring to fig. 2, the present embodiment provides a lithium ion battery SOP estimation method considering microcosmic constraints, which includes:
step 1: and constructing an electrochemical-thermal coupling model of the lithium ion battery, and obtaining parameters of the electrochemical-thermal coupling model of the lithium ion battery.
The lithium ion battery electrochemical-thermal coupling model of the present embodiment describes battery dynamic characteristics by using a simplified electrochemical model and a total heat model, and the lithium ion battery electrochemical-thermal coupling model structure and the coupling relationship between the lithium ion battery electrochemical-thermal coupling model structure are shown in fig. 1(a) and fig. 1 (b).
The electrochemical model of the lithium ion battery can describe the physical and chemical processes such as solid phase diffusion, liquid phase diffusion, charge transfer reaction, ohmic effect and the like which occur in the battery, and has higher precision. The conventional electrochemical model includes a plurality of coupled partial differential equations and nonlinear algebraic equations, and the present embodiment adopts a simplified electrochemical model to describe the cell characteristics due to its complicated calculation.
Wherein:
open circuit voltage:
open circuit voltage, i.e. the potential difference between the two electrodes in the steady state of the cell:
Figure BDA0003259081080000061
in the formula of UocAn open circuit voltage for the battery; u shapepAnd UnThe open-circuit potentials of the anode and the cathode of the battery are respectively a function of the solid-phase lithium intercalation amount of the electrode;
Figure BDA0003259081080000062
and
Figure BDA0003259081080000063
saturated lithium ion concentrations of the positive and negative electrode materials, respectively;
Figure BDA0003259081080000064
and
Figure BDA0003259081080000065
the average lithium ion concentration in the positive and negative electrodes respectively has the following corresponding relation with the battery SOC:
Figure BDA0003259081080000071
wherein i ═ p, and n represents a positive electrode or a negative electrode of the battery;
Figure BDA0003259081080000072
and
Figure BDA0003259081080000073
the average lithium ion concentration in the electrode at a battery SOC of 1 and 0, respectively.
Solid phase diffusion:
the charging and discharging of the battery depends on the intercalation and deintercalation of lithium ions in the electrode material, i.e., a solid phase diffusion process. This process can cause an uneven distribution of lithium ion concentration in the electrode particles. The relationship between the electrode particle surface and the average lithium ion concentration is expressed as:
Figure BDA0003259081080000074
wherein i ═ p, and n represents a positive electrode or a negative electrode of the battery;
Figure BDA0003259081080000075
is the lithium ion concentration at the surface of the electrode particle;
Figure BDA0003259081080000076
is the difference between the lithium ion concentration on the surface of the electrode particles and the average value; i is the battery load current, and the current is set to be a positive value during discharging; tau iss,iIs the time constant of the solid phase diffusion of the electrode; cs,iIs an electrodeEquivalent polarization capacitance of solid phase diffusion.
The change in surface lithium ion concentration corresponds to a change in surface potential, thereby creating a solid phase diffusion overpotential. Solid phase diffusion overpotential eta of single electrodes,iExpressed as:
Figure BDA0003259081080000077
total overpotential eta generated by solid phase diffusion of positive and negative electrodess,totNamely:
ηs,tot＝ηs,p-ηs,n (5)
liquid phase diffusion:
the liquid phase diffusion refers to a diffusion phenomenon of lithium ions in the electrolyte. This process causes an uneven distribution of the lithium ion concentration in the thickness direction of the electrode. The change in the liquid phase lithium ion concentration at the electrode and current collector is expressed as:
Figure BDA0003259081080000081
wherein i ═ p, and n represents a positive electrode or a negative electrode of the battery; c. Ce,iThe concentration of liquid-phase lithium ions at the junction of the positive and negative electrodes and the current collector;
Figure BDA0003259081080000082
is the average lithium ion concentration in the electrolyte; tau ise,iIs the time constant of liquid phase diffusion in the two electrode areas; ce,iIs the equivalent polarization capacitance of liquid phase diffusion.
The change of the concentration of liquid phase lithium ions at the junction of the two electrodes and the current collector generates a liquid phase diffusion overpotential, which is expressed as:
Figure BDA0003259081080000083
in the formula etaeIs a liquid phase diffusion overpotential; keAre lumped conversion coefficients.
Charge transfer:
the charge transfer reaction occurs at the interface between the solid-phase electrode particles and the electrolyte, and the generated over-potential can be expressed by a newly derived Butler-Volmer equation as follows:
Figure BDA0003259081080000084
wherein i ═ p, and n represents a positive electrode or a negative electrode of the battery; etact,iTransferring an overpotential for the charge of an electrode; kiIs the lumped electrode reaction constant. RgRepresents the ideal gas constant; f denotes the faraday constant.
The total overpotential generated by charge transfer of the positive electrode and the negative electrode is as follows:
ηct,tot＝ηct,p-ηct,n (9)
ohmic overpotential:
the ohmic overpotential inside the cell is expressed as:
Figure BDA0003259081080000085
in the formula, RohmThe total ohmic internal resistance is mainly determined by the internal resistance R of the electrolyteeAnd resistance R in SEI filmseiAnd (4) forming.
In conjunction with the above analysis, cell terminal voltage is expressed as:
Vb＝Uoc+ηs,tot+ηe+ηct,tot+ηohm (11)
the heat generation power of the battery mainly comprises reversible heat and irreversible heat, and can be calculated by a Bernardi heat generation power formula as follows:
Figure BDA0003259081080000091
in the formula, QgenGenerating thermal power for the battery; qrAnd QirRespectively representing reversible thermal and irreversible thermal power; t isbIs the battery temperature;
Figure BDA0003259081080000092
and
Figure BDA0003259081080000093
respectively representing the entropy thermal coefficients of the anode and the cathode of the battery, and the values of the entropy thermal coefficients are functions of the quantity of solid-phase embedded lithium in the electrodes.
The present embodiment employs a lumped parameter thermal model to describe its thermal characteristics. According to the law of conservation of energy, the rate of change of temperature of a cell is expressed as:
Figure BDA0003259081080000094
in the formula, RthAnd CthThermal resistance and thermal capacity of the battery respectively; t is∞Is ambient temperature.
Considering the effect of temperature on the open circuit voltage, the electrode open circuit potential in equation (1) is expressed according to Nernst equation:
Figure BDA0003259081080000095
wherein i ═ p, and n represents a positive electrode or a negative electrode of the battery; t isrefIs a reference temperature;
Figure BDA0003259081080000096
is the electrode open circuit potential at the reference temperature.
In addition, the ohmic effect, charge transfer reactions and lithium ion diffusion can also be affected by temperature. The variation of the relevant parameters with temperature is expressed by the Arrhenius equation:
Figure BDA0003259081080000101
wherein X represents a parameter having Arrhenius characteristics including a solid phase diffusion time constant τs,iLiquid phase diffusion time constant τe,iElectrode counterShould constant KiAnd ohmic internal resistance Rohm；KpreAnd EaPre-exponential factor and activation energy respectively; rgIs an ideal gas constant.
Step 2: estimating the power state of the lithium ion battery based on the lithium ion battery electrochemical-thermal coupling model under the condition of considering macroscopic constraint and microscopic constraint to generate a power state characteristic diagram of the lithium ion battery;
the macro-constraint comprises a current constraint, a voltage constraint, a charge state constraint and a temperature constraint, and the micro-constraint comprises a liquid phase concentration constraint, a solid phase concentration constraint and a lithium precipitation constraint.
Generally, to ensure safe and efficient operation of the battery, in the prediction time domain (i.e., t)k∈[t0,t0+Δt]) Multiple macroscopic constraints of current, voltage, SOC, temperature, etc. need to be satisfied, expressed as:
Figure BDA0003259081080000102
in the formula (I), the compound is shown in the specification,
Figure BDA0003259081080000103
and
Figure BDA0003259081080000104
designed lower and upper terminal voltage limits, respectively, typically referred to as discharge and charge cutoff voltages; i isminAnd ImaxDesigned lower and upper current limits, respectively; SOCminAnd SOCmaxDesigned SOC lower and upper limits, respectively;
Figure BDA0003259081080000105
the designed upper battery temperature limit. Since a large amount of heat is generated when the battery is charged and discharged at the peak power to increase the temperature of the battery, it is not necessary to set a lower temperature limit.
The power performance of the battery is limited by the internal microscopic reaction, and the SOP is estimated only based on the macroscopic constraint, so that the safe and efficient management of the battery is not facilitated. Thus, the present invention also contemplates microscopic constraints related to battery aging and safety status, including liquid phase concentration constraints, solid phase concentration constraints, and lithium deposition constraints, based on the macroscopic constraints described above. The specific introduction is as follows:
liquid phase concentration constraint: liquid phase diffusion can lead to an uneven distribution of lithium ion concentration in the electrolyte inside the battery. When the concentration is too low, the de-intercalation rate of lithium ions in electrode particles can be influenced, so that the performance of the battery is suddenly reduced; if the concentration is too high, corrosion may occur to metal components such as the current collector inside the battery. Therefore, in order to protect the lithium ions in the electrolyte from over-exhaustion and over-saturation, the following constraints should be imposed on the lithium ion concentration in the electrolyte during charging and discharging:
Figure BDA0003259081080000111
wherein i ═ p, and n represents a positive electrode or a negative electrode of the battery;
Figure BDA0003259081080000112
and
Figure BDA0003259081080000113
respectively, the lower limit and the upper limit of the ratio between the electrolyte concentration and the initial concentration.
Solid phase concentration constraint: the solid phase diffusion process is driven by a lithium ion concentration gradient within the electrode active particles. At the same time, due to the elastoplastic properties of the active particles, an inhomogeneous concentration distribution will undoubtedly lead to a radially inhomogeneous volume expansion of the particles, thereby inducing internal mechanical stresses. Such stress increases the likelihood of active particle breakage, which can lead to loss of electrode material over time. To suppress this unfavorable phenomenon, the difference between the surface of the counter electrode particles and the average lithium ion concentration is constrained as follows:
Figure BDA0003259081080000114
wherein i ═ p, and n represents a positive electrode or a negative electrode of the battery;
Figure BDA0003259081080000115
the upper limit of the ratio between the concentration difference and the saturation concentration.
Lithium separation constraint: lithium deposition is a phenomenon in which lithium ions are not inserted into the negative electrode during charging of the battery, and metal lithium is deposited on the surface of the negative electrode. If the lithium separation phenomenon continuously occurs, lithium dendrite can be formed on the surface of negative electrode particles, and the negative electrode particles penetrate through a diaphragm when the lithium separation phenomenon is serious, so that an internal short circuit thermal runaway accident is caused. Therefore, the following lithium segregation constraints are considered in the present invention:
Figure BDA0003259081080000116
wherein, ULiPFor the reaction equilibrium potential for lithium precipitation, the value is generally 0.
In view of the above macroscopic and microscopic constraints, a cell SOP profile is generated based on an electrochemical-thermal coupling model and a dichotomy principle, as shown in fig. 2.
(1) For discharge scenario
Since the discharge current is positive, Ilower＝0,Iupper＝Imax；IlowerAnd IupperRespectively representing the lower limit and the upper limit of the search current in the dichotomy;
② with continuous discharge current
Figure BDA0003259081080000121
As an input of an electrochemical-thermal coupling model, under a given initial temperature and a given charge state, simulating to obtain a result within a predicted time duration delta t;
if the simulation result meets all macro-micro constraints, updating IlowerValue of (1)
Figure BDA0003259081080000122
Otherwise update IupperValue of (1)
Figure BDA0003259081080000123
Fourthly, repeating the steps II and III until the current meets the tolerance requirement, namely Iupper-Ilower≤εI(ii) a Wherein epsilonIIs the current tolerance;
calculating discharge peak power:
Figure BDA0003259081080000124
wherein, VbIs the battery terminal voltage.
(2) For charging scenarios
Since the charging current is negative, Ilower＝Imin,Iupper＝0；
② with continuous charging current
Figure BDA0003259081080000125
As an input of an electrochemical-thermal coupling model, under a given initial temperature and a given charge state, simulating to obtain a result within a predicted time duration delta t;
if the simulation result meets all macro-micro constraints, updating IupperValue of (1)
Figure BDA0003259081080000126
Otherwise update IlowerValue of (1)
Figure BDA0003259081080000127
Fourthly, repeating the steps II and III until the current meets the tolerance requirement, namely Iupper-Ilower≤εI；
Calculating the charging peak power:
Figure BDA0003259081080000128
taking a lithium cobaltate 18650 battery produced by a certain company as an example, electrochemical-thermal model parameters are obtained through experiments, and a peak power characteristic diagram of the battery when the predicted time length is 30s is given based on the proposed SOP estimation method. The associated macroscopic and microscopic constraints are shown in table 1:
TABLE 1 SOP estimation of macroscopic and microscopic constraint settings
Figure BDA0003259081080000131
Fig. 3(a) -3 (d) show the discharge/charge peak power profiles when both macroscopic and microscopic constraints are considered and only the macroscopic constraint is considered. Since the battery has good lithium ion diffusion, electrochemical reaction and conductive properties at high temperatures, it can be seen that the battery exhibits better discharge and charge capabilities as the temperature increases. At the same temperature, the discharge peak power increases with the increase of the SOC, and the charge peak power is opposite. The results, considering both macroscopic and microscopic constraints, exhibit a general declining trend, in contrast to the results considering only macroscopic constraints. This also illustrates that existing SOP estimation methods that apply only macroscopic constraints are relatively aggressive. Further, the information in the signature may be stored by the vehicle controller in the form of a multi-dimensional look-up table to guide vehicle optimal control and energy management.
Example two
The embodiment provides a lithium ion battery SOP estimation system considering microcosmic constraint, which comprises the following modules:
the model building module is used for building an electrochemical-thermal coupling model of the lithium ion battery and obtaining parameters of the electrochemical-thermal coupling model of the lithium ion battery;
the power state estimation module is used for estimating the power state of the lithium ion battery and generating a power state characteristic diagram of the lithium ion battery under the condition of considering macroscopic constraint and microscopic constraint based on the lithium ion battery electrochemical-thermal coupling model;
the macro-constraint comprises a current constraint, a voltage constraint, a charge state constraint and a temperature constraint, and the micro-constraint comprises a liquid phase concentration constraint, a solid phase concentration constraint and a lithium precipitation constraint.
It should be noted that, each module in the present embodiment corresponds to each step in the first embodiment one to one, and the specific implementation process is the same, which is not described herein again.
EXAMPLE III
The present embodiment provides a computer-readable storage medium having stored thereon a computer program which, when being executed by a processor, implements the steps in the lithium ion battery SOP estimation method taking into account the microscopic constraints as described above.
The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
Example four
The present embodiment provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the program, the steps in the lithium ion battery SOP estimation method considering the micro constraints are implemented as described above.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams 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, embedded processor, 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, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (10)

1. A lithium ion battery SOP estimation method considering microcosmic constraint is characterized by comprising the following steps:
constructing an electrochemical-thermal coupling model of the lithium ion battery, and obtaining parameters of the electrochemical-thermal coupling model of the lithium ion battery;
estimating the power state of the lithium ion battery based on the lithium ion battery electrochemical-thermal coupling model under the condition of considering macroscopic constraint and microscopic constraint to generate a power state characteristic diagram of the lithium ion battery;
the macro-constraint comprises a current constraint, a voltage constraint, a charge state constraint and a temperature constraint, and the micro-constraint comprises a liquid phase concentration constraint, a solid phase concentration constraint and a lithium precipitation constraint.
2. The lithium ion battery SOP estimation method taking into account microcosmic constraints of claim 1, characterized in that in the lithium ion battery electrochemical-thermal coupling model, the temperature is taken into account for the electrode open circuit potential UiAccording to Nernst equation UiCharacterized in that:
Figure FDA0003259081070000011
wherein i ═ p, and n represents a positive electrode or a negative electrode of the battery; t isrefIs a reference temperature;
Figure FDA0003259081070000012
is the electrode open circuit potential at the reference temperature; t isbIs the battery temperature.
3. The lithium ion battery SOP estimation method taking into account microcosmic constraints according to claim 1, characterized in that in the lithium ion battery electrochemical-thermal coupling model, ohmic effect, charge transfer reaction and lithium ion diffusion are also affected by temperature, and the variation relationship of relevant parameters of ohmic effect, charge transfer reaction and lithium ion diffusion with temperature is characterized by Arrhenius equation:
Figure FDA0003259081070000013
in the formula, X represents parameters with Arrhenius characteristics, including a solid phase diffusion time constant, a liquid phase diffusion time constant, an electrode reaction constant and ohmic internal resistance; kpreAnd EaPre-exponential factor and activation energy respectively; rgIs an ideal gas constant; t isbIs the battery temperature.
4. The lithium ion battery SOP estimation method taking into account microcosmic constraints of claim 1 wherein the lithium ion battery electrochemical-thermal coupling model parameters are obtained experimentally.
5. The lithium ion battery SOP estimation method taking into account the microscopic constraints of claim 1, wherein the power state of the lithium ion battery is estimated under consideration of the macroscopic constraints and the microscopic constraints based on a lithium ion battery electrochemical-thermal coupling model and a bisection method.
6. The lithium ion battery SOP estimation method considering microcosmic constraints as claimed in claim 1, wherein in the process of estimating the power state of the lithium ion battery, for the discharge scene, the discharge current is a positive value, the continuous discharge current is used as the input of the electrochemical-thermal coupling model, and under the given initial temperature and charge state, the simulation obtains the result within the prediction duration; and if the simulation result simultaneously meets all macroscopic constraints and microscopic constraints, updating the lower limit of the search current in the dichotomy, otherwise, updating the upper limit of the search current until the difference between the upper limit and the lower limit of the search current in the dichotomy meets the tolerance requirement, and calculating the discharge peak power.
7. The lithium ion battery SOP estimation method taking the microscopic constraints into account of claim 1, wherein in the process of estimating the power state of the lithium ion battery, for a charging scene, the charging current is a negative value, the continuous charging current is taken as an input of an electrochemical-thermal coupling model, and under the given initial temperature and the given charge state, a result within a prediction duration is obtained through simulation; and if the simulation result simultaneously meets all macroscopic constraints and microscopic constraints, updating the upper limit of the search current in the bisection method, otherwise, updating the lower limit of the search current until the difference between the upper limit and the lower limit of the search current in the bisection method meets the tolerance requirement, and calculating the peak charging power.
8. A lithium ion battery SOP estimation system that accounts for microscopic constraints, comprising:
the model building module is used for building an electrochemical-thermal coupling model of the lithium ion battery and obtaining parameters of the electrochemical-thermal coupling model of the lithium ion battery;
the power state estimation module is used for estimating the power state of the lithium ion battery and generating a power state characteristic diagram of the lithium ion battery under the condition of considering macroscopic constraint and microscopic constraint based on the lithium ion battery electrochemical-thermal coupling model;
the macro-constraint comprises a current constraint, a voltage constraint, a charge state constraint and a temperature constraint, and the micro-constraint comprises a liquid phase concentration constraint, a solid phase concentration constraint and a lithium precipitation constraint.
9. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, carries out the steps in the method for estimating the SOP of a lithium-ion battery taking into account the microscopic constraints of any of claims 1-7.
10. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the steps in the method of estimating SOP of a lithium ion battery taking into account microcosmic constraints as claimed in any one of claims 1-7.
CN202111067889.1A 2021-09-13 2021-09-13 Lithium-ion battery SOP estimation method and system taking into account microscopic constraints Active CN113779794B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202111067889.1A CN113779794B (en) 2021-09-13 2021-09-13 Lithium-ion battery SOP estimation method and system taking into account microscopic constraints

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202111067889.1A CN113779794B (en) 2021-09-13 2021-09-13 Lithium-ion battery SOP estimation method and system taking into account microscopic constraints

Publications (2)

Publication Number Publication Date
CN113779794A true CN113779794A (en) 2021-12-10
CN113779794B CN113779794B (en) 2023-12-01

Family

ID=78842971

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202111067889.1A Active CN113779794B (en) 2021-09-13 2021-09-13 Lithium-ion battery SOP estimation method and system taking into account microscopic constraints

Country Status (1)

Country Link
CN (1) CN113779794B (en)

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115064795A (en) * 2022-06-28 2022-09-16 章鱼博士智能技术(上海)有限公司 Battery power state switching method and device and electronic equipment
CN115144760A (en) * 2022-09-01 2022-10-04 中创新航科技股份有限公司 Estimation method and device for battery system SOP
CN116840685A (en) * 2023-06-05 2023-10-03 哈尔滨工业大学 A method to jointly predict SOP based on equivalent circuit model and pseudo-two-dimensional electrochemical model
CN117060553A (en) * 2023-10-13 2023-11-14 快电动力(北京)新能源科技有限公司 Battery management method, device, system and component of energy storage system
CN117637044A (en) * 2023-10-07 2024-03-01 武汉理工大学 Battery voltage prediction method and device, electronic equipment and storage medium
WO2024156139A1 (en) * 2023-01-29 2024-08-02 山东大学 Method and system for evaluating state of safety of battery

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109344429A (en) * 2018-08-24 2019-02-15 江苏大学 A modeling method to improve the temperature applicability and accuracy of the electrochemical-thermal coupled model
CN112305426A (en) * 2020-10-27 2021-02-02 上海交通大学 A Power State Estimation System for Li-ion Battery under Multiple Constraints
CN112485673A (en) * 2020-11-19 2021-03-12 哈尔滨工业大学（威海） Battery charging and discharging peak power prediction method based on dynamic multiple safety constraints

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109344429A (en) * 2018-08-24 2019-02-15 江苏大学 A modeling method to improve the temperature applicability and accuracy of the electrochemical-thermal coupled model
CN112305426A (en) * 2020-10-27 2021-02-02 上海交通大学 A Power State Estimation System for Li-ion Battery under Multiple Constraints
CN112485673A (en) * 2020-11-19 2021-03-12 哈尔滨工业大学（威海） Battery charging and discharging peak power prediction method based on dynamic multiple safety constraints

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
田华;王伟光;舒歌群;严南华;: "基于多尺度、电化学-热耦合模型的锂离子电池生热特性分析", 天津大学学报(自然科学与工程技术版), no. 07, pages 1 *

Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115064795A (en) * 2022-06-28 2022-09-16 章鱼博士智能技术(上海)有限公司 Battery power state switching method and device and electronic equipment
CN115144760A (en) * 2022-09-01 2022-10-04 中创新航科技股份有限公司 Estimation method and device for battery system SOP
WO2024156139A1 (en) * 2023-01-29 2024-08-02 山东大学 Method and system for evaluating state of safety of battery
CN116840685A (en) * 2023-06-05 2023-10-03 哈尔滨工业大学 A method to jointly predict SOP based on equivalent circuit model and pseudo-two-dimensional electrochemical model
CN116840685B (en) * 2023-06-05 2024-05-10 哈尔滨工业大学 Method for jointly predicting SOP based on equivalent circuit model and pseudo two-dimensional electrochemical model
CN117637044A (en) * 2023-10-07 2024-03-01 武汉理工大学 Battery voltage prediction method and device, electronic equipment and storage medium
CN117060553A (en) * 2023-10-13 2023-11-14 快电动力(北京)新能源科技有限公司 Battery management method, device, system and component of energy storage system
CN117060553B (en) * 2023-10-13 2024-01-02 快电动力(北京)新能源科技有限公司 Battery management method, device, system and component of energy storage system

Also Published As

Publication number Publication date
CN113779794B (en) 2023-12-01

Similar Documents

Publication Publication Date Title
CN113779794A (en) Method and system for SOP estimation of lithium-ion battery considering microscopic constraints
CN109586373B (en) Battery charging method and device
CN106450536B (en) Quick charging method for lithium ion battery
Yang et al. State of charge estimation for pulse discharge of a LiFePO4 battery by a revised Ah counting
Li et al. Study of battery modeling using mathematical and circuit oriented approaches
Kang et al. Comparison of comprehensive properties of Ni-MH (nickel-metal hydride) and Li-ion (lithium-ion) batteries in terms of energy efficiency
KR102650965B1 (en) Method of estimating battery states
KR101903225B1 (en) Apparatus for Estimating Degree-of-Aging of Secondary Battery and Method thereof
KR101985812B1 (en) Charging limit evaluation method of battery, method and apparatus for fast charging using the same
US6700383B2 (en) Method of detecting and resolving memory effect
CN112820963B (en) Low-temperature charging method for lithium ion battery
US11592490B2 (en) Method and device for estimating a voltage of a battery
CN111062137B (en) Lithium ion battery performance prediction model, construction method and application thereof
KR101282687B1 (en) Method for estimating battery SOC of vehicle
KR20190045736A (en) Method and apparatus for battery charging
CN108761341A (en) A method for obtaining electrochemical model parameters of lithium-ion batteries
Yu et al. Study of hysteresis voltage state dependence in lithium-ion battery and a novel asymmetric hysteresis modeling
Jin et al. Comparison of Li-ion battery degradation models for system design and control algorithm development
Baek et al. Capacity fade modeling of a lithium-ion battery for electric vehicles
CN111665451B (en) A Lithium-ion battery aging test method under time-varying cycle conditions
Khalik et al. Ageing-aware charging of lithium-ion batteries using an electrochemistry-based model with capacity-loss side reactions
CN116368707A (en) Intelligent battery management system and method
CN112526350B (en) Lithium ion battery peak power prediction method considering thermal effect influence
Zheng et al. Dynamic model for characteristics of Li-ion battery on electric vehicle
Zhao et al. Analysis of fading characteristics of a lithium ion battery based on an integration model

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