Lithium ion battery multi-field coupling modeling method

Abstract

The invention discloses a multi-field coupling modeling method for a lithium ion battery. The method comprises the following steps: initially setting parameters and given working conditions of the lithium ion battery; constructing an electrical model, a thermal model and an aging model to realize bottom-layer electrical simulation, thermal simulation and aging simulation; completing parameter coupling and transmission to obtain a new parameter and taking the new parameter as an input variable; and continuously iterating to obtain real-time battery state parameters and a relationship between the real-time battery state parameters Compared with a traditional lithium ion battery electric-thermal coupling model, the method has the advantages that the research on the aging which is a long-time scale variable is added, and the working state of the whole life cycle of the lithium ion battery can be described. The behavior and the state of the lithium ion battery are described more accurately and comprehensively by establishing a simulation model of'multi-time scale 'and'multi-physical field coupling'.

Description

Technical Field

[0001] The present invention belongs to the field of lithium-ion batteries, and in particular relates to a multi-field coupling modeling method for lithium-ion batteries. Background Art

[0002] As a key component, power battery systems are increasingly used in electric vehicles and power storage. During the application process, a battery management system (BMS) is needed to estimate and monitor the parameters and status of the battery, and further used for battery power estimation or life state estimation. However, traditional experimental methods can only monitor the apparent characteristics of the battery, such as voltage, temperature, capacity, etc., but it is difficult to obtain the electrochemical, mechanical and thermal characteristics of the battery from the microscopic to the macroscopic level, such as the lithium ion concentration, current and potential distribution, temperature distribution of each layer inside the battery, and even the stress and strain of the internal particles. Although researchers have begun to explore and use precise in-situ experimental techniques in recent years, such as in-situ neutron diffraction, X-ray diffraction, nuclear magnetic resonance and other technologies, they can detect tiny phase parameters and structural changes inside the battery, but due to the limitation of the experimental site and the high price, it is difficult to achieve engineering application in a short time. In view of this, numerical simulation has shown its unique advantages. Only a small number of experiments are needed to verify the effectiveness of the model and then it can be put into engineering application to observe the internal electrical and thermal characteristics of the battery. This not only saves manpower, material and financial resources, but also effectively shortens the battery development and performance testing cycle, and helps to clarify the relationship and coupling mechanism between the internal electrical and thermal characteristics of the battery. Summary of the invention

[0003] The purpose of the present invention is to overcome the above problems existing in the prior art and provide a multi-field coupling modeling method for lithium-ion batteries.

[0004] In order to achieve the above technical objectives and the above technical effects, the present invention is implemented through the following technical solutions:

[0005] A multi-field coupling modeling method for lithium-ion batteries, comprising:

[0006] Initialize and set lithium-ion battery parameters and given operating conditions;

[0007] Build electrical models, thermal models, and aging models to achieve low-level electrical simulation, thermal simulation, and aging simulation;

[0008] Complete parameter coupling and transfer to obtain new parameters as input variables;

[0009] Real-time battery status parameters and the relationship between them are obtained through continuous iteration.

[0010] Furthermore, the electrical model adopts a second-order RC equivalent circuit model to describe the nonlinear characteristics of the lithium battery and the polarization effect during the charging and discharging process.

[0011] Furthermore, according to the characteristic parameters of the second-order RC equivalent circuit model, a simulation model based on Matlab / simulink is constructed.

[0012] Furthermore, the simulation model includes:

[0013] The current signal module is used to generate a current signal that simulates the actual current, using a constant current signal and a pulse signal;

[0014] The remaining power calculation module obtains the SOC value through the ampere-hour method;

[0015] RC parameter calculation module, which uses the lookup module in the toolbox to find the RC parameter value corresponding to each SOC;

[0016] The voltage calculation module obtains the relationship between the battery terminal voltage and the RC network voltage through the KCL law and the KVL law.

[0017] Furthermore, the thermal model includes a heat generation model and a heat dissipation model.

[0018] Furthermore, the heat generation model calculation formula is as follows:

[0019]

[0020] Furthermore, the heat dissipation model calculation formula is as follows:

[0021]

[0022] And the heat dissipation balance equation is obtained as:

[0023]

[0024] Furthermore, the construction of the aging model includes analyzing the aging mechanism of the lithium-ion battery, and establishing a semi-empirical model of lithium-ion battery capacity decay and a semi-empirical model of internal resistance growth.

[0025] Furthermore, the method for establishing the capacity decay semi-empirical model includes:

[0026] Fit the effect of temperature on battery capacity into an exponential function and place the temperature on the exponent;

[0027] Fit the effect of cycle number on battery capacity decay rate to form a power function;

[0028] A semi-empirical model coupling temperature and cycle number is established.

[0029] Furthermore, the semi-empirical model of internal resistance growth is a model that couples temperature and cycle number, and the formula is as follows:

[0030]

[0031] The beneficial effects of the present invention are:

[0032] Compared with the traditional lithium-ion battery electric-thermal coupling model, the present invention adds the study of aging, a long time scale variable, which is conducive to describing the working state of the lithium-ion battery throughout its life cycle. By establishing a "multi-time scale" and "multi-physical field coupling" simulation model, the behavior and state of lithium-ion batteries can be described more accurately and comprehensively. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0034] Figure 1 is a flow chart of the method of the present invention;

[0035] Figure 2 It is a schematic diagram of the second-order RC equivalent circuit model in the present invention;

[0036] Figure 3 It is a schematic diagram of model simulation based on Matlab / simulink in the present invention;

[0037] Figure 4 It is a schematic diagram of a simulation model of a remaining power calculation module in the present invention;

[0038] Figure 5 It is a schematic diagram of the simulation model of the RC parameter calculation module in the present invention;

[0039] Figure 6 It is a schematic diagram of a simulation model of a voltage calculation module in the present invention;

[0040] Figure 7 It is a schematic diagram of heat generation inside the lithium-ion battery of the present invention;

[0041] Figure 8 is a schematic diagram of a lumped parameter thermal model in the present invention;

[0042] Fig. 9 is a schematic diagram of a second-order RC circuit considering temperature factors in the present invention;

[0043] Fig.10 Schematic diagram of the relationship between 0°C OCV and time in the present invention;

[0044] Fig.11This is a schematic diagram of the relationship curve of 25°C OCV changing with time in the present invention;

[0045] Fig.12 This is a schematic diagram of the 45°C OCV variation curve over time in the present invention;

[0046] Fig.13 It is a schematic diagram of the 0°C SOC-OCV curve of the present invention;

[0047] Fig.14 It is a schematic diagram of the 25°C SOC-OCV curve of the present invention;

[0048] Fig.15 It is a schematic diagram of the 45°C SOC-OCV curve of the present invention;

[0049] Fig.16 It is a schematic diagram of the battery capacity decay test results in the present invention;

[0050] Fig.17 It is a schematic diagram of the battery internal resistance growth test results in the present invention. DETAILED DESCRIPTION

[0051] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0052] like Figure 1 A multi-field coupling modeling method for lithium-ion batteries is shown, comprising:

[0053] Initialize and set lithium-ion battery parameters and given operating conditions;

[0054] Build electrical models, thermal models, and aging models to achieve low-level electrical simulation, thermal simulation, and aging simulation;

[0055] Complete parameter coupling and transfer to obtain new parameters as input variables;

[0056] Real-time battery status parameters and the relationship between them are obtained through continuous iteration.

[0057] Under given working conditions, the actual charging and discharging process of the battery is a unity of multiple processes coupled and interacted. Therefore, each process cannot be separated and analyzed separately, and needs to be considered together to ensure the authenticity of the results. Through the above process, the changes in the actual output current, voltage, and battery capacity of the lithium-ion battery under various cycle times are recorded to determine the optimal time scale for lithium-ion battery coupling modeling.

[0058] The electrical model uses a second-order RC equivalent circuit model to describe the nonlinear characteristics of lithium batteries and the polarization effect during the charging and discharging process.

[0059] The second-order RC equivalent circuit model is based on the first-order RC equivalent circuit model and has an RC parallel circuit in series. While describing the nonlinear characteristics of lithium batteries, it also more accurately describes the polarization effect during the charging and discharging process of lithium batteries. The model is less complex and easy to implement in engineering. Figure 2 shown.

[0060] The principle is as follows:

[0061] V o =U OC -U1-U2-IR0

[0062]

[0063]

[0064] Integrated:

[0065]

[0066] τ1=R1C1

[0067] τ2=R2C2

[0068] Where: U0 is the battery terminal voltage; U OC is the open circuit voltage; U1 is the voltage of the first RC parallel circuit; U2 is the voltage of the second RC parallel circuit; I is the total current flowing through the battery; R0 is the ohmic internal resistance; R1, R2 are polarization resistances; C1, C2 are polarization capacitances; τ1, τ2 are the time constants of the two RC parallel circuits.

[0069] Body process:

[0070] According to the above characteristic parameters, we constructed a model simulation based on Matlab / simulink, such as Figure 3 shown.

[0071] The battery simulation model mainly consists of four parts:

[0072] (1) The current signal module is mainly used to generate current signals that simulate actual current. This paper mainly uses constant current signals and pulse signals for verification.

[0073] (2) The remaining power calculation module mainly obtains the SOC value through the ampere-hour method. The simulation model is as follows Figure 4 As shown;

[0074] (3) The RC parameter calculation module mainly uses the lookup module in the toolbox to find the RC parameter value corresponding to each SOC. The simulation model is as follows Figure 5 As shown;

[0075] (4) The voltage calculation module mainly obtains the relationship between the battery terminal voltage and the RC network voltage through the KCL law and the KVL law. The simulation model is as follows Figure 6 shown.

[0076] Thermal models include heat generation models and heat dissipation models: In the actual use of lithium-ion batteries, electrical characteristics and thermal characteristics are inseparable. Based on this, in order to improve the accuracy of the model, this project will analyze the thermal balance mechanism of lithium-ion batteries and establish heat generation models and heat dissipation models accordingly.

[0077] like Figure 7 The figure shows a schematic diagram of heat generation inside a lithium-ion battery. During operation, complex chemical reactions will occur inside the battery, generating a large amount of heat. In addition, the ohmic internal resistance and polarization internal resistance of the battery will also generate a large amount of ohmic heat and polarization heat. According to the source of heat, it can be divided into reversible heat and irreversible heat. Reversible heat refers to the enthalpy change of reversible chemical reactions of positive and negative electrode materials; irreversible heat includes ohmic heat caused by the internal resistance of the battery and heat generated by side reactions such as SEI film decomposition, electrolyte decomposition, and decomposition of the positive electrode (side reaction heat is much smaller than polarization heat and ohmic heat, so its influence is ignored in this modeling). In summary, the charging and discharging process of lithium-ion batteries mainly includes reaction heat Q r ,Ohm heat Q j and polarization heat Q p .

[0078] The current method for calculating battery heat generation is usually the following battery heat generation power model:

[0079]

[0080] In the formula, Q total is the total heat generation power of the lithium-ion battery; I is the charge and discharge current; R is the ohmic internal resistance of the battery; R p is the equivalent polarization resistance; T is the average temperature of the battery; is the entropy thermal coefficient of the battery open circuit voltage changing with temperature.

[0081] The heat dissipation process of lithium-ion batteries can be divided into concentrated mass models and multi-dimensional heat dissipation models according to the dimensions. The multi-dimensional thermal model can be divided into one-dimensional models, two-dimensional models and three-dimensional models according to the selected direction dimensions. The multi-dimensional parameter thermal model considers the temperature distribution in different directions inside the battery cell, but its calculation amount is large and relatively complex. In actual modeling, the lumped parameter thermal model is more commonly used. It has a small amount of calculation, the state quantity is easy to obtain, and it can approximately describe the temperature difference between the battery cells of the battery pack. Taking all the above into consideration, the heat dissipation model will adopt a lumped parameter thermal model, such as Figure 8 shown.

[0082] The heat dissipation of batteries mainly includes three ways: convection, conduction and radiation. Among them, radiation is usually ignored. Conduction refers to the heat conduction inside the battery, and convection refers to the convection heat exchange between the battery surface and the air. The lumped parameter heat dissipation model regards the battery as a whole, assuming that the battery temperature is evenly distributed, and only considers the surface temperature, that is, convection, for calculation.

[0083] Thermal convection refers to the heat on the battery surface exchanging heat through the flow of the ambient medium, i.e. the fluid, which is proportional to the temperature difference and can be expressed using Newton's formula as:

[0084]

[0085] In the formula, Q c is the heat dissipation power of the battery; S is the surface area of ​​the battery; h is the convection heat transfer coefficient, R th is the battery surface convection thermal resistance, T a is the ambient temperature; T is the average temperature of the battery surface.

[0086] In summary, according to the heat transfer equilibrium formula:

[0087] Q total =Q c +Q bat

[0088] In the formula, Q bat Absorbs heat for the battery per unit time

[0089] Finally, the heat dissipation and heat generation balance equation is obtained:

[0090]

[0091] Specific process:

[0092] The electrical-thermal coupling model established using MATLAB / Simulink is as follows: Fig. 9 shown.

[0093] Since the incremental-current OCV test has higher stability than the low-current OCV test, our team selected the experimental data obtained from the incremental OCV test and processed and analyzed it. (The battery model selected is INR 18650-20R Battery)

[0094] Through processing, we obtained the curve of the change of the open circuit voltage of the battery over time under the condition of constant current discharge at different temperatures for this model of battery, as shown in Figure 10-12 shown.

[0095] At the same time, the open circuit voltage values ​​corresponding to each state of charge are also obtained through the above figure. Through quintic interpolation fitting, the SOC-OCV curve of this model battery at different temperatures is obtained. The curve is as follows: Figure 13-15 As shown:

[0096] Next, we will deal with the various parameters inside the battery

[0097] ①R0, obtained by R=ΔU / I,

[0098] For example, at 0°C and battery SOC = 90%, its internal resistance

[0099] R0=

(UA-UB)+(UC-UD)

[0100] The values ​​of R0 under other conditions are similar.

[0101] ②The values ​​of R1, R2, C1, and C2 are obtained by exponential fitting.

[0102] We process and analyze the recovery process of the lithium-ion battery after discharge (the horizontal and vertical axes are operation time and measured voltage, i.e. x and y, respectively) and perform exponential fitting.

[0103] The specific expression is as follows

[0104] y=ab*exp(-c*x)-d*exp(-f*x)

[0105] Among them, a, b, c, d, f are parameters to be fitted (among which a can be directly substituted into the open circuit voltage Uoc). The specific fitting process is to import the experimental data into 1stop for fitting, and obtain the initial values ​​of b, c, d, f. Then, the curve fitting is performed through the curve fitter of Matlab to obtain the values ​​of b, c, d, f with a confidence level of 95%.

[0106] Substituting the formula into the future, we can get

[0107]

[0108] The value of R1R2C1C2 can be obtained.

[0109] The electro-thermal coupling model can analyze the working state of lithium-ion batteries in a shorter period of time, but it cannot describe the law of change in its entire life cycle. Therefore, it is necessary to introduce an aging model to better characterize the relationship between the various parameters of lithium-ion batteries. The research on aging models is mainly divided into three categories: electrochemical mechanism aging model, semi-empirical model and empirical model. The electrochemical mechanism aging model is established in combination with the internal aging mechanism of the battery. The research on the mechanism model involves many physical and chemical quantities, and many parameters are difficult to obtain accurately. The semi-empirical model has an intuitive expression and can better combine the input and output relationship. Therefore, it is more commonly used in battery life prediction and simulation. The empirical model does not care about the mechanism at all. Based on a large amount of data and intelligent algorithms, it establishes a mapping relationship between the battery health status and the influencing factors. It is often used to predict the remaining life of lithium-ion batteries. Based on this, this project will analyze the aging mechanism of lithium-ion batteries, and establish a semi-empirical model of lithium-ion battery capacity decline and a semi-empirical model of internal resistance growth, such as Fig.16 and Fig.17 shown.

[0110] Current research often combines the Arrhenius equation such as Formula Q1 to establish a semi-empirical model of battery capacity decay, that is, to fit the effect of temperature on battery capacity into an exponential function, and place the temperature on the exponent, such as Formula Q2. A large number of experiments have shown that the battery capacity decay rate is relatively linear in the early stage, mainly because the SEI film inside the battery is gradually formed during this process, and the internal chemical system of the battery is relatively stable. As the degree of aging continues to deepen, the internal structure of the battery is gradually destroyed, and its aging rate changes. Therefore, the effect of the number of cycles on the battery capacity decay rate is usually fitted into a power function such as Formula Q3, which reflects the change in capacity decay with the increase of aging time, and multiply it with Formula Q2 to establish a semi-empirical model that couples temperature and cycle number, as shown in Formula Q4.

[0111]

[0112] Q2=B×e f(T)

[0113] Q3=n C

[0114] Q4=D×e f(T) ×n E

[0115] Where Q1, Q2, Q3 and Q4 are capacity decay rates, A is the pre-exponential factor, E a is the activation energy, R is the gas constant, T is the temperature, B, C, D and E are the parameters to be fitted, and n is the number of cycles.

[0116] from Figure 16-17 It can be seen that the curve trend of internal resistance growth is different from that of capacity decay under different working conditions, and it is approximately an upward exponential function under all working conditions. At the same time, considering that temperature has a great influence on internal resistance growth (the influence of surface temperature on battery aging in many experiments is exponential), a model coupling temperature and cycle number is established:

[0117]

[0118] Where R is the predicted value of internal resistance; T is the temperature; n is the number of cycles; S, U, V, W and Z are the parameters to be fitted.

[0119] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0120] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements all fall within the scope of the present invention to be protected.

Claims

1. A multi-field coupling modeling method for lithium-ion batteries, characterized in that: include: Initialize and set lithium-ion battery parameters and given operating conditions; Build electrical models, thermal models, and aging models to achieve low-level electrical simulation, thermal simulation, and aging simulation; Complete parameter coupling and transfer to obtain new parameters as input variables; Real-time battery status parameters and the relationship between them are obtained through continuous iteration.

2. A lithium-ion battery multi-field coupling modeling method according to claim 1, characterized in that: The electrical model uses a second-order RC equivalent circuit model to describe the nonlinear characteristics of lithium batteries and the polarization effect during the charging and discharging process.

3. A lithium-ion battery multi-field coupling modeling method according to claim 2, characterized in that: According to the characteristic parameters of the second-order RC equivalent circuit model, a simulation model based on Matlab / simulink is constructed.

4. A lithium-ion battery multi-field coupling modeling method according to claim 3, characterized in that: The simulation model includes: The current signal module is used to generate a current signal that simulates the actual current, using a constant current signal and a pulse signal; The remaining power calculation module obtains the SOC value through the ampere-hour method; RC parameter calculation module, which uses the lookup module in the toolbox to find the RC parameter value corresponding to each SOC; The voltage calculation module obtains the relationship between the battery terminal voltage and the RC network voltage through the KCL law and the KVL law.

5. A lithium-ion battery multi-field coupling modeling method according to claim 1, characterized in that: The thermal model includes a heat generation model and a heat dissipation model.

6. A lithium-ion battery multi-field coupling modeling method according to claim 5, characterized in that: The heat generation model calculation formula is as follows:

7. A lithium-ion battery multi-field coupling modeling method according to claim 5, characterized in that: The heat dissipation model calculation formula is as follows: And the heat dissipation balance equation is obtained as:

8. A lithium-ion battery multi-field coupling modeling method according to claim 1, characterized in that: The construction of the aging model includes analyzing the aging mechanism of lithium-ion batteries and establishing a semi-empirical model of lithium-ion battery capacity decay and a semi-empirical model of internal resistance growth.

9. A lithium-ion battery multi-field coupling modeling method according to claim 8, characterized in that: The method for establishing the capacity decay semi-empirical model includes: Fit the effect of temperature on battery capacity into an exponential function and place the temperature on the exponent; Fit the effect of cycle number on battery capacity decay rate to form a power function; A semi-empirical model coupling temperature and cycle number is established.

10. A lithium-ion battery multi-field coupling modeling method according to claim 8, characterized in that: The semi-empirical model of internal resistance growth is a model that couples temperature and cycle number, and the formula is as follows:

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