Lithium-ion battery energy storage system modeling parameter acquisition method, device and electronic equipment

Abstract

The present invention provides a method, device and electronic device for obtaining modeling parameters of a lithium-ion battery energy storage system, wherein the method includes: obtaining the pulse voltage response of the lithium-ion battery energy storage system under different charge states; obtaining the initial value of the constructed equivalent circuit parameter according to the pulse voltage response under each charge state; obtaining the operating voltage response curve of the lithium-ion battery energy storage system; dividing the operating voltage response curve into segments with each charge state as a dividing point; assigning the corresponding initial value to the equivalent circuit for each actual use condition, using the operating voltage response curve of the actual use condition as a target curve for parameter identification and optimization, and obtaining the equivalent circuit parameters that meet the actual use condition. The present invention can save test resources, only requires less measured data to complete the parameter acquisition of the actual use condition, reduce development costs, and also improve the simulation accuracy of the energy storage system, and obtain equivalent circuit parameters that match different actual use conditions.

Description

Technical Field

[0001] The present invention mainly relates to the technical field of lithium-ion battery simulation models, and in particular to a method, device and electronic equipment for obtaining modeling parameters of a lithium-ion battery energy storage system. Background Art

[0002] The equivalent circuit model is a commonly used modeling method for characterizing the external characteristics of lithium-ion batteries. It takes into account both modeling accuracy and model complexity. Based on the actual system topology, by constructing an equivalent circuit simulation model of the energy storage system, it can be well applied to system performance simulation. An equivalent circuit simulation model is established for the energy storage battery system, which can be used in scenarios such as fault simulation analysis, formulation of battery cell incoming material consistency parameter standards, and system topology optimization. The performance indicators of the energy storage system under different operating conditions can be easily simulated by adjusting the simulation model parameters and the operating environment, with high reliability and no safety risks. The use of simulation methods can effectively reduce development costs and speed up R&D.

[0003] The equivalent circuit modeling method is relatively mature and widely used. It is also widely used in the SOC (State of Charge) estimation of vehicle power systems, but there are few reports on its use in energy storage system modeling and simulation. The core issue in building an equivalent circuit model is how to obtain accurate battery equivalent resistance R and capacitance C parameters. The accuracy of the parameters directly determines the accuracy of the simulation model. However, since lithium-ion batteries are complex electrochemical systems, their internal equivalent resistance and capacitance characteristics are easily affected by actual operating conditions and the state of charge. How to obtain parameters with high accuracy and strong applicability is a key issue in the current modeling method development stage.

[0004] Currently, equivalent circuit parameters are mostly obtained by performing HPPC (Hybrid Pulse Power Characteristic) tests on battery cells to obtain the voltage response curve of the sample under pulse excitation. The measured voltage response curve is then fitted with the output voltage of the equivalent model, with the minimum error as the optimization goal, and the estimation of the resistance R and capacitance C parameters is achieved through machine algorithms. The shortcomings of this method are: 1. The standard hybrid power pulse characteristic test scheme can obtain the zero input and zero state response of the battery cell under the set specified state of charge breakpoint, and obtain the equivalent parameters at the state of charge point by fitting the response curve. However, the actual system is a continuous signal in which the excitation signal (current / power) changes with time, and the standard hybrid power pulse characteristic operating conditions have limitations and it is difficult to cover all actual operating conditions, resulting in limited parameter applicability and accuracy; 2. The equivalent R and C parameters are variables that change with the state of charge. The setting method of the state of charge breakpoint during the hybrid power pulse characteristic test determines the range of the parameter lookup table. More dense state of charge breakpoints can improve accuracy but the amount of calculation is too large, and fewer state of charge point settings can easily cause the fitting process to not converge when optimizing parameters. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a method, device and electronic device for obtaining modeling parameters of a lithium-ion battery energy storage system, so as to save testing resources, obtain equivalent circuit parameters of actual use conditions with only less measured data, and reduce development costs.

[0006] To solve the above technical problems, in a first aspect, the present invention provides a method for acquiring modeling parameters of a lithium-ion battery energy storage system, comprising: acquiring a pulse voltage response of the lithium-ion battery energy storage system under different states of charge, wherein the pulse voltage response is obtained by pulse charging and discharging the lithium-ion battery energy storage system; obtaining initial values ​​of constructed equivalent circuit parameters according to the pulse voltage response under each state of charge; acquiring an operating voltage response curve of the lithium-ion battery energy storage system, wherein the operating voltage response curve is a functional relationship between a dynamic voltage and a state of charge during actual use conditions; dividing the operating voltage response curve into segments with each state of charge as a dividing point; for each actual use condition, assigning a corresponding initial value to the equivalent circuit, and performing parameter identification and optimization with the operating voltage response curve of the actual use condition as a target curve to obtain equivalent circuit parameters that meet the actual use conditions.

[0007] Optionally, the pulse voltage response is obtained by a hybrid power pulse characteristic test, and the state of charge interval at the beginning and end of the hybrid power pulse characteristic test is greater than the state of charge interval in the middle.

[0008] Optionally, the method further includes: during the hybrid power pulse characteristic test, charging and discharging the lithium-ion battery energy storage system in each charge state, and then performing pulse charging and discharging after standing for a period of time.

[0009] Optionally, the equivalent circuit is an n-order RC model equivalent circuit, where n≥2.

[0010] Optionally, the equivalent circuit parameters include an equivalent ohmic resistance R0, and the equivalent ohmic resistance R0 is calculated by the ratio of the voltage ΔU to the current I before and after the static-charging moment.

[0011] Optionally, the equivalent circuit parameters also include: resistors R1 to R n And capacitors C1~C n , through the equivalent function expression Find the resistance R1~R n And capacitors C1~C n The initial value of; where τ1 = R1C1, τ2 = R2C2, ..., τ n =R n C n , a==U OCV -U R0 , and the parameters a1, a2, ..., a n are constant values, U OCV is the equivalent power supply voltage, U R0 is the voltage across the equivalent ohmic resistance R0.

[0012] Optionally, using the operating voltage response curve of the actual operating condition as the target curve for parameter identification and optimization to obtain equivalent circuit parameters that meet the actual operating condition also includes: using the operating voltage response curve as the target curve for automatic parameter identification, and obtaining optimized equivalent circuit parameters according to set convergence conditions.

[0013] Optionally, the convergence condition is that the difference between the optimized simulation value and the corresponding value on the operating voltage response curve is less than or equal to a set threshold, or the number of optimization times reaches a set maximum number of iterations.

[0014] In a second aspect, the present invention provides a device for acquiring modeling parameters of a lithium-ion battery energy storage system, comprising: a first acquisition module, used to acquire a pulse voltage response of the lithium-ion battery energy storage system under different states of charge, wherein the pulse voltage response is obtained by pulse charging and discharging the lithium-ion battery energy storage system; an acquisition module, used to acquire the initial value of the constructed equivalent circuit parameter according to the pulse voltage response under each state of charge; a second acquisition module, used to acquire an operating voltage response curve of the lithium-ion battery energy storage system, wherein the operating voltage response curve is a functional relationship between the dynamic voltage and the state of charge during actual use; a segmentation module, used to segment the operating voltage response curve with each state of charge as a dividing point; an optimization module, used to assign the corresponding initial value to the equivalent circuit for each actual use condition, and use the operating voltage response curve of the actual use condition as the target curve for parameter identification and optimization to obtain equivalent circuit parameters that meet the actual use condition.

[0015] In a third aspect, the present invention provides an electronic device comprising: a processor and a memory, wherein the memory stores programs or instructions that can be run on the processor, and when the programs or instructions are executed by the processor, the steps of the method for obtaining modeling parameters of a lithium-ion battery energy storage system as described in the first aspect are implemented.

[0016] In a fourth aspect, the present invention provides a readable storage medium storing a program or instruction, which, when executed by a processor, implements the steps of the method for acquiring modeling parameters of a lithium-ion battery energy storage system as described in the first aspect.

[0017] Compared with the prior art, the present invention has the following advantages: first, the pulse voltage response of the lithium-ion battery energy storage system under different charge states is obtained; secondly, under each charge state, the initial value of the constructed equivalent circuit parameter is obtained according to the pulse voltage response; then the operating voltage response curve of the lithium-ion battery energy storage system is obtained; and the operating voltage response curve is segmented with each charge state as a dividing point; finally, for each actual use condition, the corresponding initial value is assigned to the equivalent circuit, and the operating voltage response curve of the actual use condition is used as the target curve for parameter identification and optimization, so as to obtain the equivalent circuit parameters that meet the actual use condition, thereby saving test resources, requiring only less measured data to complete the parameter acquisition of the actual use condition, reducing the development cost, and also improving the simulation accuracy of the energy storage system, obtaining the equivalent circuit parameters matching different actual use conditions, and having better adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings are included to provide a further understanding of the present application. They are included and constitute a part of the present application. The accompanying drawings illustrate embodiments of the present application and together with the present specification serve to explain the principles of the present application. In the accompanying drawings:

[0019] Figure 1 It is a flow chart of a method for obtaining modeling parameters of a lithium-ion battery energy storage system according to an embodiment of the present invention;

[0020] Figure 2 It is a flow chart of a method for obtaining modeling parameters of a lithium-ion battery energy storage system according to another embodiment of the present invention;

[0021] Figure 3 It is a schematic diagram of the voltage characteristic curve of the LFP lithium-ion battery in the present invention;

[0022] Figure 4 This is a schematic diagram of an equivalent circuit model of an n-order RC model of a lithium-ion battery in the present invention;

[0023] Figure 5 It is a curve schematic diagram of the pulse voltage response in the present invention;

[0024] Figure 6 It is a schematic diagram of an equivalent circuit model of a second-order RC model of a lithium-ion battery in the present invention;

[0025] Figure 7 It is a schematic diagram of the fitting of the charging condition voltage of the second-order RC model equivalent circuit in one embodiment of the present invention;

[0026] Figure 8 It is a schematic diagram of the fitting of the charging condition temperature of the second-order RC model equivalent circuit in one embodiment of the present invention;

[0027] Fig. 9 It is a schematic diagram of the fitting of the discharge voltage of the equivalent circuit of the second-order RC model in one embodiment of the present invention;

[0028] Fig.10 It is a schematic diagram of the fitting of the discharge working condition temperature of the second-order RC model equivalent circuit in one embodiment of the present invention;

[0029] Fig.11 It is a schematic diagram of the fitting of the charging condition voltage of the third-order RC model equivalent circuit in one embodiment of the present invention;

[0030] Fig.12 It is a schematic diagram of the fitting of the charging condition temperature of the third-order RC model equivalent circuit in one embodiment of the present invention;

[0031] Fig.13 It is a schematic diagram of the fitting of the discharge working condition voltage of the third-order RC model equivalent circuit in one embodiment of the present invention;

[0032] Fig.14 It is a schematic diagram of the fitting of the temperature of the discharge condition of the equivalent circuit of the third-order RC model in one embodiment of the present invention;

[0033] Fig.15 It is a structural schematic diagram of a device for acquiring modeling parameters of a lithium-ion battery energy storage system according to an embodiment of the present invention;

[0034] Fig.16 FIG. 4 is a schematic diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0035] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for the description of the embodiments. Obviously, the drawings described below are only some examples or embodiments of the present application. For ordinary technicians in this field, the present application can also be applied to other similar scenarios based on these drawings without creative work. Unless it is obvious from the language environment or otherwise explained, the same reference numerals in the figures represent the same structure or operation.

[0036] As shown in this application and claims, unless the context clearly indicates an exception, the words "a", "an", "an" and / or "the" do not refer to the singular and may also include the plural. Generally speaking, the terms "include" and "comprise" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0037] Unless otherwise specifically stated, the relative arrangement, numerical expressions and numerical values ​​of the parts and steps set forth in these embodiments do not limit the scope of the present application. At the same time, it should be understood that, for ease of description, the sizes of the various parts shown in the accompanying drawings are not drawn according to the actual proportional relationship. The technology, method and equipment known to those of ordinary skill in the relevant field may not be discussed in detail, but in appropriate cases, the technology, method and equipment should be considered as a part of the authorization specification. In all examples shown and discussed here, any specific value should be interpreted as being merely exemplary, rather than as a limitation. Therefore, other examples of exemplary embodiments may have different values. It should be noted that similar reference numerals and letters represent similar items in the following drawings, so that once a certain item is defined in an accompanying drawing, it does not need to be further discussed in subsequent drawings.

[0038] Flowcharts are used in the present application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps may be processed in reverse order or simultaneously. At the same time, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0039] Embodiment 1

[0040] Figure 1 is a flow chart of a method for obtaining modeling parameters of a lithium-ion battery energy storage system according to an embodiment of the present invention, with reference to Figure 1 , the method 100 shown includes:

[0041] S110, obtaining a pulse voltage response of the lithium-ion battery energy storage system under different states of charge, wherein the pulse voltage response is obtained by pulse charging and discharging the lithium-ion battery energy storage system.

[0042] In one example, the pulse voltage response is obtained by a hybrid power pulse characteristic test, and the state of charge interval at the beginning and the end of the hybrid power pulse characteristic test is greater than the state of charge interval at the middle stage.

[0043] The hybrid pulse power characteristic test is an important test method in the performance evaluation of power batteries. It is mainly used for performance evaluation of battery systems, modules and battery cells for hybrid vehicles and power system management. The hybrid pulse power characteristic test is used to reflect the pulse charge and discharge performance of power batteries. The basic idea is to use a periodic pulse (large current in a short time) to discharge, charge and stand still the battery, which is equivalent to connecting a variable load to the voltage output terminal. It can achieve constant current charge and discharge conditions, and can also record the terminal voltage change curve over time. It can be understood that only when the current in the circuit changes can the presence of capacitance or inductance be judged by the terminal voltage curve. If a constant current excitation is used, it is impossible to judge whether the circuit contains capacitance when the circuit reaches a steady state.

[0044] In this embodiment, a certain type of lithium iron phosphate (LFP) lithium-ion battery cell_1 for energy storage is taken as an example. Figure 3 As shown, Figure 3 This is a schematic diagram of the voltage characteristic curve of the LFP lithium-ion battery in the present invention. Since the iron lithium has a long voltage plateau period, its voltage changes rapidly with the state of charge at the initial charging stage S1 and the final charging stage S3, while the voltage fluctuation is small in the intermediate state of charge stage S2. Therefore, in order to accurately characterize the accuracy of the hybrid power pulse characteristic test and better apply the test results in the subsequent optimization process, the state of charge interval at the initial and final stages of the hybrid power pulse characteristic test in this embodiment is greater than the state of charge interval in the middle stage.

[0045] In one example, during the hybrid power pulse characteristic test, the lithium-ion battery energy storage system is charged and discharged at each charge state, and then pulse charged and discharged after being left idle for a period of time.

[0046] In this embodiment, according to the platform voltage characteristics of the commonly used iron-lithium platform for energy storage, relatively dense charge state interval points are set at the beginning and end of the test, while the charge state points can be appropriately reduced in the middle of the test (middle platform period), and pulse charge and discharge current excitation is performed to obtain the sample pulse voltage response.

[0047] For example, taking a certain type of lithium iron phosphate lithium-ion battery cell_1 for energy storage as an example, the test sample cell cell_1 is initialized and capped C0, and the charge is adjusted to 0% state of charge at room temperature, and fully thermally balanced (standing for ≥ 2h), and the capacitive load is accurately adjusted based on the capacitive load result. Then, according to the platform characteristics of the cell cell_1, the mixed power pulse characteristic test is divided into the following Figure 3 The three stages shown are the initial charging stage S1: 0-33% state of charge, the charging platform stage (mid-term) S2: 33%-86% state of charge, and the final charging stage S3: 86%-100% state of charge (the ternary system can also be set according to equal state of charge intervals due to its good linearity).

[0048] For the initial S1: 1C (charging rate) constant current charging is performed for every 3.3% state of charge. After standing for 2 hours, 2C pulse charging and discharging are performed for 5 seconds each. The pulses are left to stand for 60 seconds, and the sampling accuracy is set to 0.1s.

[0049] For mid-term S2: every 6.6% state of charge, the charge is adjusted and then left to stand, and then 1C (charging rate) constant current charging is performed. After standing for 2 hours, 2C pulse charging and discharging are performed for 5 seconds each, and the pulses are left to stand for 60 seconds. The sampling accuracy is set to 0.1s.

[0050] For the final stage S3: the charge is adjusted and allowed to stand for every 3.3% state of charge, and 1C (charging rate) constant current charging is performed. After standing for 2 hours, 2C pulse charging and discharging are performed for 5 seconds each, and the pulses are allowed to stand for 60 seconds. The sampling accuracy is set to 0.1s, until 100% state of charge.

[0051] The pulse test data obtained through the above steps covers the characteristics of each charging stage of the test sample, which can reduce the test resource usage while taking into account the accuracy.

[0052] S120. Under each state of charge, obtain initial values ​​of the constructed equivalent circuit parameters according to the pulse voltage response.

[0053] Battery capacity estimation, battery balancing, and battery charge and discharge performance indicators are all important research objects for battery applications in various fields. Their accurate calculation is inseparable from the battery model. Currently, the commonly used battery models include neural network models, thermal models, electrochemical models, and equivalent circuit models, among which the most widely used is the equivalent circuit model. The equivalent circuit model uses a network composed of series and parallel connections of simple components such as voltage sources, capacitors, inductors, and resistors to equate the external characteristics of actual batteries, such as the Rint model, Thevenin model, PNGV model, Universal model, RC model, etc.

[0054] In this embodiment, according to the hybrid power pulse characteristic test, the current and voltage data of the lithium-ion battery can be obtained, and then the parameter values ​​of the components in the equivalent circuit model can be determined through the estimated equivalent circuit model. For example, after obtaining the current and voltage data, the values ​​of the equivalent circuit parameters are obtained using the least squares fitting method.

[0055] In one example, the equivalent circuit is an n-order RC model equivalent circuit, where n≥2, such as Figure 4 As shown. Further, the equivalent circuit parameters include the equivalent ohmic resistance R0, which is calculated by the ratio of the voltage ΔU and the current I before and after the static-charging moment. Further, the equivalent circuit parameters also include: resistances R1 to R n And capacitors C1~C n , through the equivalent function expression Find the resistance R1~R n And capacitors C1~C n The initial value of; where τ1 = R1C1, τ2 = R2C2, ..., τ n =R n C n , a=U OCV -U R0 , and the parameters a1, a2, ..., a n are constant values, U OCV is the equivalent power supply voltage, U R0 is the voltage across the equivalent ohmic resistance R0.

[0056] For example, Figure 6 As shown, Figure 6This is a schematic diagram of a second-order RC model equivalent circuit model of a lithium-ion battery in the present invention, wherein R0 is an equivalent ohmic internal resistance, R1 and C1 are electrochemical polarization resistance and capacitance respectively, R2 and C2 are concentration difference polarization equivalent resistance and capacitance respectively, and u is a dynamic voltage. Through the aforementioned related steps, sample pulse transient voltage response curves corresponding to 24 charge state moments of 0%, ..., 3.3%, ..., 33%, 36.6%, ..., 86%, ..., 100% and a charge state-dynamic voltage look-up table value can be obtained.

[0057] In this embodiment, a corresponding equivalent circuit simulation model can be built on the simulink platform, such as a second-order RC model equivalent circuit model, and its equivalent ohmic resistance R0 can be calculated by the ratio of the voltage ΔU before and after the static-charging moment to the current I, while the pulse process and the static recovery state are caused by the polarization effect. The equivalent function expression is: Where τ1=R1C1,τ2=

[0058] R2C2,a=U OCV -U R0 , and parameters b and c are constant values. Through the curve fitting tool, L1, L2, …, L24 are used as the target values ​​of the simulation model function, and the corresponding values ​​of R1, C1 and R2, C2 can be accurately analyzed.

[0059] By executing the above steps in sequence, the initial value of the parameter at each state of charge can be obtained as θ0 = {R00[L1~L24], R10[L1~L24], R20[L1~L24], C10[L1~L24], C20[L1~L24]}.

[0060] The above parameter determination method has the same applicability in other multi-order equivalent circuit models. Adding RC parallel units can improve the model accuracy, but it will increase the model complexity.

[0061] For example, taking a certain model of 100Ah capacity lithium iron phosphate battery sample as an example, the HPPC test calibration results are obtained according to the same test steps. In order to illustrate the universal applicability of the method of this embodiment, the fitting effects of constant current working conditions and different temperatures are compared. The equivalent circuit model expression of the multi-order equivalent circuit model to be fitted changes with the increase in the number of RC parallel units. Taking the third-order RC as an example, the voltage response expression of the dynamic process of the battery cell described by its model is:

[0062] Among them, τ1=R1C1, τ2=R2C2, τ3=

[0063] R3C3, and then the corresponding values ​​of R1, C1, R2, C2, R3, and C3 can be analyzed.

[0064] S130, obtaining an operating voltage response curve of the lithium-ion battery energy storage system, wherein the operating voltage response curve is a functional relationship between a dynamic voltage and a state of charge during actual operating conditions.

[0065] In this embodiment, after completing the hybrid power pulse characteristic test, actual measurements can be performed based on actual continuous operating conditions, and the operating voltage response curve of the sample can be recorded. The actual continuous operating condition test results provide a basis for accurate modeling.

[0066] S140, dividing the operating condition voltage response curve into segments with each state of charge as a dividing point.

[0067] Exemplarily, in the process of obtaining the pulse voltage response of the lithium-ion battery energy storage system under different states of charge, there are several corresponding states of charge, including 0%, ..., 3.3%, ..., 33%, 36.6%, ..., 86%, ..., 100%, a total of 24 state of charge moments, and these state of charge moments are used as separation points and applied to the obtained operating voltage response curve. Then, the operating voltage response curve can be segmented according to the separation points composed of the state of charge, and the corresponding operating voltage response curves are intercepted as L1, L2, ..., L24, respectively. Of course, the voltage response process under the actual use condition is also obtained (represented by L_char).

[0068] S150, for each actual use condition, assign the corresponding initial value to the equivalent circuit, use the voltage response curve of the actual use condition as the target curve to perform parameter identification and optimization, and obtain equivalent circuit parameters that meet the actual use condition.

[0069] The actual equivalent circuit parameters are not only affected by the state of charge, but also by coupling factors such as rate and temperature. Therefore, there are still systematic errors between the results of pulse excitation identification and the actual working conditions.

[0070] In this embodiment, the initial value θ0 of the equivalent parameter of the segmented fitting is loaded into the simulation model, and the parameters are automatically identified and optimized with L_char (specifically, the voltage response curve of each operating condition in L_char) as the objective function, so as to obtain the equivalent circuit parameters that meet the actual operating conditions.

[0071] Taking the second-order RC model equivalent circuit as an example, it includes loading the initial values ​​of the equivalent parameters of the segmented fitting, namely the ohmic resistance R0, the electrochemical polarization resistance R1 and the capacitance C1, the concentration difference polarization equivalent resistance R2 and the capacitance C2, into the corresponding equivalent circuit simulation model, and automatically identifying the parameters with the operating voltage response curve as the objective function to obtain the optimization results.

[0072] In one example, the operating voltage response curve may be used as a target curve for automatic parameter identification, and optimized equivalent circuit parameters may be obtained according to set convergence conditions.

[0073] Furthermore, in this embodiment, the convergence condition is that the difference between the optimized simulation value and the corresponding value on the operating voltage response curve is less than or equal to a set threshold, or the number of optimization times reaches a set maximum number of iterations.

[0074] Exemplarily, taking the equivalent circuit of the second-order RC model as an example, the convergence condition may be |Lsimulation-L_char|≤threshold value, or when the maximum number of iterations of optimization is ≥Nmax, the identification is completed, and the final optimized parameters are output as follows: θchar={Rchar0[L1~L24], Rchar1[L1~L24], Rchar2[L1~L24], Cchar1[L1~L24], Cchar2[L1~L24]}. Among them, Lsimulation represents the simulation value after optimization, L_char represents the corresponding value on the working voltage response curve, and Nmax represents the maximum number of iterations.

[0075] In the same way, in the third-order RC model equivalent circuit, the operating voltage response curves L1, L2, ..., L24 are obtained according to the same HPPC optimization test method mentioned above, and the HPPC calibration data is segmented in the same way to obtain the pulse voltage responses L1, L2, ..., L24 in each SOC segment and the operating charging continuous voltage response L_char; the corresponding third-order equivalent circuit simulation model is built on the simulink platform, and the calculation method of the equivalent ohmic resistance R0 remains unchanged, and can be calculated by the ratio of the voltage ΔU before and after the static-charging moment to the current I. The pulse process and the static recovery state are caused by the polarization effect. The equivalent expression of the model is as shown in the above formula. Taking the pulse voltage response as the fitting target value, the parameter value θ0 corresponding to each SOC can be obtained accordingly = {R00[L1~L24], R10[L1~L24], R20[L1~L24], R30[L1~L24], C10[L1~L24], C20[L1~L24], C30[L1~L24]}. Then, based on the 0.5C constant current charge and discharge condition, the initial value θ0 of the equivalent parameter of the piecewise fitting is loaded into the simulation model, and the parameter automatic identification is performed with the 0.5C constant current condition response curve L_char as the objective function. When the convergence condition is |Lsimulation-L_char|≤threshold or the maximum number of iterations ≥Nmax, the identification ends, and the output parameters are finally identified as follows: θchar={Rchar0[L1~L24]、Rchar1[L1~L24]、Rchar2[L1~L24]、Rchar2[L1~L24]、Cchar1[L1~L24]、Cchar2[L1~L24]、Cchar3[L1~L24]}.

[0076] From the above steps, we can see that when constructing equivalent circuit models of different orders and performing parameter identification, the main difference is the number of parameters to be identified. Therefore, for models built based on the equivalent circuit model mechanism, the step-by-step parameter acquisition method is generally applicable. Figure 7 to Figure 10 As shown, the equivalent circuit fitting effect of the third-order RC model is as follows Figure 11 to Figure 14 shown.

[0077] The method for acquiring modeling parameters of a lithium-ion battery energy storage system provided in the present embodiment first acquires the pulse voltage response of the lithium-ion battery energy storage system under different states of charge; secondly, in each state of charge, obtains the initial value of the constructed equivalent circuit parameter according to the pulse voltage response; then acquires the operating voltage response curve of the lithium-ion battery energy storage system; and divides the operating voltage response curve into segments with each state of charge as a dividing point; finally, for each actual use condition, assigns the corresponding initial value to the equivalent circuit, and uses the operating voltage response curve of the actual use condition as the target curve for parameter identification and optimization, so as to obtain the equivalent circuit parameters that meet the actual use condition, thereby saving test resources, requiring only less measured data to complete the parameter acquisition of the actual use condition, reducing development costs, and improving the simulation accuracy of the energy storage system, so as to obtain the equivalent circuit parameters that match different actual use conditions, and has better adaptability.

[0078] Embodiment 2

[0079] Figure 2 is a flow chart of a method for obtaining modeling parameters of a lithium-ion battery energy storage system according to another embodiment of the present invention, with reference to Figure 2 The method shown is described by taking the second-order RC model equivalent circuit as an example. Of course, the method of this embodiment is not limited to the second-order RC model equivalent circuit, and mainly includes the following steps:

[0080] Step 1: Use the optimized hybrid power pulse characteristic test method to test the lithium-ion battery energy storage system.

[0081] That is, to obtain the pulse voltage response of the lithium-ion battery energy storage system under different states of charge. For example, taking a certain type of lithium iron phosphate lithium-ion battery cell_1 for energy storage as an example, relatively dense state of charge interval points are set at its initial and final stages, while the state of charge points can be appropriately reduced in the middle platform stage (mid-term), and pulse charge and discharge current excitation is performed to obtain the pulse voltage response of the sample.

[0082] Step 2: Obtain the operating voltage response curve and perform segmented fitting.

[0083] In this embodiment, the state of charge moment shown in step 1 is used as a dividing point and applied to the obtained working condition voltage response curve, and then the working condition voltage response curve can be segmented according to the dividing points composed of the state of charge, and the corresponding working condition voltage response curve is intercepted. In this embodiment, the working condition voltage response curve is divided into 24 segments, namely L1, L2, ..., L24.

[0084] Step 3: Determine whether the segmentation is completed.

[0085] After the segmentation of the operating voltage response curve is completed, proceed to the subsequent steps, otherwise continue to perform segmented fitting of the operating voltage curve.

[0086] Step 4: Under each state of charge, the initial values ​​of the constructed equivalent circuit parameters are obtained according to the pulse voltage response.

[0087] Specifically, the equivalent circuit parameters include the equivalent ohmic resistance R0, which is calculated by the ratio of the voltage ΔU before and after the static-charging moment to the current I. The equivalent circuit parameters also include the electrochemical polarization resistance R1 and capacitance C1, the concentration difference polarization equivalent resistance R2 and capacitance C2, and the equivalent function expression U = a + be -t / τ1 +ce -t / τ2 Find the initial values ​​of R1, R2, C1 and C2; where τ1 = R1C1, τ2 = R2C2, a = U OCV -U R0 , and parameters b and c are constant values.

[0088] Step 5: Perform parameter identification and optimization, and use the least squares method to estimate the parameters.

[0089] In this embodiment, for each actual operating condition, the corresponding initial value is assigned to the constructed equivalent circuit, and the operating voltage response curve of the actual operating condition is used as the target curve for parameter identification and optimization to obtain equivalent circuit parameters that meet the actual operating condition.

[0090] Step 6: Determine whether the error of optimal fitting is less than a preset threshold. If not, continue to optimal fitting. If yes, proceed to step 7.

[0091] In this embodiment, the operating voltage response curve is used as the target curve for automatic parameter identification, and the optimized equivalent circuit parameters are obtained according to the set convergence conditions. The convergence condition can be that the difference between the simulation value after optimization and the corresponding value on the operating voltage response curve is less than or equal to a set threshold, or the number of optimization times reaches a set maximum number of iterations.

[0092] Step 7: After the optimization is completed, the equivalent circuit parameters after optimization are obtained.

[0093] The experimental results show that the results of parameter identification and optimization are highly correlated with the selection of initial values. The initial values ​​of parameters obtained by fitting the pulse response of segmented hybrid power pulse characteristics can effectively speed up the optimization process and avoid abnormal situations such as non-convergence.

[0094] The details of other operations performed in each step of this embodiment can be referred to the relevant steps of the aforementioned embodiment 1, and will not be expanded here.

[0095] The method for acquiring modeling parameters of a lithium-ion battery energy storage system provided in the present embodiment first acquires the pulse voltage response of the lithium-ion battery energy storage system under different states of charge; secondly, in each state of charge, obtains the initial value of the constructed equivalent circuit parameter according to the pulse voltage response; then acquires the operating voltage response curve of the lithium-ion battery energy storage system; and divides the operating voltage response curve into segments with each state of charge as a dividing point; finally, for each actual use condition, assigns the corresponding initial value to the equivalent circuit, and uses the operating voltage response curve of the actual use condition as the target curve for parameter identification and optimization, so as to obtain the equivalent circuit parameters that meet the actual use condition, thereby saving test resources, requiring only less measured data to complete the parameter acquisition of the actual use condition, reducing development costs, and improving the simulation accuracy of the energy storage system, so as to obtain the equivalent circuit parameters that match different actual use conditions, and has better adaptability.

[0096] Embodiment 3

[0097] Fig.15 is a schematic diagram of the structure of a device for acquiring modeling parameters of a lithium-ion battery energy storage system according to an embodiment of the present invention, with reference to Fig.15 , the device 1500 shown mainly comprises:

[0098] The first acquisition module 1501 is used to acquire the pulse voltage response of the lithium-ion battery energy storage system under different states of charge, wherein the pulse voltage response is obtained by pulse charging and discharging the lithium-ion battery energy storage system.

[0099] In one example, the pulse voltage response is obtained by a hybrid power pulse characteristic test, and the state of charge interval at the beginning and the end of the hybrid power pulse characteristic test is greater than the state of charge interval at the middle stage.

[0100] In one example, during the hybrid power pulse characteristic test, the lithium-ion battery energy storage system is charged and discharged at each charge state, and then pulse charged and discharged after being left idle for a period of time.

[0101] In one example, the equivalent circuit is an n-order RC model equivalent circuit, where n≥2.

[0102] The obtaining module 1502 is used to obtain the initial value of the constructed equivalent circuit parameter according to the pulse voltage response in each of the charge states.

[0103] In one example, the equivalent circuit parameters include an equivalent ohmic resistance R0, and the equivalent ohmic resistance R0 is calculated by the ratio of the voltage ΔU to the current I before and after the static-charging moment.

[0104] In one example, the equivalent circuit parameters also include: resistances R1 to R n And capacitors C1~C n , through the equivalent function expression Find the resistance R1~R n And capacitors C1~C n The initial value of; where τ1 = R1C1, τ2 = R2C2, ..., τ n =R n C n , a=U OCV -U R0 , and the parameters a1, a2, ..., a n are constant values, U OCV is the equivalent power supply voltage, U R0 is the voltage across the equivalent ohmic resistance R0.

[0105] The second acquisition module 1503 is used to obtain the operating voltage response curve of the lithium-ion battery energy storage system. The operating voltage response curve is a functional relationship between the dynamic voltage and the state of charge during actual use.

[0106] The segmentation module 1504 is used to segment the operating voltage response curve based on each state of charge as a dividing point.

[0107] The optimization module 1505 is used to assign the corresponding initial value to the equivalent circuit for each actual use condition, and use the voltage response curve of the actual use condition as the target curve to perform parameter identification and optimization to obtain equivalent circuit parameters that meet the actual use condition.

[0108] In one example, it also includes automatically identifying parameters using the operating voltage response curve as the target curve, and obtaining optimized equivalent circuit parameters according to set convergence conditions.

[0109] In one example, the convergence condition is that the difference between the optimized simulation value and the corresponding value on the operating voltage response curve is less than or equal to a set threshold, or the number of optimization times reaches a set maximum number of iterations.

[0110] The details of other operations performed by each module in this embodiment can be referred to the aforementioned embodiment 1, which will not be elaborated here.

[0111] The modeling parameter acquisition device of the lithium-ion battery energy storage system provided in the present embodiment obtains the pulse voltage response of the lithium-ion battery energy storage system under different charge states; secondly, in each charge state, obtains the initial value of the constructed equivalent circuit parameter according to the pulse voltage response; then obtains the working condition voltage response curve of the lithium-ion battery energy storage system; and divides the working condition voltage response curve into segments with each charge state as a dividing point; finally, for each actual use condition, the corresponding initial value is assigned to the equivalent circuit, and the working condition voltage response curve of the actual use condition is used as the target curve for parameter identification and optimization, so as to obtain the equivalent circuit parameters that meet the actual use condition, thereby saving test resources, requiring only less measured data to complete the parameter acquisition of the actual use condition, reducing the development cost, and also improving the simulation accuracy of the energy storage system, obtaining the equivalent circuit parameters matching different actual use conditions, and having better adaptability.

[0112] A lithium-ion battery energy storage system modeling parameter acquisition device in the embodiment of the present application can be a device, or a component, integrated circuit, or chip in a terminal. A lithium-ion battery energy storage system modeling parameter acquisition device in the embodiment of the present application can be a device with an operating system. The operating system can be an Android operating system, an iOS operating system, or other possible operating systems, which are not specifically limited in the embodiment of the present application.

[0113] The present application also provides an electronic device, comprising: a memory for storing programs or instructions executable by a processor; and a processor for executing the above-mentioned programs or instructions to implement the various processes of the above-mentioned lithium-ion battery energy storage system modeling parameter acquisition method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0114] Fig.161 is a schematic diagram of an electronic device according to an embodiment of the present invention. The electronic device 1600 may include an internal communication bus 1601, a processor 1602, a read-only memory (ROM) 1603, a random access memory (RAM) 1604, and a communication port 1605. When applied to a personal computer, the electronic device 1600 may also include a hard disk 1606. The internal communication bus 1601 can realize data communication between components of the electronic device 1600. The processor 1602 can make judgments and issue prompts. In some embodiments, the processor 1602 can be composed of one or more processors. The communication port 1605 can realize data communication between the electronic device 1600 and the outside. In some embodiments, the electronic device 1600 can send and receive information and data from the network through the communication port 1605. The electronic device 1600 may also include different forms of program storage units and data storage units, such as a hard disk 1606, a read-only memory (ROM) 1603 and a random access memory (RAM) 1604, which can store various data files used for computer processing and / or communication, as well as possible programs or instructions executed by the processor 1602. The results processed by the processor 1602 are transmitted to the user device through the communication port 1605 and displayed on the user interface.

[0115] The above-mentioned lithium-ion battery energy storage system modeling parameter acquisition method can be implemented as a computer program, saved in the hard disk 1606, and can be recorded in the processor 1602 for execution to implement any lithium-ion battery energy storage system modeling parameter acquisition method in this application.

[0116] The embodiment of the present application also provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, each process of the above-mentioned lithium-ion battery energy storage system modeling parameter acquisition method embodiment is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0117] The processor is the processor in the electronic device in the above embodiment. The readable storage medium includes a computer readable storage medium, such as a computer read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0118] Obviously, for those skilled in the art, the above invention disclosure is only used as an example and does not constitute a limitation of the present application. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements and amendments to the present application. Such modifications, improvements and amendments are suggested in the present application, so such modifications, improvements and amendments still belong to the spirit and scope of the exemplary embodiments of the present application.

[0119] At the same time, the present application uses specific words to describe the embodiments of the present application. For example, "one embodiment", "an embodiment", and / or "some embodiments" refer to a certain feature, structure or characteristic related to at least one embodiment of the present application. Therefore, it should be emphasized and noted that "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more in different positions in this specification does not necessarily refer to the same embodiment. In addition, some features, structures or characteristics in one or more embodiments of the present application can be appropriately combined.

[0120] Some aspects of the present application may be performed entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. The above hardware or software may be referred to as "data blocks", "modules", "engines", "units", "components" or "systems". The processor may be one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DAPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, or combinations thereof. In addition, various aspects of the present application may be expressed as computer products located in one or more computer-readable media, which include computer-readable program codes. For example, computer-readable media may include, but are not limited to, magnetic storage devices (e.g., hard disks, floppy disks, tapes ...), optical disks (e.g., compact disks CDs, digital versatile disks DVDs ...), smart cards, and flash memory devices (e.g., cards, sticks, key drives ...).

[0121] In some embodiments, numbers describing the number of components and attributes are used. It should be understood that such numbers used in the description of the embodiments are modified by the modifiers "about", "approximately" or "substantially" in some examples. Unless otherwise specified, "about", "approximately" or "substantially" indicate that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may change according to the required features of individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and adopt the general method of retaining digits. Although the numerical domains and parameters used to confirm the breadth of their range in some embodiments of the present application are approximate values, in specific embodiments, the setting of such numerical values ​​is as accurate as possible within the feasible range.

[0122] Although the present application has been described with reference to the current specific embodiments, ordinary technicians in this technical field should recognize that the above embodiments are only used to illustrate the present application, and various equivalent changes or substitutions may be made without departing from the spirit of the present application. Therefore, as long as the changes and modifications to the above embodiments are within the essential spirit of the present application, they will fall within the scope of the claims of the present application.

Claims

1. A method for obtaining modeling parameters of a lithium-ion battery energy storage system, characterized in that: include: Obtaining a pulse voltage response of the lithium-ion battery energy storage system at different states of charge, wherein the pulse voltage response is obtained by pulse charging and discharging the lithium-ion battery energy storage system; At each of the charge states, obtaining initial values ​​of the constructed equivalent circuit parameters according to the pulse voltage response; Obtaining an operating voltage response curve of the lithium-ion battery energy storage system, wherein the operating voltage response curve is a functional relationship between a dynamic voltage and a state of charge during actual operating conditions; Taking each state of charge as a dividing point, the operating condition voltage response curve is segmented to obtain a plurality of actual operating conditions; For each actual operating condition, the corresponding initial value is assigned to the equivalent circuit, and the operating voltage response curve of the actual operating condition is used as the target curve for parameter identification and optimization to obtain equivalent circuit parameters that meet the actual operating condition.

2. The method for obtaining modeling parameters of a lithium-ion battery energy storage system according to claim 1, characterized in that: The pulse voltage response is obtained by a hybrid power pulse characteristic test, and the state of charge interval at the beginning and end of the hybrid power pulse characteristic test is greater than the state of charge interval in the middle.

3. The method for obtaining modeling parameters of a lithium-ion battery energy storage system according to claim 2, characterized in that: Also includes: During the hybrid power pulse characteristic test, the lithium-ion battery energy storage system is charged and discharged in each charge state, and pulse charging and discharging are performed after standing for a period of time.

4. The method for obtaining modeling parameters of a lithium-ion battery energy storage system according to claim 1, characterized in that: The equivalent circuit is an n-order RC model equivalent circuit, where n≥2.

5. The method for obtaining modeling parameters of a lithium-ion battery energy storage system according to claim 4, characterized in that: The equivalent circuit parameters include an equivalent ohmic resistance R0, and the equivalent ohmic resistance R0 is calculated by the ratio of the voltage ΔU to the current I before and after the static-charging moment.

6. The method for obtaining modeling parameters of a lithium-ion battery energy storage system according to claim 5, characterized in that: The equivalent circuit parameters also include: resistances R1~R n And capacitor C1~C n , through the equivalent function expression Find the resistance R1~R n and capacitors C1~C n The initial value of , , and the parameter are constant values, is the equivalent power supply voltage, is the voltage across the equivalent ohmic resistance R0.

7. The method for obtaining modeling parameters of a lithium-ion battery energy storage system according to any one of claims 1 to 6, characterized in that: The voltage response curve of the actual working condition is used as the target curve for parameter identification and optimization. The equivalent circuit parameters that meet the actual working condition are also as follows: Automatic parameter identification is performed using the operating voltage response curve as a target curve, and optimized equivalent circuit parameters are obtained according to set convergence conditions.

8. The method for obtaining modeling parameters of a lithium-ion battery energy storage system according to claim 7, characterized in that: The convergence condition is that the difference between the optimized simulation value and the corresponding value on the operating voltage response curve is less than or equal to a set threshold, or the number of optimization times reaches a set maximum number of iterations.

9. A device for acquiring modeling parameters of a lithium-ion battery energy storage system, characterized in that: include: A first acquisition module is used to acquire a pulse voltage response of the lithium-ion battery energy storage system under different states of charge, wherein the pulse voltage response is obtained by pulse charging and discharging the lithium-ion battery energy storage system; An obtaining module, used for obtaining the initial value of the constructed equivalent circuit parameter according to the pulse voltage response in each of the charge states; A second acquisition module is used to obtain a working condition voltage response curve of the lithium-ion battery energy storage system, wherein the working condition voltage response curve is a functional relationship between a dynamic voltage and a state of charge during an actual use condition; A segmentation module, used to segment the operating condition voltage response curve using each state of charge as a dividing point to obtain a plurality of actual operating conditions; The optimization module is used to assign the corresponding initial value to the equivalent circuit for each actual use condition, and use the working voltage response curve of the actual use condition as the target curve to perform parameter identification and optimization to obtain equivalent circuit parameters that meet the actual use condition.

10. An electronic device, characterized in that: include: A processor and a memory, wherein the memory stores programs or instructions that can be run on the processor, and when the programs or instructions are executed by the processor, the steps of the method for obtaining modeling parameters of a lithium-ion battery energy storage system as described in any one of claims 1 to 8 are implemented.

11. A readable storage medium, characterized in that: The readable storage medium stores a program or instruction, and when the program or instruction is executed by the processor, the steps of the method for obtaining modeling parameters of a lithium-ion battery energy storage system as described in any one of claims 1 to 8 are implemented.

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