Modeling method and device for battery pack simulation platform

Abstract

The invention discloses a modeling method and device for a battery pack simulation platform, and the method comprises the steps: determining the types of batteries and the number of single batteries according to the requirements of an operation environment for a battery pack; equivalent circuit model parameters of each single battery are obtained through a battery parameter identification experiment; determining a thermal model parameter and an aging model parameter of each battery monomer according to the battery type and the operating environment; and obtaining a heat-electricity-aging coupling model based on the equivalent circuit model parameter, the heat model parameter and the aging model parameter of each battery monomer. According to the invention, the test cost can be greatly reduced, and the complex thermoelectric reaction of the single battery can be simulated; and inconsistent parameters can be given to the thermal model and the aging model of the single battery, so that the aging inconsistent evolution process of the battery pack can be visually reflected, and the authenticity of the battery pack simulation platform is enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of modeling of a battery pack simulation platform, and in particular to a modeling method and device for a battery pack simulation platform. Background Art

[0002] Due to the inconsistency between battery cells within the battery pack, it is more complicated to model the battery pack and conduct full life cycle state estimation research. If the full life cycle aging data of all cells in the battery pack can be obtained, it will inevitably bring great convenience to the modeling of the battery pack and the full life cycle state estimation. However, the currently available full life cycle aging data of battery cells are relatively small and incomplete, and there are even fewer aging data for battery packs. There are two main reasons: on the one hand, the full life cycle aging experiment of the battery requires high precision of the experimental equipment, and on the other hand, the experimental time is too long, and the time required for thousands of cycle aging experiments is too long. If a battery pack simulation platform that takes into account the inconsistency of cell aging can be established in a simulation manner, thereby simulating the aging trend of a real battery pack, then the pain points of battery pack research can be solved.

[0003] However, there is currently no method for modeling a battery pack simulation platform over its entire life cycle. Summary of the invention

[0004] In response to the problems in the prior art, an embodiment of the present invention provides a modeling method and device for a battery pack simulation platform. The present invention models a battery pack simulation platform over its entire life cycle, and can simulate the aging trend of the battery pack over its entire life cycle, providing a simulation data set for the research on the full life state estimation method of the battery pack and the research on the inconsistent aging trend of the battery pack.

[0005] An embodiment of the present invention provides a modeling method for a battery pack simulation platform, the method comprising:

[0006] Determine the battery type and number of battery cells based on the operating environment's requirements for the battery pack;

[0007] The equivalent circuit model parameters of each battery cell are obtained through battery parameter identification experiments;

[0008] Determine the thermal model parameters and aging model parameters of each battery cell according to the battery type and operating environment;

[0009] A thermal-electrical-aging coupling model is obtained based on the equivalent circuit model parameters, the thermal model parameters and the aging model parameters of each battery cell.

[0010] In one embodiment, the equivalent circuit model parameters include ohmic internal resistance, polarization internal resistance, polarization capacitance, and maximum available capacity.

[0011] In one embodiment, the equivalent circuit model parameters of each battery cell obtained through the battery parameter identification experiment specifically include:

[0012] In the battery parameter identification experiment, normal distribution statistics are used to obtain the equivalent circuit model parameters of each battery cell.

[0013] In one embodiment, in the battery parameter identification experiment, obtaining the equivalent circuit model parameters of each battery cell using normal distribution statistics specifically includes:

[0014] In the battery parameter identification experiment, the mean and standard deviation of the first equivalent circuit model parameters of each battery cell at different temperatures are calculated, and a corresponding normal distribution is generated based on the mean and the standard deviation. The second equivalent circuit model parameters of each battery cell are obtained based on the normal distribution statistics.

[0015] In one embodiment, the thermal model in the thermal-electrical-aging coupling model adopts a heat source transfer model.

[0016] In one embodiment, the heat source transfer model is expressed as follows:

[0017] T 后 =∫((I 2 Rh(T 前 -T 0 )A) / mC m )dt+T 0 ;

[0018] Where, T 后 represents the temperature of the battery cell after heat transfer occurs; I represents the current of the battery cell; R represents the resistance of the battery cell; h is the heat dissipation coefficient; T 前 is the battery cell temperature before heat transfer occurs; T 0 is the ambient temperature; A is the heat dissipation area; m is the mass of the battery cell; C m is the unit specific heat capacity.

[0019] In one embodiment, the aging model in the thermal-electrical-aging coupling model includes a capacity decay model and an internal resistance growth model.

[0020] In one embodiment, the capacity decay model is expressed as follows:

[0021]

[0022] In the formula, B is the capacity attenuation percentage of the battery cell during cycle aging; Q,cyc (I rate ) is the coefficient affected by the charge and discharge rate; Ea Q,cycis the activation energy of the battery cell during the cycle aging process; R is the gas constant; T is the battery cell temperature; Z Q,cyc Is a dimensionless constant; Ah is the ampere-hour of charge and discharge.

[0023] In one embodiment, a capacity fade loss rate is introduced into the capacity fade model.

[0024] In one embodiment, the capacity attenuation loss rate is expressed as:

[0025]

[0026] In the formula, B is the capacity attenuation percentage of the battery cell during cycle aging; Q,cyc (I rate ) is the coefficient affected by the charge and discharge rate; Ea Q,cyc is the activation energy of the battery cell during the cycle aging process; R is the gas constant; T is the battery cell temperature; Z Q,cyc is a dimensionless constant; Ah is the ampere-hour of charge and discharge; Q is the rated capacity, I rate is the charge and discharge rate.

[0027] In one embodiment, the internal resistance growth model is expressed as:

[0028]

[0029] In the formula, B is the percentage of internal resistance growth of the battery cell during the cycle aging process; R,cyc (I rate ) is the coefficient affected by the charge and discharge rate, Ea R,cyc is the activation energy of the battery cell during the cycle aging process, R is the gas constant; T is the battery cell temperature; Ah is the charge and discharge ampere-hours.

[0030] In one embodiment, the internal resistance growth rate is introduced into the internal resistance growth model.

[0031] In one embodiment, the internal resistance growth rate is expressed as:

[0032]

[0033] In the formula, B is the percentage of internal resistance growth of the battery cell during the cycle aging process; R,cyc (I rate ) is the coefficient affected by the charge and discharge rate, Ea R,cyc is the activation energy of the battery cell during the cycle aging process, R is the gas constant; T is the battery cell temperature; Q is the rated capacity, I rate is the charge and discharge rate.

[0034] An embodiment of the present invention further provides a modeling device for a battery pack simulation platform, the device comprising:

[0035] The first module is used to determine the battery type and the number of battery cells according to the requirements of the operating environment for the total capacity, total voltage and total power;

[0036] The second module is used to obtain the equivalent circuit model parameters of each battery cell through a battery parameter identification experiment;

[0037] The third module is used to determine the thermal model parameters and aging model parameters of each battery cell according to the battery type and the operating environment;

[0038] The fourth module is used to obtain a thermal-electrical-aging coupling model based on the equivalent circuit model parameters, the thermal model parameters and the aging model parameters.

[0039] An embodiment of the present invention further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements any of the above methods when executing the computer program.

[0040] An embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, any of the above methods is implemented.

[0041] An embodiment of the present invention further provides a computer program product, wherein the computer program product includes a computer program, and when the computer program is executed by a processor, any of the above methods is implemented.

[0042] The embodiment of the present invention provides a modeling method and device for a battery pack simulation platform, wherein the method includes: determining the battery type and the number of battery cells according to the requirements of the operating environment for the battery pack; obtaining the equivalent circuit model parameters of each battery cell through a battery parameter identification experiment; determining the thermal model parameters and aging model parameters of each battery cell according to the battery type and the operating environment; and obtaining a thermal-electric-aging coupling model based on the equivalent circuit model parameters, the thermal model parameters and the aging model parameters of each battery cell. The present invention can greatly reduce the test cost, and can also simulate the complex thermo-electrochemical reactions of battery cells; it can also intuitively reflect the evolution process of aging inconsistency of the battery pack by assigning inconsistent parameters to the thermal model and aging model of the battery cells. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0044] Figure 1 is a flow chart of a modeling method of a battery pack simulation platform in an embodiment of the present invention;

[0045] Figure 2 A schematic diagram of an aging training process of a battery pack simulation platform in an embodiment of the present invention;

[0046] Figure 3 A schematic diagram of the aging of parameters of each battery cell after 100 cycles of aging training of a battery pack simulation platform in an embodiment of the present invention;

[0047] Figure 4 A schematic diagram of the aging of parameters of each battery cell after 600 cycles of aging training of a battery pack simulation platform in an embodiment of the present invention;

[0048] Figure 5 A schematic diagram of the aging of parameters of each battery cell after 1300 cycles of aging training of a battery pack simulation platform in an embodiment of the present invention;

[0049] Figure 6 A schematic diagram of the structure of a modeling device for a battery pack simulation platform provided by an embodiment of the present invention;

[0050] Figure 7 A schematic diagram of the physical structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0051] To make the purpose, technical solution and advantages of the embodiments of the present invention more clear, the embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings. Here, the exemplary embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0052] In order to facilitate understanding of the technical solution provided by this application, the research background of the technical solution of this application is briefly described below.

[0053] At present, there are few modeling methods for battery pack simulation platforms, and existing inventions are only aimed at modeling battery pack simulation platforms under a certain aging state, and there is no modeling method for battery pack simulation platforms under the full life cycle. The present invention is a modeling method for battery pack simulation platform under the full life cycle, which can simulate the aging trend of battery packs under the full life cycle, and provide a simulation data set for the research of battery pack full life state estimation method and battery pack inconsistency degradation trend research.

[0054] Specifically, Figure 1 As shown, an embodiment of the present invention provides a modeling method for a battery pack simulation platform, the method comprising:

[0055] S101, determining the battery type and the number of battery cells according to the requirements of the operating environment for the battery pack;

[0056] S102, obtaining equivalent circuit model parameters of each battery cell through a battery parameter identification experiment;

[0057] S103, determining thermal model parameters and aging model parameters of each battery cell according to the battery type and operating environment;

[0058] S104 , obtaining a thermal-electrical-aging coupling model based on the equivalent circuit model parameters, the thermal model parameters, and the aging model parameters of each battery cell.

[0059] Specifically, the present invention first determines the type of battery and the number of battery cells according to the requirements of the operating environment for parameters such as the total voltage, total capacity and total power of the battery pack. Among them, the type of battery can be any one of lithium batteries such as lithium iron phosphate and lithium titanate, or any other available battery, which are not listed here one by one. The battery cell of the present invention refers to the same batch of battery cells produced by the same material ratio, the same production process and production process. The method of the present invention uses a number of battery cells of the same type to form a battery pack simulation platform, that is, a battery parameter identification experiment is performed on a number of battery cells of the same type to obtain the equivalent circuit model parameters of each battery cell, and then the thermal model parameters and aging model parameters of each battery cell are determined according to the battery type and the operating environment. Based on the equivalent circuit model parameters, the thermal model parameters and the aging model parameters of each battery cell, a thermal-electric-aging coupling model is obtained, that is, each battery cell model is constructed by an equivalent circuit model, a thermal model and an aging model. The present invention can simulate the aging trend of the battery pack under the full life cycle, and provide a simulation data set for the study of the full life state estimation method of the battery pack and the study of the inconsistency degradation trend of the battery pack.

[0060] The present invention can greatly reduce the test cost and simulate the complex thermo-electrochemical reactions of battery cells. It can also intuitively reflect the evolution process of aging inconsistency of the battery pack by assigning inconsistent parameters to the thermal model and aging model of the battery cells, thereby enhancing the authenticity of the battery pack simulation platform.

[0061] In one embodiment, the battery pack is applied to an electrical system, such as a train traction system or a vehicle transmission system. First, the type of battery to be used and the number of battery cells required are determined based on the requirements of the train traction system or the vehicle transmission system for parameters such as the total voltage, total capacity, and total power of the battery pack. For example, based on the requirements of the train traction system or the vehicle transmission system for the total capacity, total voltage, and total power of the battery pack, it is determined that the battery pack needs to include N battery cell models, and P battery cells are connected in parallel to form a single battery pack, and S battery packs are connected in series to form a battery pack simulation platform, where P×S=N.

[0062] In one embodiment, the battery cell parameters used are: upper cut-off voltage is 4.2V, lower cut-off voltage is 2.5V, and rated capacity is 30Ah. The battery pack simulation platform is composed of 24 battery cells connected in series, and the initial ambient temperature is set to 25°C.

[0063] In one embodiment, the equivalent circuit model parameters include ohmic internal resistance, polarization internal resistance, polarization capacitance, and maximum available capacity.

[0064] Specifically, the equivalent circuit model is constructed using an internal resistance model, a Thevenin model or a dual polarization model, and the model parameters are obtained by testing the battery.

[0065] The internal resistance equivalent circuit model is the simplest equivalent circuit model, which consists of a nonlinear voltage source and a resistor in series. The voltage source and the resistor change continuously with the battery state. The parameters are easy to identify, but the accuracy is not high, and it cannot reflect the dynamic characteristics of the battery during battery polarization and polarization elimination.

[0066] The Thevenin model, also known as the first-order RC model, includes a nonlinear voltage source and an RC link. The nonlinear voltage source is used to describe the steady-state open-circuit voltage UOC of the battery, and the ohmic internal resistance R0 and the parallel link of the resistor Rp and the capacitor Cp are used to describe the transient response. Compared with the internal resistance model, the first-order RC model can simulate the dynamic process of the battery.

[0067] The dual-polarization model includes a nonlinear voltage source and two RC links. Similar to the first-order RC model, the nonlinear voltage source is used to describe the steady-state open-circuit voltage UOC of the battery, and the ohmic internal resistance R0 and the parallel links of the resistors Rp, Rs and the capacitors Cp, Cs are used to describe the transient response. The dual-polarization model can better simulate the dynamic process of the battery through two RC loops, taking into account the transient and steady-state characteristics of the battery.

[0068] Taking the Thevenin model as an example, the Thevenin model is composed of a voltage source, an ohmic internal resistance and a first-order RC network in series. The battery model can be expressed as:

[0069]

[0070] Where U OCV (SOC) represents the current open circuit voltage value; i t Indicates the battery current; U t Represents the terminal voltage of the battery; R o is the ohmic internal resistance of the battery; R p is the polarization internal resistance of the battery; C p is the polarization capacitance of the battery; U P Represents the polarization voltage.

[0071] In one embodiment, the equivalent circuit model parameters of each battery cell obtained through the battery parameter identification experiment specifically include:

[0072] In the battery parameter identification experiment, normal distribution statistics are used to obtain the equivalent circuit model parameters of each battery cell.

[0073] In one embodiment, in the battery parameter identification experiment, obtaining the equivalent circuit model parameters of each battery cell using normal distribution statistics specifically includes:

[0074] In the battery parameter identification experiment, the mean and standard deviation of the first equivalent circuit model parameters of each battery cell at different temperatures are calculated, and a corresponding normal distribution is generated based on the mean and the standard deviation. The second equivalent circuit model parameters of each battery cell are obtained based on the normal distribution statistics.

[0075] Specifically, parameter identification is performed on 5 battery cells at 5°C, 10°C, 15°C, 20°C, 25°C, 30°C, 35°C and 40°C, respectively, to obtain the means and standard deviations of the first equivalent circuit model parameters of the 5 battery cells, and a corresponding normal distribution is generated based on the means and the standard deviation, and the second equivalent circuit model parameters of each battery cell are obtained based on the normal distribution statistics.

[0076] In one embodiment, the parameters at different temperatures are fitted using a third-order polynomial, and the relationship between each parameter and temperature can be obtained as follows:

[0077] R o (T) = 2.889·10 -7 T 3 -2.014·10 -5 T 2 +0.0003273T+0.01106;

[0078] R d (T) = 2.404·10 -7 T 3 -1.039·10 -5 T 2 -2.941·10 -5 T+0.006257;

[0079] C d (T) = 17.93T 3 -1006T 2 +1.685 10 4 T-1.01·10 4 ;

[0080] C max (T) = 5.455 10 -6 T 3 -7.277 10 -4 T 2 +0.04089T+28.61;

[0081] Among them, R 0 , R d , C d , C max They are ohmic internal resistance, polarization internal resistance, polarization capacitance, and maximum available capacity.

[0082] In one embodiment, the distribution of various parameters at 25° C. obtained through simulation experiments is shown in Table 1.

[0083] Table 1

[0084] Monomer inconsistency parameters average value Standard Deviation Monomer initial capacity SOC 0.9 0.04 Monomer ohmic internal resistance Ro / mΩ 11.4 1 Monomer polarization internal resistance Rd / mΩ 3.1 1 Monomer polarization capacitance Cd / KF 83.186 10 Maximum available capacity of a single unit Cmax / Ah 29 0.5

[0085] In one embodiment, the thermal model in the thermal-electrical-aging coupling model adopts a heat source transfer model.

[0086] In one embodiment, the heat source transfer model is expressed as follows:

[0087] T 后 =∫((I 2 Rh(T 前 -T 0 )A) / mC m )dt+T 0 ;

[0088] Where, T 后 represents the temperature of the battery cell after heat transfer occurs; I represents the current of the battery cell; R represents the resistance of the battery cell; h is the heat dissipation coefficient; T 前 is the battery cell temperature before heat transfer occurs; T0 is the ambient temperature; A is the heat dissipation area; m is the mass of the battery cell; C m is the unit specific heat capacity.

[0089] It should be noted that for the battery pack simulation platform, the different types of batteries studied and the different heat dissipation environments will lead to different parameters of the thermal model. Therefore, the thermal model parameters of all cells in the battery pack simulation platform need to be determined according to the specific situation.

[0090] In one embodiment, the heat dissipation area in the battery thermal model is set to 0.022, the unit specific heat capacity is set to 900, and the battery cell mass is set to 0.45. Regarding the setting of the heat dissipation coefficient, a 1x24 heat dissipation coefficient matrix with a mean of 20 and a standard deviation of 2 can be generated through simulation experiments.

[0091] In one embodiment, the aging model in the thermal-electrical-aging coupling model includes a capacity decay model and an internal resistance growth model.

[0092] In one embodiment, the capacity decay model is expressed as follows:

[0093]

[0094] In the formula, B is the capacity attenuation percentage of the battery cell during cycle aging; Q,cyc (I rate ) is the coefficient affected by the charge and discharge rate; Ea R,cyc is the activation energy of the battery cell during the cycle aging process; R is the gas constant; T is the battery cell temperature; Z Q,cyc Is a dimensionless constant; Ah is the ampere-hour of charge and discharge.

[0095] In one embodiment, a capacity fade loss rate is introduced into the capacity fade model.

[0096] It should be noted that the capacity decay model can only be used under constant current and temperature, but in fact the current and temperature of the battery are constantly changing. Therefore, the present invention assumes that the battery can be approximately considered to be under constant current and temperature conditions in a short period of time, and the capacity loss over a period of time can be formed by superimposing the capacity loss in each time period. Based on this assumption, the present invention introduces the capacity decay loss rate into the capacity decay model.

[0097] In one embodiment, the capacity attenuation loss rate is expressed as:

[0098]

[0099] In the formula, B is the capacity attenuation percentage of the battery cell during cycle aging; Q,cyc (I rate ) is the coefficient affected by the charge and discharge rate; Ea Q,cyc is the activation energy of the battery cell during the cycle aging process; R is the gas constant; T is the battery cell temperature; Z Q,cyc is a dimensionless constant; Ah is the ampere-hour of charge and discharge; Q is the rated capacity; I rate Indicates the charge and discharge rate.

[0100] Specifically, considering the inconsistency of battery cell capacity decay, the present invention introduces the parameter F q To characterize the changing law of capacity attenuation rate of different batteries, if the single cell fully conforms to the above model, then F q =1, for monomer i, its capacity loss rate is:

[0101]

[0102] In the formula, F q It can be given using statistical methods such as normal distribution.

[0103] In one embodiment, the internal resistance growth model is expressed as:

[0104]

[0105] In the formula, B is the percentage of internal resistance growth of the battery cell during the cycle aging process; R,cyc (I rate ) is the coefficient affected by the charge and discharge rate, Ea R,cyc is the activation energy of the battery cell during the cycle aging process, R is the gas constant; T is the battery cell temperature; Ah is the charge and discharge ampere-hours.

[0106] In one embodiment, the internal resistance growth rate is introduced into the internal resistance growth model.

[0107] In one embodiment, the internal resistance growth rate is expressed as:

[0108]

[0109] In the formula, B is the percentage of internal resistance growth of the battery cell during the cycle aging process; R,cyc (I rate ) is the coefficient affected by the charge and discharge rate, Ea R,cyc is the activation energy of the battery cell during the cycle aging process, R is the gas constant; T is the battery cell temperature; Q is the rated capacity; I rate Indicates the charge and discharge rate.

[0110] Specifically, for monomer i, its internal resistance growth rate is:

[0111]

[0112] In the formula, is the percentage increase of the internal resistance of the battery during the cycle aging process; F r The changing rules of capacity attenuation rates of different batteries can be characterized using statistical methods such as normal distribution.

[0113] It should be noted that for the battery pack simulation platform, different types of batteries studied will result in different parameters of the aging model. Therefore, the aging model parameters of all cells in the battery pack simulation platform need to be determined according to specific circumstances.

[0114] In one embodiment, Z in the aging model Q,cyc , Ea R,cyc and Ea Q,cyc is set to 0.48, 51800 and 22406, B Q,cyc and B R,cyc The expression is as follows:

[0115]

[0116] B R,cyc =320532+3634.2·exp(0.9179·(5-I rate ));

[0117] In the formula, I rate Indicates the discharge rate.

[0118] Regarding the coefficients of inconsistency of capacity attenuation and internal resistance growth, simulation experiments generated 1x24 inconsistency coefficient matrices with a mean of 1, a standard deviation of 0.1 and a mean of 1, a standard deviation of 0.2 respectively.

[0119] In one embodiment, using Figure 2 The training process shown performs aging training on the constructed battery pack simulation platform. First, the ambient temperature is set to 25°C, and the battery is charged to the charging cut-off condition using a multi-stage constant current (MSCC) charging condition, and then discharged to the discharging cut-off condition using an urban dynamometer driving schedule (UDDS) dynamic condition. At this time, it is determined whether the total number of cycles is greater than a preset number (such as 100, 600, or 1300). If so, the training ends. Otherwise, the battery continues to be charged to the charging cut-off condition using MSCC, and then discharged to the discharging cut-off condition using a dynamic condition, until the total number of cycles is greater than the preset number.

[0120] Specifically, Figure 3 Schematic diagram of the aging of parameters of each battery cell after 100 cycles of aging training of a battery pack simulation platform in an embodiment of the present invention. Figure 4 Schematic diagram of the aging of battery cell parameters after 600 cycles of aging training on a battery pack simulation platform in an embodiment of the present invention. Figure 5 The figure is a schematic diagram of the aging of each battery cell parameter after 1300 cycles of aging training of a battery pack simulation platform in an embodiment of the present invention. Each cycle consists of a charging condition and a discharging condition. The charging condition adopts MSCC; and for the discharging condition, the UDDS dynamic condition is adopted as the discharging condition. It should be noted that the charging cut-off condition in the figure is that any cell in the battery pack simulation platform reaches the upper cut-off voltage; the discharging cut-off condition is that any cell in the battery pack simulation platform reaches the lower cut-off voltage.

[0121] The present invention first solves the number of series and parallel connections of the battery pack according to the system's requirements for total voltage, total power and total capacity; then obtains the circuit model parameters of the battery cell by performing parameter identification experiments on the battery cell at different temperatures, and determines the parameters of the battery cell thermal model and aging model according to the type of battery and the operating environment, thereby obtaining a battery pack simulation platform; finally, a cycle aging test is performed on the battery pack simulation platform, which can provide data for research such as battery pack modeling and full life cycle state estimation.

[0122] Compared with the prior art, the present invention selects several battery cells to identify battery parameters at different temperatures, and performs normal distribution statistics on the battery parameters. The normal distribution statistical results can generate parameter data of several battery cells, and then form a battery pack simulation platform, which greatly reduces the test cost. In addition, the thermal model and aging model are introduced into the battery cell model to construct a thermal-electric-aging coupling model, which can simulate the complex thermo-electrochemical reactions of the battery cell and simulate the thermo-electric coupling process of the battery cell over its entire life cycle. By assigning inconsistent parameters to the thermal model and aging model of the battery cell, the inconsistency evolution process of the battery pack is reflected, thereby enhancing the authenticity of the battery pack simulation platform.

[0123] The present invention also provides a modeling device for a battery pack simulation platform, as described in the following embodiments. Since the principle of solving the problem by the device is similar to that of the above method, the implementation of the device can refer to the implementation of the above method, and the repeated parts will not be repeated.

[0124] Based on the same inventive idea, Figure 6 As shown, an embodiment of the present invention further provides a modeling device for a battery pack simulation platform, the device comprising:

[0125] The first module 201 is used to determine the battery type and the number of battery cells according to the requirements of the operating environment for the total capacity, total voltage and total power;

[0126] The second module 202 is used to obtain the equivalent circuit model parameters of each battery cell through a battery parameter identification experiment;

[0127] The third module 203 is used to determine the thermal model parameters and aging model parameters of each battery cell according to the battery type and the operating environment.

[0128] The fourth module 204 is used to obtain a thermal-electrical-aging coupling model based on the equivalent circuit model parameters, the thermal model parameters and the aging model parameters.

[0129] In one embodiment, the equivalent circuit model parameters in the second module 202 include ohmic internal resistance, polarization internal resistance, polarization capacitance, and maximum available capacity.

[0130] In one embodiment, the second module 202 obtains the equivalent circuit model parameters of each battery cell through a battery parameter identification experiment, specifically including:

[0131] In the battery parameter identification experiment, normal distribution statistics are used to obtain the equivalent circuit model parameters of each battery cell.

[0132] In one embodiment, the second module 202 uses normal distribution statistics to obtain the equivalent circuit model parameters of each battery cell in the battery parameter identification experiment, specifically including:

[0133] In the battery parameter identification experiment, the mean and standard deviation of the first equivalent circuit model parameters of each battery cell at different temperatures are calculated, and a corresponding normal distribution is generated based on the mean and the standard deviation. The second equivalent circuit model parameters of each battery cell are obtained based on the normal distribution statistics.

[0134] In one embodiment, the thermal model in the thermal-electrical-aging coupling model constructed by the fourth module 204 adopts a heat source transfer model.

[0135] In one embodiment, the heat source transfer model is expressed as follows:

[0136] T 后 =∫((I 2 Rh(T 前 -T 0 )A) / mC m )dt+T 0 ;

[0137] Where, T 后represents the temperature of the battery cell after heat transfer occurs; I represents the current of the battery cell; R represents the resistance of the battery cell; h is the heat dissipation coefficient; T 前 is the battery cell temperature before heat transfer occurs; T 0 is the ambient temperature; A is the heat dissipation area; m is the mass of the battery cell; C m is the unit specific heat capacity.

[0138] In one embodiment, the aging model in the thermal-electrical-aging coupling model constructed by the fourth module 204 includes a capacity decay model and an internal resistance growth model.

[0139] In one embodiment, the capacity decay model is expressed as follows:

[0140]

[0141] In the formula, B is the capacity attenuation percentage of the battery cell during cycle aging; Q,cyc (I rate ) is the coefficient affected by the charge and discharge rate; Ea Q,cyc is the activation energy of the battery cell during the cycle aging process; R is the gas constant; T is the battery cell temperature; Z Q,cyc Is a dimensionless constant; Ah is the ampere-hour of charge and discharge.

[0142] In one embodiment, a capacity fade loss rate is introduced into the capacity fade model.

[0143] In one embodiment, the capacity attenuation loss rate is expressed as:

[0144]

[0145] In the formula, B is the capacity attenuation percentage of the battery cell during cycle aging; Q,cyc (I rate ) is the coefficient affected by the charge and discharge rate; Ea Q,cyc is the activation energy of the battery cell during the cycle aging process; R is the gas constant; T is the battery cell temperature; Z Q,cyc is a dimensionless constant; Ah is the ampere-hour of charge and discharge; Q is the rated capacity; I rate is the charge and discharge rate.

[0146] In one embodiment, the internal resistance growth model is expressed as:

[0147]

[0148] In the formula, B is the percentage of internal resistance growth of the battery cell during the cycle aging process; R,cyc(I rate ) is the coefficient affected by the charge and discharge rate, Ea R,cyc is the activation energy of the battery cell during the cycle aging process, R is the gas constant; T is the battery cell temperature; Ah is the charge and discharge ampere-hours.

[0149] In one embodiment, the internal resistance growth rate is introduced into the internal resistance growth model.

[0150] In one embodiment, the internal resistance growth rate is expressed as:

[0151]

[0152] In the formula, B is the percentage of internal resistance growth of the battery cell during the cycle aging process; R,cyc (I rate ) is the coefficient affected by the charge and discharge rate, Ea R,cyc is the activation energy of the battery cell during the cycle aging process, R is the gas constant; T is the battery cell temperature; Q is the rated capacity; I rate is the charge and discharge rate.

[0153] Other device embodiments can be implemented in one-to-one correspondence with the aforementioned method embodiments, and will not be described in detail here.

[0154] An embodiment of the present invention further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements any of the above methods when executing the computer program.

[0155] An embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, any of the above methods is implemented.

[0156] An embodiment of the present invention further provides a computer program product, wherein the computer program product includes a computer program, and when the computer program is executed by a processor, any of the above methods is implemented.

[0157] Figure 7 A schematic diagram of the physical structure of an electronic device provided in an embodiment of the present invention, such as Figure 7 As shown, the electronic device includes: a processor (processor) 301, a memory (memory) 302 and a bus 303.

[0158] The processor 301 and the memory 302 communicate with each other via the bus 303 .

[0159] The processor 301 is used to call the program instructions in the memory 302 to execute the methods provided by the above method embodiments.

[0160] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0161] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0162] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0163] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0164] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A modeling method for a battery pack simulation platform, characterized in that: include: Determine the battery type and number of battery cells based on the operating environment's requirements for the battery pack; The equivalent circuit model parameters of each battery cell are obtained through battery parameter identification experiments; Determine the thermal model parameters and aging model parameters of each battery cell according to the battery type and operating environment; A thermal-electrical-aging coupling model is obtained based on the equivalent circuit model parameters, the thermal model parameters and the aging model parameters of each battery cell.

2. The method according to claim 1, characterized in that: The equivalent circuit model parameters include ohmic internal resistance, polarization internal resistance, polarization capacitance, and maximum available capacity.

3. The method according to claim 1 or 2, characterized in that: The equivalent circuit model parameters of each battery cell obtained through the battery parameter identification experiment specifically include: In the battery parameter identification experiment, normal distribution statistics are used to obtain the equivalent circuit model parameters of each battery cell.

4. The method according to claim 3, characterized in that: In the battery parameter identification experiment, the equivalent circuit model parameters of each battery cell obtained by using normal distribution statistics specifically include: In the battery parameter identification experiment, the mean and standard deviation of the first equivalent circuit model parameters of each battery cell at different temperatures are calculated, and a corresponding normal distribution is generated based on the mean and the standard deviation. The second equivalent circuit model parameters of each battery cell are obtained based on the normal distribution statistics.

5. The method according to claim 1, characterized in that: The thermal model in the thermal-electrical-aging coupling model adopts a heat source transfer model.

6. The method according to claim 5, characterized in that The heat source transfer model is expressed as follows: T 后 =∫((I 2 R-h(T 前 -T0)A) / mC m )dt+T0; Where, T 后 represents the temperature of the battery cell after heat transfer occurs; I represents the current of the battery cell; R represents the resistance of the battery cell; h is the heat dissipation coefficient; T 前 is the battery cell temperature before heat transfer occurs; T0 is the ambient temperature; A is the heat dissipation area; m is the mass of the battery cell; C m is the unit specific heat capacity.

7. The method according to claim 1, characterized in that: The aging model in the thermal-electrical-aging coupling model includes a capacity attenuation model and an internal resistance growth model.

8. The method according to claim 7, characterized in that: The capacity decay model is expressed as follows: In the formula, B is the capacity attenuation percentage of the battery cell during cycle aging; Q,cyc (I rate ) is the coefficient affected by the charge and discharge rate; Ea Q,cyc is the activation energy of the battery cell during the cycle aging process; R is the gas constant; T is the battery cell temperature; Z Q,cyc Is a dimensionless constant; Ah is the ampere-hour of charge and discharge.

9. The method according to claim 8, characterized in that The capacity fade loss rate is introduced into the capacity fade model.

10. The method according to claim 9, characterized in that The capacity attenuation loss rate is expressed as: In the formula, B is the capacity attenuation percentage of the battery cell during cycle aging; Q,cyc (I rate ) is the coefficient affected by the charge and discharge rate; Ea Q,cyc is the activation energy of the battery cell during the cycle aging process; R is the gas constant; T is the battery cell temperature; Z Q,cyc is a dimensionless constant; Ah is the ampere-hour of charge and discharge; Q is the rated capacity; I rate is the charge and discharge rate.

11. The method according to claim 7, characterized in that: The internal resistance growth model is expressed as: In the formula, B is the percentage of internal resistance growth of the battery cell during the cycle aging process; R,cyc (I rate ) is the coefficient affected by the charge and discharge rate, Ea R,cyc is the activation energy of the battery cell during the cycle aging process, R is the gas constant; T is the battery cell temperature; Ah is the charge and discharge ampere-hours.

12. The method according to claim 11, characterized in that: The internal resistance growth rate is introduced into the internal resistance growth model.

13. The method according to claim 12, characterized in that: The internal resistance growth rate is expressed as: In the formula, B is the percentage of internal resistance growth of the battery cell during the cycle aging process; R,cyc (I rate ) is the coefficient affected by the charge and discharge rate, Ea R,cyc is the activation energy of the battery cell during the cycle aging process, R is the gas constant; T is the battery cell temperature; Q is the rated capacity; I rate is the charge and discharge rate.

14. A modeling device for a battery pack simulation platform, characterized in that: include: The first module is used to determine the battery type and the number of battery cells according to the requirements of the operating environment for the total capacity, total voltage and total power; The second module is used to obtain the equivalent circuit model parameters of each battery cell through a battery parameter identification experiment; The third module is used to determine the thermal model parameters and aging model parameters of each battery cell according to the battery type and the operating environment; The fourth module is used to obtain a thermal-electrical-aging coupling model based on the equivalent circuit model parameters, the thermal model parameters and the aging model parameters.

15. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 13 is implemented.

16. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 13 is implemented.

17. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 13 is implemented.

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