Electrothermal coupling parameter identification method and identification device thereof

Abstract

The invention discloses a method and a device for identifying electrothermal coupling parameters. The identification method comprises the following steps: constructing an equivalent circuit model of a battery, and carrying out preliminary identification on electrical performance parameters corresponding to a plurality of charge state points; according to the heat production mechanism of the battery, a heat production model of the battery cell is constructed to be coupled into the battery equivalent circuit model, an electrothermal coupling model is obtained, and the heat production model comprises heat parameters; and jointly identifying the electrical performance parameters and the thermal parameters in the electrothermal coupling model to obtain optimized electrothermal coupling parameters. Through the identification method, the identification accuracy of the electrical performance parameters in the battery model can be improved, the heat production condition of the battery can be described more accurately, and the accuracy and the reliability of identification results of the electrical performance parameters and the heat parameters in the battery can be ensured at the same time.

Description

Technical Field

[0001] The present invention relates to the technical field of battery management, and in particular to a method for identifying an electrothermal coupling parameter, an electrothermal coupling parameter identification device, and a computer-readable storage medium. Background Art

[0002] Lithium-ion batteries have high energy density, long cycle life, no memory effect and are green and environmentally friendly. They are quickly becoming the latest energy storage batteries. However, in order to make them more accurate under simulation conditions, an accurate battery model must be established to estimate and predict the battery state. Currently, battery modeling is mainly divided into electrochemical models, equivalent circuit models and thermoelectric models. The electrochemical model mainly studies the principle of the molecular layer between materials. It has high accuracy but is relatively complex and takes a long time to calculate. Therefore, the equivalent circuit model is commonly used.

[0003] At present, the commonly used equivalent circuit models include Thevenin model, PNGV model and multi-order RC model. For the equivalent circuit model, the accuracy and reliability of the model are determined by the identification of model parameters. In the process of parameter identification of the equivalent circuit model, different methods have more or less defects and cannot be comprehensive. For example, the single least squares fitting calculation is simple, but its parameter error is large and cannot be well fitted; the Kalman filter method can obtain the optimal estimate of the state variable, but the existence of system noise will affect the accuracy of identification; and although the genetic algorithm can better identify the global situation, its local search ability is poor and it is easy to fall into the local optimal trap. At the same time, the battery equivalent circuit model alone cannot well describe the heating of the battery, and the influence of battery temperature on the electrical performance of the battery is more important.

[0004] In order to solve the above-mentioned problems existing in the prior art, the art urgently needs an identification technology for electrothermal coupling parameters, which can improve the identification accuracy of electrical performance parameters in battery models and more accurately describe the heat generation of the battery, and can simultaneously ensure the accuracy and reliability of the identification results of electrical performance parameters and thermal parameters in the battery. Summary of the invention

[0005] A brief summary of one or more aspects is given below to provide a basic understanding of these aspects. This summary is not an exhaustive overview of all conceived aspects, and is neither intended to identify the key or decisive elements of all aspects nor to define the scope of any or all aspects. Its only purpose is to give some concepts of one or more aspects in a simplified form as a prelude to a more detailed description that will be given later.

[0006] In order to overcome the above-mentioned defects of the prior art, the present invention provides a method for identifying electrothermal coupling parameters, an electrothermal coupling parameter identification device, and a computer-readable storage medium, which can improve the identification accuracy of electrical performance parameters in a battery model, and can also more accurately describe the heat generation of the battery, while ensuring the accuracy and reliability of the identification results of the electrical performance parameters and thermal parameters in the battery.

[0007] Specifically, the above-mentioned electrothermal coupling parameter identification method provided according to the first aspect of the present invention includes the following steps: constructing an equivalent circuit model of the battery, and preliminarily identifying the electrical performance parameters corresponding to multiple charge state points; constructing a heat generation model of the battery cell according to the heat generation mechanism of the battery to couple it to the battery equivalent circuit model to obtain an electrothermal coupling model, wherein the heat generation model includes thermal parameters; and jointly identifying the electrical performance parameters and thermal parameters in the electrothermal coupling model to obtain optimized electrothermal coupling parameters.

[0008] In addition, the above-mentioned electrothermal coupling parameter identification device provided according to the second aspect of the present invention includes: a memory; and a processor, the processor is connected to the memory and is configured to implement the above-mentioned electrothermal coupling parameter identification method provided by the first aspect of the present invention.

[0009] In addition, according to the third aspect of the present invention, there is also provided a computer-readable storage medium on which computer instructions are stored. When the computer instructions are executed by a processor, the above-mentioned method for identifying the electrothermal coupling parameters provided in the first aspect of the present invention is implemented. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The above features and advantages of the present invention can be better understood after reading the detailed description of the embodiments of the present disclosure in conjunction with the following drawings. In the drawings, the components are not necessarily drawn to scale, and components with similar related properties or features may have the same or similar reference numerals.

[0011] Figure 1 A flow chart of identifying parameters of an electrothermal coupling parameter according to some embodiments of the present invention is shown;

[0012] Figure 2 A flow chart for preliminarily identifying electrical performance parameters corresponding to a plurality of charge state points according to some embodiments of the present invention is shown;

[0013] Figure 3 A schematic diagram of a model of a second-order RC equivalent circuit provided according to some embodiments of the present invention is shown;

[0014] Figure 4A curve diagram showing the relationship between the charge state and the open circuit voltage of a single cell during the charging process in an HPPC experiment provided according to some embodiments of the present invention is shown;

[0015] Figure 5 A voltage relationship diagram corresponding to current excitation at multiple charge state points in an HPPC experiment provided according to some embodiments of the present invention is shown;

[0016] Figure 6 A schematic diagram of a voltage curve at a single charge state point in an HPPC experiment provided according to some embodiments of the present invention is shown;

[0017] Figure 7 A graph showing changes in electrical performance parameters with SOC after preliminary identification according to some embodiments of the present invention is shown;

[0018] Figure 8 A flowchart of obtaining an electrothermal coupling model provided according to some embodiments of the present invention is shown;

[0019] Fig. 9 A schematic diagram of an electrothermal coupling model provided according to some embodiments of the present invention is shown;

[0020] Fig.10 The adiabatic temperature rise curve and the calorific value curve of the battery under 25°C 0.5P discharge condition according to some embodiments of the present invention are shown;

[0021] Fig.11 shows a graph before and after identification of the entropy thermal coefficient provided in some embodiments of the present invention;

[0022] Fig.12 A fitting comparison curve diagram of a model voltage identification result provided according to some embodiments of the present invention and an experimental data curve is shown;

[0023] Fig.13 A fitting comparison curve diagram of a model temperature identification result and an experimental data curve provided according to some embodiments of the present invention is shown; and

[0024] Fig.14 A schematic structural diagram of an electrothermal coupling parameter identification device provided according to some embodiments of the present invention is shown.

[0025] Reference numerals:

[0026] Steps S110 to S130;

[0027] Steps S111 to S113;

[0028] Steps S121 to S123;

[0029] 140 Identification device for electrothermal coupling parameters;

[0030] 141 memory; and

[0031] 142 processors. DETAILED DESCRIPTION

[0032] The following specific embodiments illustrate the implementation of the present invention, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. Although the description of the present invention will be introduced in conjunction with the preferred embodiment, this does not mean that the features of this invention are limited to this implementation. On the contrary, the purpose of introducing the invention in conjunction with the implementation is to cover other options or modifications that may be extended based on the claims of the present invention. In order to provide a deep understanding of the present invention, the following description will include many specific details. The present invention can also be implemented without using these details. In addition, in order to avoid confusion or blurring the focus of the present invention, some specific details will be omitted in the description.

[0033] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0034] In addition, the terms "upper", "lower", "left", "right", "top", "bottom", "horizontal" and "vertical" used in the following description should be understood as the directions shown in the paragraph and the related drawings. Such relative terms are only used for the convenience of description and do not mean that the device described therein must be manufactured or operated in a specific direction, and therefore should not be understood as limiting the present invention.

[0035] It is understood that although the terms "first", "second", "third", etc. may be used herein to describe various components, regions, layers and / or parts, these components, regions, layers and / or parts should not be limited by these terms, and these terms are only used to distinguish different components, regions, layers and / or parts. Therefore, the first component, region, layer and / or part discussed below may be referred to as a second component, region, layer and / or part without departing from some embodiments of the present invention.

[0036] As mentioned above, in the process of parameter identification of equivalent circuit model, different identification methods have more or less some defects and cannot be comprehensive. For example, the single least squares fitting calculation is simple, but its parameter error is large and cannot be well fitted; Kalman filter method can get the optimal estimate of state variables, but the existence of system noise will affect the accuracy of identification; and although genetic algorithm can better identify the global situation, its local search ability is poor and it is easy to fall into the local optimal trap. At the same time, the heating of the battery cannot be well described by the battery equivalent circuit model alone, and the influence of battery temperature on the electrical performance of the battery is more important.

[0037] In order to solve the above-mentioned problems existing in the prior art, the present invention provides a method for identifying electrothermal coupling parameters, an electrothermal coupling parameter identification device, and a computer-readable storage medium, which can improve the identification accuracy of electrical performance parameters in a battery model, and can also more accurately describe the heat generation of the battery, while ensuring the accuracy and reliability of the identification results of the electrical performance parameters and thermal parameters in the battery.

[0038] In some non-limiting embodiments, the electrothermal coupling parameter identification device provided in the second aspect of the present invention may be used to implement the electrothermal coupling parameter identification method provided in the first aspect of the present invention.

[0039] Specifically, in some non-limiting embodiments, the computer-readable storage medium provided in the third aspect of the present invention stores computer instructions, which, when executed by a processor, can be used to implement the method for identifying the electrothermal coupling parameters provided in the first aspect of the present invention.

[0040] The working principle of the above-mentioned method for identifying the electrothermal coupling parameters will be described below in conjunction with some embodiments of the device for identifying the electrothermal coupling parameters. Those skilled in the art will understand that the embodiments of the method for identifying the electrothermal coupling parameters are only some non-restrictive implementations provided by the present invention, which are intended to clearly demonstrate the main concept of the present invention and provide some specific solutions that are convenient for the public to implement, rather than to limit all working modes or all functions of the device for identifying the electrothermal coupling parameters. Similarly, the device for identifying the electrothermal coupling parameters is also only a non-restrictive implementation provided by the present invention, and does not constitute a limitation on the implementation subject of each step of the method for identifying the electrothermal coupling parameters.

[0041] Please see Figure 1 , Figure 1 A flow chart of identifying parameters of an electrothermal coupling parameter provided according to some embodiments of the present invention is shown.

[0042] like Figure 1As shown, in some embodiments of the present invention, the method for identifying the electrothermal coupling parameters may include the following steps S110: constructing an equivalent circuit model of the battery, and preliminarily identifying the electrical performance parameters corresponding to multiple charge state points.

[0043] Specifically, as shown in FIG. 2 , step S110 can be embodied as steps S111 to S113 .

[0044] like Figure 2 As shown, first, step S111 may be performed: obtaining an equivalent circuit model of the battery, wherein the equivalent circuit model includes a multi-order RC equivalent circuit model.

[0045] Preferably, in this embodiment, the multi-order RC equivalent circuit model may include a second-order RC equivalent circuit model. In other optional embodiments, the selection of the battery model may also use an electrochemical model, a third-order equivalent circuit model, etc., but in order to facilitate system-level calculations, the second-order RC equivalent circuit model is used in this embodiment, which can ensure both calculation speed and calculation accuracy.

[0046] Specifically, see Figure 3 , Figure 3 A schematic diagram of a model of a second-order RC equivalent circuit provided according to some embodiments of the present invention is shown.

[0047] like Figure 3 As shown, a second-order RC equivalent circuit model of the battery can be constructed, wherein the mathematical expression of the second-order RC equivalent circuit model is:

[0048] Vo=Uoc-U1-U2-IR0

[0049]

[0050] Where Vo is the terminal voltage of the battery, Uoc is the open circuit voltage of the battery, R0 is the ohmic resistance of the battery, R1 and R2 are the polarization resistance of the battery, C1 and C2 are the polarization capacitance of the battery, and I is the load current. Figure 3 In the second-order RC equivalent circuit model shown in , the current is positive during discharge and negative during charge, that is, Figure 3 The current direction is discharge. Parameter identification is required in the second-order RC equivalent circuit model. The parameters that need to be solved in this second-order RC equivalent circuit model are R0, R1, R2, C1, and C2.

[0051] like Figure 3 As shown, the open circuit voltage Uoc is a constant voltage source, and the terminal voltage Vo when the open circuit voltage Uoc is in constant current discharge is obtained, wherein the calculation formula of the terminal voltage Vo can be:

[0052]

[0053] Based on the calculation formula of the terminal voltage Vo, and due to the change of Vo caused by the voltage drop on the ohmic resistor R0, the ohmic resistor R0 is obtained, wherein the expression of the ohmic resistor R0 is: That is to say, it can be determined that the resistance parameters R0, R1, R2 and the capacitance parameters C1, C2 are the electrical performance parameters to be preliminarily identified.

[0054] Continue back Figure 2 , step S112 may be executed to construct a first nonlinear least squares optimization model of the multi-order RC equivalent model.

[0055] Based on the nonlinear characteristics of the circuit, the problem of solving the above five parameters can be transformed into a nonlinear optimization problem. And since for any X1 vector, where X1 = [R1, R2, C1, C2], there is a unique Uo(t) corresponding to any time t, where Uo(t) is the terminal voltage corresponding to X1, the expression of Uo(t) corresponding to the X1 vector is:

[0056]

[0057] Based on the terminal voltage Uo(t) corresponding to X1 and the actual output terminal voltage V0(t) of the circuit, the first nonlinear least squares optimization model of the second-order RC equivalent circuit model can be constructed:

[0058] minFX1={Uo(t)-Vo(t)} 2

[0059] stX1(1)>0,X1(2)>0,X1(3)>0,X1(4)>0,t>0

[0060] Among them, X1(1) and X(2) correspond to resistance parameters R1 and R2 respectively, and X(3) and X(4) correspond to capacitance parameters C1 and C2 respectively.

[0061] Continue back Figure 2 , step S113 may be executed to solve the first nonlinear least squares optimization model and preliminarily identify the electrical performance parameters corresponding to each charge state point.

[0062] When solving the first nonlinear least squares optimization model, the initial parameter values ​​of the resistance parameters R1, R2 and the capacitance parameters C1, C2 can be determined first, and then, the Levenberg-Marquard algorithm (LM algorithm for short) can be used to continuously iterate the initial parameter values ​​of the resistance parameters R1, R2 and the capacitance parameters C1, C2 to find the optimal X1 vector so as to minimize the FX1 value of the first nonlinear least squares optimization model; and based on the minimum FX1 value of the first nonlinear least squares optimization model, the electrical performance parameters corresponding to each state of charge (SOC) point after preliminary identification can be obtained.

[0063] In some optional embodiments, taking lithium batteries as an example, the experimental object can be a single battery with a nominal capacity of 280Ah, and the experimental environment is a constant temperature of 25°C, 35°C, and 45°C. Since the internal parameters of lithium batteries are constantly changing, the RC parameter identification at different charge states (SOC) points corresponds to different open circuit voltages Uoc, which can be obtained through a hybrid power pulse characteristic (HPPC) experiment, including current excitation, static, and capacity adjustment steps, and finally the SOC-Uoc relationship is obtained.

[0064] First, you can refer to Figure 4 , Figure 4 A curve diagram showing the relationship between the state of charge and the open circuit voltage of a single cell during the charging process in an HPPC experiment provided according to some embodiments of the present invention is shown. Figure 4 The voltage change curve of a single battery during the charging process is shown in Figure 1, where the horizontal axis is the battery charge state and the vertical axis is the voltage value. Figure 4 As shown, when SOC is 0-0.1, the voltage rises rapidly under current excitation and quickly reaches the plateau period. The theoretical plateau period of the cell voltage in this embodiment is 3.2V. Finally, at the end of SOC, the voltage will rise slightly. Different RC parameters are obtained by identifying the relationship between different charge states and open circuit voltage.

[0065] You can then refer to Figure 5 , Figure 5 FIG. 4 shows a voltage relationship diagram corresponding to current excitation at multiple charge state points in an HPPC experiment provided according to some embodiments of the present invention. Figure 5 As shown, the capacity adjustment step in the HPPC experiment includes adjusting the battery SOC to the required value and then performing the next step of current excitation. Figure 5It can represent the voltage curve corresponding to the current excitation at multiple charge state points. In the process of HPPC experiment, when the battery is fully charged, it can be discharged with a 0.5C pulse current, and then the charge can be adjusted, and then it can be left to stand, and the experiment can be repeated until the battery is discharged, so as to adjust the battery SOC and perform current pulse tests at multiple SOC points. At the end of each charge adjustment and standing stage (no polarization reaction is assumed, and the terminal voltage V0 is the open circuit voltage Uoc), the terminal voltage, that is, the Uoc value corresponding to the current SOC, is tested to obtain the SOC-Uoc curve.

[0066] For further information, see Figure 6 , Figure 6 A schematic diagram of a voltage curve at a single charge state point in an HPPC experiment provided according to some embodiments of the present invention is shown.

[0067] like Figure 6 As shown, point A is the moment when the battery starts discharging, point C is the moment when the battery discharge ends, curve segment AB is the beginning of the discharge segment, and the terminal voltage Vo(t) drops rapidly due to the ohmic resistance R0. Curve segment BC is the terminal voltage that slowly drops due to the polarization characteristics inside the battery, and the terminal voltage corresponding to curve segment BC can be the first terminal voltage Vo1(t). The process of the first terminal voltage Vo1(t) slowly dropping is approximately a zero-state response, where the expression of the first terminal voltage Vo1(t) is:

[0068]

[0069] Among them, Uoc SOC 、R0 SOC 、R1 SOC , R2 SOC 、C1 SOC 、C2 SOC They are the open circuit voltage, ohmic resistance, polarization resistance and polarization capacitance corresponding to a specific single charge state point.

[0070] Continue as Figure 6 As shown, the reverse process of curve segment AB is curve segment CD, and curve segment DE is the terminal voltage that rises slowly due to the rebound characteristics of the battery. The terminal voltage corresponding to curve segment DE can be the second terminal voltage Vo2(t). The process of the second terminal voltage Vo2(t) from static to stable state is approximately zero input response, where the expression of the second terminal voltage Vo2(t) is:

[0071]

[0072] The characteristics of the curve segments BC and DE are analyzed by the first terminal voltage Vo1(t) and the second terminal voltage Vo2(t), and then the ohmic resistance R0 corresponding to the single battery at a single charge state point is obtained according to the terminal voltages corresponding to the curve segments AB and CD. SOC , where the expression for ohmic resistance is:

[0073]

[0074] Among them, VoA is the terminal voltage at point A, VoB is the terminal voltage at point B, VoC is the terminal voltage at point C, and VoD is the terminal voltage at point D.

[0075] Afterwards, the ohmic resistance R0 corresponding to a single charge state point can be calculated. SOC , and combined with the curve segment DE according to the voltage rebound characteristic, a first nonlinear least square optimization model is established to determine the initial parameter values ​​of the resistance parameters R1, R2 and the capacitance parameters C1, C2.

[0076] Specifically, the ohmic resistance R0 corresponding to a single charge state point can be SOC , and combined with the curve segment DE according to the voltage rebound characteristic, a first nonlinear least squares optimization model minFX1 = {Uo(t) - Vo(t)} is established 2 , stX1(1)>0,X1(2)>0,X1(3)>0,X1(4)>0,t>0, so as to preliminarily identify the resistance parameters R1, R2 and capacitance parameters C1, C2 corresponding to each SOC point. Based on the preliminary identification results of the resistance parameters R1, R2 and capacitance parameters C1, C2 corresponding to each SOC point, the relationship expressions of the resistance parameters R1, R2 and capacitance parameters C1, C2 can be obtained respectively, where the relationship expressions of the resistance parameters are:

[0077]

[0078] The relationship expression of capacitance parameters is:

[0079]

[0080] Alternatively, the HPPC experimental data of a single cell at SOC = 0 is used and substituted into the expression of ohmic resistance: Relationship expressions of resistance parameters And the relationship expression of capacitance parameters Furthermore, in order to facilitate calculation, the selection of initial parameter values ​​can be simplified. For example, the two pairs of RC parameters are the same by default, that is, R1 SOC =R2 SOC , C1SOC =C2 SOC , and Figure 6 Substituting the corresponding points (point A, point B, point C, point D and point E) into the above three relational expressions for calculation, the initial parameter values ​​of the resistance parameters R0, R1, R2 and the capacitance parameters C1, C2 can be obtained. The initial parameter values ​​of the resistance parameters R0, R1, R2 and the capacitance parameters C1, C2 can be shown in the following Table 1:

[0081] R0 R1 R2 C1 C2 Parameter Value 0.00027Ω 0.000207Ω 0.000207Ω 109910F 109910F

[0082] Table 1

[0083] Afterwards, according to the experimentally measured data, and using the above-mentioned relationship expression between the capacitance parameter and the resistance parameter, the FX1 minimum value of the first nonlinear least squares optimization model can be obtained through offline preliminary identification. Optionally, offline preliminary identification can be performed through the Matlab toolbox, because this identification process is an offline process, and the identification process is the process of solving the first least squares optimization model.

[0084] You can refer to Figure 7 , Figure 7 FIG. 4 shows a curve diagram of electrical performance parameters after preliminary identification according to some embodiments of the present invention as a function of SOC. Figure 7 As shown in the figure, from left to right and from top to bottom, the resistance parameters R0, R1, R2 at different SOC points and the capacitance parameters C1, C2 values ​​are respectively shown as the SOC changes. Figure 7 In the figure, the horizontal axis is SOC, R0 is the ohmic internal resistance, which gradually decreases with the increase of SOC, R1 is relatively high at the beginning, and remains unchanged after a rapid decrease, R2 basically oscillates between 0.0005-0.0009, C1 rises slowly, and C2 is also in an oscillating trend.

[0085] Based on the minimum value of FX1, that is, the preliminary identification results of the resistance parameters R1, R2 and the capacitance parameters C1, C2 corresponding to each charge state point, the electrical performance parameters corresponding to each temperature point and each charge state point after the preliminary identification are summarized into a table including resistance parameters R0 (SOC, T), R1 (SOC, T), R2 (SOC, T), and capacitance parameters C1 (SOC, T), C2 (SOC, T).

[0086] In the present invention, in order to better fit the actual heating situation of the battery, an electrothermal coupling module is added, that is, the influence of the voltage and current changes of the battery on the heat generation of the battery cell is taken into account. At the same time, the temperature change of the battery cell affects its internal parameters. Therefore, the battery adiabatic temperature rise data curve is used to fit the entropy thermal coefficient of the battery cell to make it more accurate.

[0087] Please continue to return Figure 1The above-mentioned electrothermal coupling parameter identification method provided by the present invention may also include step S120: constructing a heat generation model of the battery cell according to the heat generation mechanism of the battery to couple it to the battery equivalent circuit model to obtain an electrothermal coupling model, wherein the heat generation model includes thermal parameters.

[0088] Specifically, referring to FIG. 8 , step S120 can be embodied as steps S121 to S123 .

[0089] like Figure 8 As shown, first, step S121 may be performed: obtaining the total thermal power of the battery according to the heat generation mechanism of the battery.

[0090] The total heat generation power of the battery refers to the total power consumed by a pure resistance circuit or a non-pure resistance circuit under a certain voltage and current, including irreversible heat power Pir and reversible heat power Pr. The calculation formula for the total heat generation power is: P=Pir+Pr, where Pir is the irreversible heat power of the battery and Pr is the reversible heat power of the battery.

[0091] Next, step 122 may be performed: obtaining a heat generation model of the battery cell based on the irreversible thermal power model and the reversible thermal power model of the battery.

[0092] Specifically, since the irreversible heat is mainly caused by the internal resistance of the battery cell, the capacitance parameters R0, R1, R2 and the resistance parameters C1, C2 can be preliminarily identified based on the second-order RC equivalent circuit model to determine the irreversible thermal power model of the battery. The expression of the irreversible thermal power model is: Wherein, Vb is the battery volume.

[0093] Reversible heat mainly includes reaction heat. The reaction heat generated by the chemical reaction inside the lithium battery is transferred to the environment, absorbing heat during charging and releasing heat during discharging. Therefore, the reversible thermal power model of the battery can be determined based on the Bernardi heat generation rate model, where the expression of the reversible thermal power model is:

[0094]

[0095] Where Vb is the battery volume, is the entropy thermal coefficient, which is the partial differential of the battery voltage with respect to temperature. It affects the heat generated by the battery and can therefore be used as a thermal parameter.

[0096] Obtain the heat generation model of the battery cell, where the expression of the heat generation model can be:

[0097]

[0098] in, is the derivative of heat. Since the battery cell dissipates heat to the outside world through the outer wall of the battery, the Newton cooling formula can be used, and its heat dissipation model is Q=hA(Ts-Ta), where h is the heat transfer coefficient, Ts is the surface temperature of the battery cell, Ta is the ambient temperature, and A is the surface area of ​​the battery cell.

[0099] Continue as Figure 8 As shown, step S123 may be performed: coupling the heat generation model of the battery cell to the battery equivalent circuit model to obtain an electrothermal coupling model to reflect the influence between the battery temperature and its electrical performance.

[0100] You can refer to Fig. 9 , Fig. 9 A schematic diagram of an electrothermal coupling model provided according to some embodiments of the present invention is shown.

[0101] like Fig. 9 As shown, combined Figure 3 The second-order RC equivalent circuit model shown in the figure inputs the current to obtain the voltage and temperature of the battery. Through the heat generation model, the current is input and the heat is calculated. The state of charge SOC is calculated based on the input current through the ampere-hour integration method.

[0102] After completing the battery's electrothermal coupling model, the RC parameters and entropy thermal coefficient can be optimized by fitting the voltage, current and temperature change curves during the charge and discharge phases using the least squares method.

[0103] Please go back Figure 1 The above-mentioned electrothermal coupling parameter identification method provided by the present invention may also include step S130: jointly identifying the electrical performance parameters and thermal parameters in the electrothermal coupling model to obtain optimized electrothermal coupling parameters.

[0104] First, the heat change of the battery during the charge and discharge stage can be obtained. The expression of heat change (heating amount and temperature) is:

[0105] Q=mCpΔT

[0106] Where m is the mass of the battery, Cp is the specific heat capacity, and ΔT is the difference between the battery temperature and the initial temperature.

[0107] Next, an adiabatic temperature rise test is performed on the battery under a preset condition to obtain its temperature change. For example, an adiabatic temperature rise test can be performed on a battery under a 0.5P discharge condition, and the initial temperature value is set to 25°C. Fig.10 As shown, Fig.10 An adiabatic temperature rise curve and a calorific value curve under 25° C. 0.5P discharge conditions of a battery provided according to some embodiments of the present invention are shown.

[0108] Due to the entropy coefficient Affects the heat generated by the battery, so The identification problem is transformed into a nonlinear optimization problem. From the expression of the reversible thermal power model, it can be seen that for any At any time t, there is a unique total heating power Correspondingly, a second linear least squares optimization model of the reversible thermal power model can be constructed, wherein the expression of the second linear least squares optimization model is:

[0109] as well as

[0110] The minimum value of FX2 of the second linear least squares optimization model is obtained to optimize the entropy heat coefficient. The optimal solution X2 of the second nonlinear least squares model is solved, which is the parameter value of the ideal entropy heat coefficient.

[0111] After that, the RC parameters and the entropy thermal coefficient can be identified through the Simulink toolbox. Specifically, the battery voltage and temperature can be used as two target values ​​to identify the resistance parameters R0, R1, R2, the capacitance parameters C1, C2, and the entropy thermal coefficient. At the same time, identification is performed. The electrothermal coupling parameters corresponding to each optimized temperature point and each charge state point are obtained, and the electrothermal coupling parameters can be updated into a table of resistance parameters R0'(SOC', T'), R1'(SOC', T'), R2'(SOC', T'), and capacitance parameters C1'(SOC', T'), C2'(SOC', T').

[0112] For further information, see Fig.11 , Fig.11 Graphs before and after identification of the entropy thermal coefficient provided in some embodiments of the present invention are shown. Fig.11 As shown, the dotted line represents the entropy thermal coefficient change curve corresponding to each charge state (SOC) before the entropy thermal coefficient identification, and the solid line represents the entropy thermal coefficient change curve corresponding to each charge state (SOC) after the thermal coefficient identification. After identification and optimization, the entropy thermal coefficient corresponding to each charge state (SOC) can be more in line with the actual entropy thermal coefficient change of the battery cell.

[0113] In the present invention, data identification and optimization are performed through the experimental data of adiabatic temperature rise, so that the simulation accuracy of the electrothermal coupling model can be improved. Further, the identification results can be compared with the experimental results, please refer to Fig.12 and Fig.13 , Fig.12 The figure shows the fitting comparison curve between the model voltage identification result and the experimental data curve. Fig.13 The figure shows the fitting comparison curve between the model temperature identification result and the experimental data curve.

[0114] like Fig.12 and Fig.13 As shown, by jointly identifying the RC parameters and the entropy thermal coefficient and taking into account the heating value of the battery cell, the model voltage identification result is more accurate, and the battery's heating temperature rise curve fitting is also more accurate.

[0115] In the above-mentioned embodiment of the present invention, a second-order RC model of a lithium battery is modeled, and the least square method is used to identify its parameters at each SOC point through test data to form an equivalent circuit model of the entire SOC domain, and the heat generation model of the battery cell is constructed through the heat generation mechanism of the battery, coupled to the battery equivalent circuit model, and then the adiabatic temperature rise test of the battery is carried out to obtain its temperature change, and its parameters are fitted synchronously through the battery temperature and voltage changes to make it more accurate and reliable. Compared with the prior art, the nonlinear least square method is used for the parameters of the RC module, and the LM algorithm is used to solve it, which can improve the problem of large errors caused by the nonlinear least square method for parameter identification; the parameters of the battery heat generation model are optimized through the adiabatic temperature rise curve of the battery, and the heat generation of the battery is described more accurately.

[0116] In some other optional embodiments of the present invention, the electrical performance parameters and thermal parameters can be identified separately, that is, the electrical performance parameters of the equivalent circuit model are first identified, and then the identified electrical performance parameters are brought into the heat generation model to identify the entropy thermal coefficient in the heat generation model. However, the above-mentioned identification of the electrical performance parameters and the entropy thermal coefficient parameters together in the present invention will make the identification result more accurate and the identification effect better.

[0117] Although the above methods are illustrated and described as a series of actions for simplicity of explanation, it should be understood and appreciated that these methods are not limited by the order of the actions, because according to one or more embodiments, some actions may occur in a different order and / or concurrently with other actions from those illustrated and described herein or not illustrated and described herein but understandable to those skilled in the art.

[0118] So far, the above-mentioned method for identifying the electrothermal coupling parameters provided by the first aspect of the present invention has been introduced. Fig.14 , Fig.14 A schematic structural diagram of an electrothermal coupling parameter identification device provided according to some embodiments of the present invention is shown.

[0119] like Fig.14As shown, the second aspect of the present invention provides an electrothermal coupling parameter identification device 140. The electrothermal coupling parameter identification device 140 may be configured with a memory 141 and a processor 142. The memory 141 may include but is not limited to the computer-readable storage medium provided in the third aspect of the present invention, on which computer instructions are stored. The processor 142 may be connected to the memory 141 and configured to execute the computer instructions stored in the memory to implement the electrothermal coupling parameter identification method provided in the first aspect of the present invention.

[0120] In summary, the present invention provides an identification method of electrothermal coupling parameters, an identification device of electrothermal coupling parameters, and a computer-readable storage medium, which can improve the identification accuracy of electrical performance parameters in a battery model, and can also more accurately describe the heat generation of the battery, while ensuring the accuracy and reliability of the identification results of the electrical performance parameters and thermal parameters in the battery. Taking lithium-ion batteries as an example, an electrothermal coupling model of lithium-ion batteries is established based on the circuit model and the heat generation model of the battery core. The lithium-ion battery circuit model adopts a second-order equivalent circuit model based on SOC and temperature T; the lithium-ion battery is subjected to constant capacity, OCV test, HPPC test and adiabatic temperature rise test to obtain its current, voltage and temperature data; the lithium-ion battery circuit model and thermal model are jointly identified with the data obtained from the above experiments. The results obtained by the identification will be better than those obtained by a single identification. The algorithm used is the least squares method to obtain RC parameters and entropy thermal coefficients, and an optimized model database is established. Compared with other methods, the present invention adopts a joint identification method, and the parameter values ​​are more accurate. In some other embodiments, the above-mentioned electrothermal coupling model provided by the present invention can also be applied to batteries such as LFP lithium iron phosphate, NCA, NMC ternary lithium, and LMO lithium manganese oxide.

[0121] The previous description of the disclosure is provided to enable any person skilled in the art to make or use the disclosure. Various modifications to the disclosure will be apparent to those skilled in the art, and the general principles defined herein may be applied to other variations without departing from the spirit or scope of the disclosure. Thus, the disclosure is not intended to be limited to the examples and designs described herein, but should be granted the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for identifying electrothermal coupling parameters, characterized in that: The following steps are involved: Construct an equivalent circuit model of the battery and preliminarily identify the electrical performance parameters corresponding to multiple charge state points; According to the heat generation mechanism of the battery, a heat generation model of the battery cell is constructed to couple it to the battery equivalent circuit model to obtain an electrothermal coupling model, wherein the heat generation model includes thermal parameters; as well as The electrical performance parameters and the thermal parameters in the electrothermal coupling model are jointly identified to obtain optimized electrothermal coupling parameters.

2. The identification method as claimed in claim 1, characterized in that: The steps of constructing an equivalent circuit model of the battery and preliminarily identifying the electrical performance parameters corresponding to multiple charge state points include: Acquire an equivalent circuit model of the battery, wherein the equivalent circuit model includes a multi-order RC equivalent circuit model; constructing a first nonlinear least squares optimization model of the multi-order RC equivalent model; and The first nonlinear least squares optimization model is solved, and the electrical performance parameters corresponding to each of the charge state points are preliminarily identified.

3. The identification method as claimed in claim 2, characterized in that: The multi-order RC equivalent circuit model includes a second-order RC equivalent circuit model, and the step of obtaining the equivalent circuit model of the battery includes: A second-order RC equivalent circuit model of the battery is constructed, wherein the mathematical expression of the second-order RC equivalent circuit model is: Vo=Uoc-U1-U2-IR0 Wherein, Vo is the terminal voltage of the battery, Uoc is the open circuit voltage of the battery, R0 is the ohmic resistance of the battery, R1 and R2 are the polarization resistance of the battery, C1 and C2 are the polarization capacitance of the battery, and I is the load current; The terminal voltage Vo when the open circuit voltage Uoc is in constant current discharge is obtained, wherein the calculation formula of the terminal voltage Vo is: Based on the calculation formula of the terminal voltage Vo, the ohmic resistance R0 is obtained, wherein the expression of the ohmic resistance R0 is: as well as The resistance parameters R0, R1, R2 and the capacitance parameters C1, C2 are determined as the electrical performance parameters to be preliminarily identified.

4. The identification method as claimed in claim 3, characterized in that: The step of constructing the first nonlinear least squares optimization model of the multi-order RC equivalent model comprises: Based on the nonlinear characteristics of the circuit, and for any X1 vector, where X1 = [R1, R2, C1, C2], any time t has a unique Uo(t) corresponding to it, Uo(t) is the terminal voltage corresponding to X1, and the expression of Uo(t) corresponding to the X1 vector is: Based on the corresponding terminal voltage Uo(t) of X1 and the actual output terminal voltage V o (t), constructing a first nonlinear least squares optimization model of the second-order RC equivalent circuit model: minFX1={Uo(t)-Vo(t)} 2 stX1(1)>0,X1(2)>0,X1(3)>0,X1(4)>0,t>0 Among them, X1(1) and X(2) correspond to the resistance parameters R1 and R2 respectively, and X(3) and X(4) correspond to the capacitance parameters C1 and C2 respectively.

5. The identification method as claimed in claim 4, characterized in that: The step of solving the first nonlinear least squares optimization model and preliminarily identifying the electrical performance parameters corresponding to each of the charge state points includes: Determine initial parameter values ​​of the resistance parameters R1, R2 and the capacitance parameters C1, C2; Iterating the initial parameter values ​​of the resistance parameters R1, R2 and the capacitance parameters C1, C2 by using the Levenberg-Marquard algorithm to minimize the FX1 value of the first nonlinear least squares optimization model; and Based on the minimum value of FX1 of the first nonlinear least squares optimization model, the electrical performance parameters corresponding to each of the charge state points after preliminary identification are obtained.

6. The identification method as claimed in claim 5, characterized in that: The step of determining the initial parameter values ​​of the resistance parameters R1, R2 and the capacitance parameters C1, C2 comprises: By performing a mixed power pulse characteristic experiment on a single cell, a charge state-open circuit voltage relationship curve of the single cell is obtained; Based on the charge state-open circuit voltage relationship curve, determine the first terminal voltage corresponding to the curve segment BC of the single battery that slowly decreases due to the polarization characteristics inside the battery, and the second terminal voltage corresponding to the curve segment DE that slowly rises to a stable state due to the voltage rebound characteristics of the battery; Based on the first terminal voltage and the second terminal voltage, determine the ohmic resistance R0 corresponding to the single battery at a single charge state point SOC ;as well as Based on the ohmic resistance R0 corresponding to the single charge state point SOC , and in combination with the curve segment DE according to the voltage rebound characteristic, the first nonlinear least squares optimization model is established to determine the initial parameter values ​​of the resistance parameters R1, R2 and the capacitance parameters C1, C2.

7. The identification method as claimed in claim 6, characterized in that: The step of determining, based on the charge state-open circuit voltage relationship curve, a first terminal voltage corresponding to a curve segment BC of the single battery that slowly decreases due to the polarization characteristics inside the battery, and a second terminal voltage corresponding to a curve segment DE of the single battery that slowly rises to a stable state due to the rebound characteristics of the battery comprises: Based on the charge state-open circuit voltage relationship curve, determine the battery discharge start time point A, the battery discharge end time point C, the discharge start curve segment AB where the terminal voltage drops rapidly due to the ohmic resistance R0, the first terminal voltage slowly drops due to the polarization characteristics of the battery, the reverse process of the curve segment AB is the curve segment CD, and the second terminal voltage slowly rises to a stable state due to the rebound characteristics of the battery. The curve segment DE; Based on the polarization characteristics inside the battery, the process of the first terminal voltage Vo1(t) slowly decreasing is approximately a zero-state response, wherein the expression of the first terminal voltage Vo1(t) is: Among them, Uoc SOC 、R0 SOC 、R1 SOC , R2 SOC 、C1 SOC 、C2 SOC are the open circuit voltage, ohmic resistance, polarization resistance and polarization capacitance corresponding to the single charge state point respectively; and Based on the rebound characteristics of the battery, the process of the second terminal voltage Vo2(t) from static to stable state is approximately zero input response, wherein the expression of the second terminal voltage Vo2(t) is:

8. The identification method as claimed in claim 7, characterized in that: The ohmic resistance R0 corresponding to the single battery at a single charge state point is determined based on the first terminal voltage and the second terminal voltage. SOC The steps include: According to the terminal voltages corresponding to the curve segment AB and the curve segment CD, the ohmic resistance corresponding to the single battery at the single charge state point is obtained, wherein the expression of the ohmic resistance is: Among them, VoA is the terminal voltage at point A, VoB is the terminal voltage at point B, VoC is the terminal voltage at point C, and VoD is the terminal voltage at point D.

9. The identification method as claimed in claim 8, characterized in that: The ohmic resistance R0 corresponding to the single charge state point SOC , and in combination with the curve segment DE according to the voltage rebound characteristic, establishing the first nonlinear least squares optimization model, the step of determining the initial parameter values ​​of the resistance parameters R1, R2 and the capacitance parameters C1, C2 includes: According to the ohmic resistance R0 corresponding to the single charge state point SOC , and combined with the curve segment DE according to the voltage rebound characteristic, the relationship expressions of the resistance parameters R1, R2 and the capacitance parameters C1, C2 are obtained respectively to establish a first nonlinear least squares optimization model, wherein the relationship expression of the resistance parameters is: The relational expression of the capacitance parameter is: Substitute the hybrid power pulse characteristic experimental data of the single cell when SOC=0 into the expression of the ohmic resistance, the relationship expression of the resistance parameters, and the relationship expression of the capacitance parameters to obtain the initial parameter values ​​of the resistance parameters R0, R1, R2 and the capacitance parameters C1, C2.

10. The identification method as claimed in claim 8, characterized in that: The step of obtaining the electrical performance parameters corresponding to each of the charge state points after preliminary identification based on the minimum value of FX1 of the first nonlinear least squares optimization model comprises: Obtaining the minimum value of FX1 of the first nonlinear least squares optimization model through offline identification; and Based on the minimum value of FX1, the electrical performance parameters corresponding to each temperature point and each charge state point after the preliminary identification are obtained, including resistance parameters R0 (SOC, T), R1 (SOC, T), R2 (SOC, T), and capacitance parameters C1 (SOC, T), C2 (SOC, T).

11. The identification method as claimed in claim 10, characterized in that: The step of constructing a heat generation model of the battery cell according to the heat generation mechanism of the battery to couple it to the battery equivalent circuit model to obtain an electrothermal coupling model, wherein the step of including thermal parameters in the heat generation model comprises: According to the heat generation mechanism of the battery, the total heat power of the battery is obtained, wherein the calculation formula of the total heat power is: P=Pir+Pr Wherein, Pir is the irreversible thermal power of the battery, and Pr is the reversible thermal power of the battery; Based on the irreversible thermal power model and the reversible thermal power model of the battery, obtaining a heat generation model of the battery cell; and The heat generation model of the battery cell is coupled to the battery equivalent circuit model to obtain the electrothermal coupling model to reflect the influence between the temperature of the battery and its electrical performance.

12. The identification method as claimed in claim 11, characterized in that: The step of obtaining the heat generation model of the battery cell based on the irreversible thermal power model and the reversible thermal power model of the battery comprises: Based on the capacitance parameters R0, R1, R2 and the resistance parameters C1, C2 preliminarily identified based on the second-order RC equivalent circuit model, the model of the irreversible thermal power of the battery is determined, wherein the expression of the irreversible thermal power model is: as well as Where Vb is the battery volume, According to the heat generation rate model, the reversible thermal power model of the battery is determined, wherein the expression of the reversible thermal power model is: in, is the entropy thermal coefficient, serving as the thermal parameter; and Obtain a heat generation model of the battery cell, wherein the expression of the heat generation model is: in, It is the derivative of heat. Since the battery cell dissipates heat to the outside through the outer wall of the battery, its heat dissipation model is Q=hA(Ts-Ta), where h is the heat transfer coefficient, Ts is the surface temperature of the battery cell, Ta is the ambient temperature, and A is the surface area of ​​the battery cell.

13. The identification method as claimed in claim 12, characterized in that: The step of jointly identifying to optimize the electrical performance parameters and thermal parameters in the electrothermal coupling model to obtain optimized electrothermal coupling parameters comprises: The heat change of the battery during the charge and discharge stage is obtained, and the expression of the heat change is: Q=mCpΔT Where m is the mass of the battery, Cp is the specific heat capacity, and ΔT is the difference between the battery temperature and the initial temperature; and The battery is subjected to an adiabatic temperature rise test under a preset operating condition, and for any At any time t, there is a unique total heating power Correspondingly, a second linear least squares optimization model of the reversible thermal power model is constructed, wherein the expression of the second linear least squares optimization model is: as well as The minimum value of FX2 of the second linear least squares optimization model is obtained to optimize the entropy thermal coefficient.

14. The identification method as claimed in claim 13, characterized in that: The step of jointly identifying to optimize the electrical performance parameters and thermal parameters in the electrothermal coupling model to obtain optimized electrothermal coupling parameters comprises: Taking the voltage and temperature of the battery as two target values, the resistance parameters R0, R1, R2, the capacitance parameters C1, C2, and the entropy thermal coefficient Simultaneous identification; and The electrothermal coupling parameters corresponding to each optimized temperature point and each charge state point are obtained, and the electrothermal coupling parameters are updated to resistance parameters R0'(SOC', T'), R1'(SOC', T'), R2'(SOC', T'), and capacitance parameters C1'(SOC', T'), C2'(SOC', T').

15. An identification device for electrothermal coupling parameters, characterized in that: include: Memory; as well as A processor is connected to the memory and is configured to implement the method for identifying the electrothermal coupling parameters according to any one of claims 1 to 14.

16. A computer-readable storage medium having computer instructions stored thereon, characterized in that: When the computer instructions are executed by a processor, the method for electrothermal coupling parameters according to any one of claims 1 to 14 is implemented.

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