A battery parameter identification method, system and product

Abstract

The present invention discloses a battery parameter identification method, system and product, which relates to the field of new energy. The method includes performing a mixed pulse power test on a power battery, and taking the stable voltage value after a set static time as the open circuit voltage value of the current SOC, fitting the mapping relationship between different SOCs and open circuit voltage values ​​to obtain the open circuit voltage identification value, and calculating the ohmic internal resistance value according to the test data under different SOCs; superimposing an AC sine wave on the power battery on an electrochemical workstation to obtain the AC impedance spectrum of the power battery under different SOCs and building a second-order RC model, taking the ohmic internal resistance value under different SOCs as the ohmic internal resistance initial value in the second-order RC model to iteratively fit each polarization parameter, obtaining the identification value of each polarization parameter under different SOCs, and completing the battery parameter identification. The present invention can improve the parameter identification accuracy of the battery second-order RC model.

Description

Technical Field

[0001] The present invention relates to the field of new energy technologies, and in particular to a battery parameter identification method, system and product. Background Art

[0002] Lithium-ion batteries (referred to as lithium batteries) have rapidly developed into a new generation of energy storage power due to their high energy density, long cycle life, no memory effect, and green environmental protection. In order to ensure the safe and reliable operation of battery energy storage systems, a complete battery management system must be established based on accurate battery models to estimate and predict battery status.

[0003] There are two commonly used battery models: electrochemical models and equivalent circuit models. Electrochemical models express basic electrochemical reactions through complex nonlinear partial differential algebraic equations. Although they can accurately capture various characteristics of batteries, they require a large number of complex calculations to obtain solutions to the equations, which places high demands on the computing power of the processor. Therefore, such models are generally suitable for battery design rather than system-level simulation. In contrast, the equivalent circuit model is based on the working principle of the battery and uses a circuit network composed of electronic components such as resistors and capacitors to describe the working characteristics of the battery. Because of its clear physical meaning, it is easy to perform mathematical analysis and parameter identification, and is widely used in new energy vehicle simulation. The accuracy of the equivalent circuit model depends largely on the structure of the model and the accuracy of the model parameter identification. Theoretically, higher-order equivalent circuit models can characterize more types of batteries and generate more accurate voltage estimates, but they have the disadvantages of increasing the computational burden and reducing the numerical stability of the later battery state parameter estimation. For the second-order RC equivalent circuit model, the quality of the model parameter identification method directly determines the reliability and accuracy of the parameters in the model. The least squares fitting method is simple to calculate, but the error is large; the Kalman filter method can obtain the optimal estimate of the state variable, but the value of the system noise will directly affect the accuracy of the identification; the genetic algorithm has good global identification accuracy, but the local search ability is poor. Since the second-order RC equivalent circuit model is a nonlinear system, for nonlinear systems, current research usually uses nonlinear parameter linearization, and then uses the linear least squares fitting method to achieve parameter identification, but there are nonlinear parameters in the second-order RC equivalent circuit model that cannot be linearized.

[0004] Therefore, a battery parameter identification method is urgently needed to solve the above problems. Summary of the invention

[0005] The purpose of the present invention is to provide a battery parameter identification method, system and product, which can improve the parameter identification accuracy of the battery second-order RC model.

[0006] To achieve the above object, the present invention provides the following solutions:

[0007] A battery parameter identification method, the method comprising:

[0008] Performing a mixed pulse power test on the power battery until the SOC value of the power battery drops to 5%, and recording the test data during the test; the test data includes the stable voltage value of the power battery after standing for a set period of time, the voltage value at the start of discharge, the voltage value at the end of discharge, the voltage value at the next moment after the end of discharge, the voltage value at the moment before charging, the voltage value at the start of charging, the voltage value at the end of charging, the voltage value at the next moment after the end of charging, the charging current, and the discharging current during each charging and discharging process;

[0009] The stable voltage value after the static setting period of time is taken as the open circuit voltage value corresponding to the current SOC, and the least squares method is used to fit the mapping relationship between different SOCs and the corresponding open circuit voltage values ​​to obtain the open circuit voltage identification value;

[0010] According to the corresponding test data under different SOCs, calculate the ohmic internal resistance value of the power battery under different SOCs;

[0011] On the electrochemical workstation, an AC sine wave with a set voltage value is superimposed on the power battery at different SOCs, and the relationship between the ratio of the AC potential and the current signal and the frequency of the AC sine wave is measured to obtain the AC impedance spectrum of the power battery at different SOCs;

[0012] Based on the AC impedance spectrum of the power battery at different SOCs, a second-order RC model is built in an electrochemical workstation, and the ohmic internal resistance value of the power battery at different SOCs is used as the initial value of the ohmic internal resistance in the second-order RC model, and different polarization parameters in the second-order RC model are iteratively fitted to obtain identification values ​​of different polarization parameters in the second-order RC model at different SOCs, thereby completing battery parameter identification; the polarization parameters include electrochemical polarization internal resistance, electrochemical polarization capacitance, concentration polarization internal resistance, and concentration polarization capacitance.

[0013] Optionally, a hybrid pulse power test is performed on the power battery, specifically including:

[0014] The power battery is fully charged by a constant voltage and current limiting method, so that the SOC value of the power battery reaches 100%, and the power battery is left to stand until the terminal voltage of the power battery is stable;

[0015] Perform constant current discharge at a rate of 1C to reduce the SOC value of the power battery by 5%, and let it stand for 10 hours to stabilize the terminal voltage of the power battery;

[0016] Discharge at a current pulse rate of 3C for 10 seconds and let stand for 5 hours to stabilize the terminal voltage of the power battery;

[0017] The battery was charged with a 3C current pulse for 10 seconds and left to stand for 5 hours to stabilize the terminal voltage of the power battery. The charging and discharging operations were repeated until the SOC value of the power battery dropped to 5%.

[0018] Optionally, when performing a mixed pulse power test on the power battery, each cycle is separated by an interval of 5% SOC value.

[0019] Optionally, the ohmic internal resistance values ​​of the power battery at different SOCs are used as the initial values ​​of the ohmic internal resistance in the second-order RC model, and different polarization parameters in the second-order RC model are iteratively fitted, specifically including:

[0020] The ohmic internal resistance values ​​of the power battery at different SOCs are used as the initial values ​​of the ohmic internal resistance at different SOCs in the second-order RC model, the initial values ​​of the polarization parameters are set to 1, and the polarization parameters are iteratively fitted to obtain fitting values ​​of the polarization parameters;

[0021] Obtaining fitting errors of the polarization parameters according to the fitting values ​​of the polarization parameters;

[0022] Determining whether the fitting error of each polarization parameter meets a set range;

[0023] If the fitting error satisfies the set range, the iterative fitting is stopped, and the fitting value of each polarization parameter is output as an identification value;

[0024] If the fitting error does not meet the set range, each of the polarization parameters is reassigned, and the iterative fitting is continued until the fitting error meets the set range.

[0025] Optionally, if the fitting error satisfies a set range, the iterative fitting is stopped, and the fitting value of each polarization parameter is output as an identification value, specifically including:

[0026] If the fitting error of any polarization parameter in the polarization parameters does not exceed 5% in two consecutive rounds of fitting, the fitting value of the latest round of the corresponding polarization parameter is selected as the final identification value, and the iterative fitting is stopped.

[0027] Optionally, if the fitting error does not satisfy a set range, reassigning each of the polarization parameters, and continuing the iterative fitting until the fitting error satisfies a set range, specifically including:

[0028] If the fitting error of any polarization parameter in the polarization parameters exceeds 10% in two consecutive rounds of fitting, the average of the fitting values ​​of the two consecutive corresponding polarization parameters is selected as the fitting value assignment for the next round, and the fitting operation is repeated;

[0029] If the fitting error of any polarization parameter in the polarization parameters does not exceed 10% in two consecutive rounds of fitting, the fitting value of the corresponding polarization parameter in the previous round is selected as the assignment for the next round of fitting, and the fitting operation is repeated.

[0030] Optionally, after completing the battery parameter identification, the method further includes:

[0031] The open circuit voltage values, ohmic internal resistance values ​​corresponding to different SOCs, and the identification values ​​of different polarization parameters in the second-order RC model under different SOCs are imported into Simulink in the form of MAP to build a one-dimensional dynamic simulation model of the power battery; the one-dimensional dynamic simulation model of the power battery is used to estimate the state of the battery and simulate the electrical characteristics of the battery.

[0032] Optionally, after obtaining the one-dimensional dynamic simulation model of the power battery, the method further includes:

[0033] Determining a terminal voltage simulation result of the current power battery according to the one-dimensional dynamic simulation model of the power battery;

[0034] The actual value of the terminal voltage of the current power battery obtained by experimental sampling is obtained, and the terminal voltage simulation result is compared with the actual value of the terminal voltage to verify the one-dimensional dynamic simulation model of the power battery.

[0035] To achieve the above objectives, the present invention further provides a computer system, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-mentioned battery parameter identification method.

[0036] To achieve the above object, the present invention further provides a computer program product, including a computer program, which implements the above-mentioned battery parameter identification method when executed by a processor.

[0037] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0038] The present invention provides a battery parameter identification method, system and product, which greatly improves the parameter identification accuracy of the battery second-order RC model through simple iterative optimization operations based on a small amount of actual battery tests, thereby building a battery second-order RC equivalent circuit model with high accuracy. The present invention reduces the testing cost, helps battery research and development, and promotes the development of battery virtual testing technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. 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 paying creative work.

[0040] Figure 1 A flow chart of a battery parameter identification method provided in Example 1 of the present invention;

[0041] Figure 2 The overall framework diagram of the battery parameter identification method provided in Example 1 of the present invention;

[0042] Figure 3 This is a local enlarged view of pulse discharge;

[0043] Figure 4 This is a local enlarged view of pulse charging;

[0044] Figure 5 It is a schematic diagram of the second-order RC equivalent circuit model;

[0045] Figure 6 This is a schematic diagram of the electrochemical workstation parameter fitting interface;

[0046] Figure 7 This is a schematic diagram of experimental equipment connections;

[0047] Figure 8 A schematic diagram of building a second-order RC equivalent model in an electrochemical workstation;

[0048] Fig. 9 A schematic diagram of the iterative process of polarization parameter optimization in an electrochemical workstation;

[0049] Fig.10 This is a schematic diagram of the Simulink battery simulation model;

[0050] Fig.11 Schematic diagram of the experimental battery model verification results. DETAILED DESCRIPTION

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

[0052] The purpose of the present invention is to provide a battery parameter identification method, system and product, which uses a second-order RC equivalent circuit model as a research object, identifies the open circuit voltage and ohmic internal resistance of the second-order RC model through the hybrid pulse power (HPPC) test data of the charging and discharging equipment, and then fits the electrochemical polarization parameters and concentration polarization parameters of the second-order RC model through the AC impedance test data and model of the electrochemical workstation, so that the identification results of HPPC and the fitting results of the electrochemical workstation are iteratively optimized to continuously improve the identification accuracy of the parameters. Finally, based on the parameters finally identified, a complete second-order RC model is built in Matlab / Simulink, and the battery terminal voltage value obtained by the model simulation is compared with the actual terminal voltage value obtained by the experimental sampling to verify the accuracy of the parameters obtained by the identification method.

[0053] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0054] Example 1

[0055] like Figure 1 and Figure 2 As shown, the battery parameter identification method in this embodiment specifically includes the following steps:

[0056] Step S1: Perform a mixed pulse power test on the power battery until the SOC value of the power battery drops to 5%, and record the test data during the test; the test data includes the voltage value corresponding to the current SOC measured after the power battery is left to stand for a set period of time after each charging and discharging.

[0057] Specifically, HPPC testing was performed using a charge-discharge device and an environmental chamber.

[0058] HPPC test method: The HPPC working condition is shown in Table 1. It is designed according to the consumption of 5% SOC per cycle, and 19 sets of HPPC working condition data can be obtained. First, the power battery is fully charged by constant voltage and current limiting. At this time, the SOC value is 100%, and it is left to stand until the voltage is stable; 5% of the initial capacity is discharged at a constant current of 1C to reduce the SOC by 5%, and the battery terminal voltage is stabilized for 10 hours; then, a 3C current pulse is discharged for 10s, and the battery terminal voltage is stabilized for 5 hours; finally, a 3C current pulse is charged for 10s, and the battery terminal voltage is stabilized for 5 hours, and the above process is repeated until the SOC value drops to 5%. After research, this scheme is used for HPPC testing. On the one hand, a smaller SOC separation interval can effectively improve the accuracy of parameter fitting. On the other hand, high pulse rate and long standing time can effectively improve the accuracy and precision of parameter identification, which is convenient for more accurate identification of the open circuit voltage value and ohmic internal resistance value of the second-order RC model in the future.

[0059] Table 1 HPPC operating conditions

[0060]

[0061] Step S2: taking the stable voltage value after standing for a set period of time as the open circuit voltage value corresponding to the current SOC, and using the least squares method to fit the mapping relationship between different SOCs and corresponding open circuit voltage values ​​to obtain an identification value of the open circuit voltage.

[0062] Specifically, the open circuit voltage of the battery is approximately equal to the voltage measured after each discharge to adjust the SOC and standing for a long period of time. Identifying the open circuit voltage is to find the functional relationship between the open circuit voltage and the SOC. The test data is as follows: In the test data of the HPPC test, the voltage value of the last row of the step number 2 (in Table 1) of each cycle is used as the open circuit voltage value under the corresponding SOC state, and then the discrete data is fitted using the least squares method to obtain the mapping relationship between the open circuit voltage and the SOC.

[0063] Step S3: Calculate the ohmic internal resistance of the power battery at different SOCs according to the corresponding test data at different SOCs. The test data include the stable voltage value after standing for a set period of time, the voltage value at the start of discharge, the voltage value at the end of discharge, the voltage value at the next moment after the end of discharge, the voltage value at the moment before charging, the voltage value at the start of charging, the voltage value at the end of charging, the voltage value at the next moment after the end of charging, the charging current, and the discharging current of the power battery during each charging and discharging process.

[0064] Specifically, the test data of the charge and discharge pulse stage are used to identify the ohmic internal resistance of the battery at different SOC states. The test data required are: in the test data of the HPPC test, the voltage value of the last row of the step number 2 (in Table 1) of each cycle is recorded as ; The voltage value of the first row of the process step number 3 (in Table 1) of each cycle is recorded as ; The voltage value of the last row of the process step number 3 (in Table 1) of each cycle is recorded as ; The voltage value of the first row of the process step number 4 (in Table 1) of each cycle is recorded as ; The voltage value of the last row of the process step number 4 (in Table 1) of each cycle is recorded as ; The voltage value of the first row of the process step number 5 (in Table 1) of each cycle is recorded as ; The voltage value of the last row of the process step number 5 (in Table 1) of each cycle is recorded as ; The voltage value of the first row of the step number 6 (in Table 1) of each cycle is recorded as In order to analyze the ohmic polarization during charge and discharge in detail, one of the discharge and charge stages of the HPPC pulse test is magnified, as shown in Figure 2. Figure 3 and Figure 4 shown.

[0065] Figure 3 and Figure 4 In the figure, the A→B and C→D stages are the moments when the current is loaded or disconnected. It can be seen that the terminal voltage will have an obvious step-like change. This is the effect of the ohmic internal resistance R0. It is not a fixed constant, but a parameter that changes with the remaining power of the battery. In order to make the battery model closer to the actual battery, the present invention will calculate the ohmic internal resistance value at different SOC values ​​of charging and discharging.

[0066] During discharge: the average value of the voltage at the beginning and end of the discharge pulse is used for calculation. The specific calculation formula is as follows:

[0067] (1)

[0068] When charging: use the average value of the voltage at the beginning and end of the charging pulse to calculate. The specific calculation formula is as follows:

[0069] (2)

[0070] In the formula, is the ohmic internal resistance during the discharge process, is the ohmic internal resistance during charging, The stable voltage value after the first period of static setting is the voltage value of the last row of the process step numbered 2 (in Table 1) of each cycle; is the voltage value at the start of discharge, i.e., the voltage value of the first row of the process step numbered 3 (in Table 1) of each cycle; is the voltage value at the end of discharge, i.e., the voltage value of the last row of the process step numbered 3 (in Table 1) of each cycle; is the voltage value at the next moment when the discharge ends, that is, the voltage value of the first row of the process step numbered 4 (in Table 1) of each cycle; is the voltage value at the moment before charging, that is, the voltage value of the last row of the process step numbered 4 (in Table 1) of each cycle; is the voltage value at the start of charging, i.e., the voltage value of the first row of the process step numbered 5 (in Table 1) of each cycle; is the voltage value at the end of charging, i.e., the voltage value of the last row of step number 5 (in Table 1) of each cycle; It is the voltage value at the next moment when charging is completed, that is, the voltage value of the first row of step number 6 (in Table 1) of each cycle.

[0071] Step S4: On the electrochemical workstation, an AC sine wave with a set voltage value is superimposed on the power battery at different SOCs, and the relationship between the ratio of the AC potential and the current signal and the frequency of the AC sine wave is measured to obtain the AC impedance spectrum of the power battery at different SOCs.

[0072] Under different SOCs, an AC sine wave with a voltage of 5mV from 0.1Hz to 10KHz is superimposed on the power battery on the electrochemical workstation, and the relationship between the ratio of the AC potential and the current signal and the frequency of the sine wave is measured to form the AC impedance spectrum of the battery, and then the AC impedance spectrum under different SOCs is obtained.

[0073] Step S5: Based on the AC impedance spectrum of the power battery at different SOCs, a second-order RC model is built in an electrochemical workstation, and the ohmic internal resistance value of the power battery at different SOCs is used as the initial value of the ohmic internal resistance in the second-order RC model, and different polarization parameters in the second-order RC model are iteratively fitted to obtain identification values ​​of different polarization parameters in the second-order RC model at different SOCs, thereby completing battery parameter identification; the polarization parameters include electrochemical polarization internal resistance, electrochemical polarization capacitance, concentration polarization internal resistance, and concentration polarization capacitance.

[0074] Specifically, the second-order RC model consists of a resistor and two RC networks, such as Figure 5 As shown. Among them, is the open circuit voltage, V is the terminal voltage, I is the current, is the ohmic internal resistance, , is the polarization internal resistance, , is a polarized capacitor.

[0075] According to the measured AC impedance spectrum, a second-order RC equivalent model is constructed in the electrochemical workstation. A resistor module is dragged to the main circuit, and then two RC parallel circuits are dragged to the main circuit. Figure 6 As shown. Fill the ohmic internal resistance values ​​at different SOCs identified by the HPPC test data into the R0 dialog box of the parameter fitting window as the initial value, set it to the fixed value state "Fixed", and assign 1 to the other four parameters (2 polarization internal resistances, 2 polarization capacitors) as the initial value (different initial assignments can be made according to the actual conditions of different batteries), and set it to the state to be fitted "Free". Click the "Fit" button, and the electrochemical workstation will iteratively fit according to the Nyquist plot of the electrochemical impedance spectrum to obtain the first round of fitting results. , , and value.

[0076] If the "Error%" value of a polarization parameter exceeds 10% in two consecutive rounds of fitting, the average of the two consecutive fitting results is selected as the fitting value, and the fitting operation is repeated; if the "Error%" value of a polarization parameter does not exceed 10% in two consecutive rounds of fitting, the fitting result of a new round is selected as the fitting value, and the fitting operation is repeated; if the "Error%" value of a polarization parameter does not exceed 5% in two consecutive rounds of fitting, the latest round of fitting results of the polarization parameter can be taken as the final identification value of the polarization parameter.

[0077] By repeating the above iterative fitting operation, the identification results of the four polarization parameters of the second-order RC equivalent model with errors within a controllable range under different SOCs can be obtained.

[0078] Furthermore, the battery parameter identification method of the present invention also includes step S6: importing the corresponding open circuit voltage value, ohmic internal resistance value under different SOCs and the identification values ​​of different polarization parameters in the second-order RC model under different SOCs into Simulink in the form of MAP to build a one-dimensional dynamic simulation model of the power battery; the one-dimensional dynamic simulation model of the power battery is used to estimate the state of the battery and simulate the electrical characteristics of the battery.

[0079] Specifically, after the one-dimensional dynamic simulation model of the power battery is determined, the six identified parameters ( , , , , and ) is imported into the Simulink module in the form of MAP.

[0080] According to Kirchhoff's law, the circuit equation of the second-order RC equivalent circuit model is sorted out:

[0081] (3)

[0082] in, is the electrochemical polarization voltage, is the concentration polarization voltage. And formula (3), SOC, and is the state variable, I is the input, and V is the output, and the state space equation of the battery model is obtained. In order to facilitate computer data processing, the state equation needs to be discretized. The discretized system state equation is as follows:

[0083] (4)

[0084] in, T is the sampling time, , , C = battery nominal capacity (mAh) * nominal voltage. The state equation of the battery model is:

[0085] (5)

[0086] Furthermore, after obtaining the mathematical equation of the second-order RC equivalent circuit model, a one-dimensional dynamic simulation model equivalent to the actual battery can be built in MATLAB / Simulink software.

[0087] The one-dimensional dynamic simulation model built in Simulink is run under the same current conditions as the actual test. The battery terminal voltage value obtained by model simulation is compared with the actual terminal voltage value obtained by experimental sampling to verify the accuracy of the one-dimensional dynamic simulation model, thereby verifying the accuracy of the parameters obtained by the identification method.

[0088] Technical effects of the present invention:

[0089] The present invention can greatly improve the parameter identification accuracy of the battery second-order RC model through simple iterative optimization operations based on a small amount of actual battery tests, thereby building a battery second-order RC equivalent circuit model with high accuracy. This method reduces the testing cost, helps battery research and development, and promotes the development of battery virtual testing technology.

[0090] Example 2

[0091] Embodiment 2 is a preferred implementation of Embodiment 1, and specifically comprises the following steps:

[0092] (1) Perform HPPC testing using charging and discharging equipment and an environmental chamber.

[0093] A special battery test platform is used to test the battery's charge and discharge characteristics. Figure 7 As shown, it consists of a control computer, a single-cell charging and discharging device, and a high and low temperature test chamber (-40℃~150℃). The maximum discharge current of the single-cell charging and discharging device is 300A, and the accuracy is 0.02%. It can support constant voltage, constant current, constant power, pulse, and any programming conditions. According to the "US Freedom CAR Battery Experiment Manual", the battery is subjected to HPPC test, which is carried out at 25℃ and standard atmospheric pressure.

[0094] A lithium iron phosphate hard shell battery produced by a battery company was selected for HPPC testing, and its basic parameters are shown in Table 2. The battery was placed in an environmental chamber, and the HPPC test conditions shown in Table 3 were applied to the charging and discharging equipment. After the test was completed, complete data of the HPPC test was obtained.

[0095] Table 2 Basic parameters of batteries

[0096]

[0097] Table 3 HPPC operating conditions

[0098]

[0099] (2) Use HPPC test data to identify open circuit voltage and ohmic polarization parameters: open circuit voltage and ohmic internal resistance.

[0100] The stable voltage value after each discharge to adjust the SOC of the battery and standing for a long time is extracted as the open circuit voltage value under the corresponding SOC state, that is, the voltage value at the last moment of step 2 in Table 3. Then, the least squares method is used to fit the discrete data to obtain the mapping relationship between the open circuit voltage and the SOC.

[0101] The test data of the charge and discharge pulse stages are used to identify the ohmic internal resistance of the battery at different SOC states.

[0102] Through the identification and fitting of HPPC test data, the open circuit voltage of the battery in different SOC states in this embodiment ( ) and the internal resistance in ohms ( ) are shown in Table 4 respectively.

[0103] Table 4 Parameter identification results

[0104]

[0105] (3) Perform AC impedance test using an electrochemical workstation.

[0106] Under different SOC, an AC sine wave with a voltage of 0.1Hz~10KHz5mV is superimposed on the power battery on the electrochemical workstation, and the relationship between the ratio of the AC potential and the current signal and the frequency of the sine wave is measured to form the electrochemical impedance spectrum of the battery.

[0107] (4) A second-order RC model was built in the electrochemical workstation to fit the electrochemical polarization internal resistance, electrochemical polarization capacitance, concentration polarization internal resistance, and concentration polarization capacitance, and the models were iteratively optimized in conjunction with the HPPC identification results.

[0108] According to the measured AC impedance spectrum, a second-order RC equivalent model was constructed in the electrochemical workstation, such as Figure 8-Figure 9 The ohmic internal resistance values ​​at different SOCs identified by HPPC test data are filled into the parameter fitting window In the dialog box, set it to the fixed value state "Fixed", set the other four parameters to the to-be-fitted state "Free", click the "Fit" button, the electrochemical workstation will fit the other four parameters according to the tested AC impedance spectrum and the model built by the user, find the parameter with the smaller "Error%" and set it to "Fixed", click the "Fit" button again to fit, view the results, find the parameter with the smaller "Error%" in the new round of fitting results and set it to "Fixed", and at the same time increase or decrease the parameter with the larger "Error%", click the "Fit" button again to view the results, and so on, continuously optimize the iterative fitting results until the "Error%" values ​​of all polarization parameters are controlled within an acceptable range. The iterative process is as follows: Figure 8 As shown. After iteration, the electrochemical polarization internal resistance ( )、Electrochemical polarization capacitance( )、Concentration polarization internal resistance( )、Concentration polarization capacitance( ), and iteratively optimized with the identification results of HPPC test. They are shown in Table 5 and Table 6 respectively.

[0109] Table 5 Pulse discharge parameter identification results

[0110]

[0111] Table 6 Pulse charging parameter identification results

[0112]

[0113] (5) Simulink model construction.

[0114] Build its dynamic simulation model in MATLAB / Simulink software. Import the identified 6 parameters into the Simulink module in the form of MAP to obtain a dynamic simulation model equivalent to the actual battery, such as Fig.10 shown.

[0115] (6) Model validation.

[0116] The battery terminal voltage value obtained by model simulation is compared with the actual value obtained by experimental sampling to verify the accuracy of the parameters obtained by the identification method.

[0117] The battery equivalent model is simulated and verified under HPPC working conditions. Fig.11 It shows that the terminal voltage curve simulated by the model is consistent with the change pattern of the terminal voltage curve of the actual test, and the maximum error of the entire simulation stage is 0.026V, which proves the accuracy of the parameter identification method and the dynamic simulation model.

[0118] Example 3

[0119] A computer system includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the battery parameter identification method in embodiment 1.

[0120] Example 4

[0121] A computer program product includes a computer program, and when the computer program is executed by a processor, the battery parameter identification method in embodiment 1 is implemented.

[0122] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0123] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A battery parameter identification method, characterized in that: The method comprises: Performing a mixed pulse power test on the power battery until the SOC value of the power battery drops to 5%, and recording the test data during the test; the test data includes the stable voltage value of the power battery after standing for a set period of time, the voltage value at the start of discharge, the voltage value at the end of discharge, the voltage value at the next moment after the end of discharge, the voltage value at the moment before charging, the voltage value at the start of charging, the voltage value at the end of charging, the voltage value at the next moment after the end of charging, the charging current, and the discharging current during each charging and discharging process; The stable voltage value after the static setting period of time is taken as the open circuit voltage value corresponding to the current SOC, and the least squares method is used to fit the mapping relationship between different SOCs and the corresponding open circuit voltage values ​​to obtain the open circuit voltage identification value; According to the corresponding test data under different SOCs, calculate the ohmic internal resistance value of the power battery under different SOCs; On the electrochemical workstation, an AC sine wave with a set voltage value is superimposed on the power battery at different SOCs, and the relationship between the ratio of the AC potential and the current signal and the frequency of the AC sine wave is measured to obtain the AC impedance spectrum of the power battery at different SOCs; Based on the AC impedance spectrum of the power battery at different SOCs, a second-order RC model is built in an electrochemical workstation, and the ohmic internal resistance value of the power battery at different SOCs is used as the initial value of the ohmic internal resistance in the second-order RC model, and different polarization parameters in the second-order RC model are iteratively fitted to obtain identification values ​​of different polarization parameters in the second-order RC model at different SOCs, thereby completing battery parameter identification; the polarization parameters include electrochemical polarization internal resistance, electrochemical polarization capacitance, concentration polarization internal resistance, and concentration polarization capacitance; Conduct mixed pulse power testing on power batteries, including: The power battery is fully charged by a constant voltage and current limiting method, so that the SOC value of the power battery reaches 100%, and the power battery is left to stand until the terminal voltage of the power battery is stable; Perform constant current discharge at a rate of 1C to reduce the SOC value of the power battery by 5%, and let it stand for 10 hours to stabilize the terminal voltage of the power battery; Discharge at a current pulse rate of 3C for 10 seconds and let stand for 5 hours to stabilize the terminal voltage of the power battery; Charge at a current pulse rate of 3C for 10 seconds and let it stand for 5 hours to stabilize the terminal voltage of the power battery. Repeat the charging and discharging operation until the SOC value of the power battery drops to 5%. When performing a mixed pulse power test on a power battery, each cycle is separated by a 5% SOC value.

2. The battery parameter identification method according to claim 1, characterized in that: The ohmic internal resistance values ​​of the power battery at different SOCs are used as the initial values ​​of the ohmic internal resistance in the second-order RC model, and different polarization parameters in the second-order RC model are iteratively fitted, specifically including: The ohmic internal resistance values ​​of the power battery at different SOCs are used as the initial values ​​of the ohmic internal resistance at different SOCs in the second-order RC model, the initial values ​​of the polarization parameters are set to 1, and the polarization parameters are iteratively fitted to obtain fitting values ​​of the polarization parameters; Obtaining fitting errors of the polarization parameters according to the fitting values ​​of the polarization parameters; Determining whether the fitting error of each polarization parameter meets a set range; If the fitting error satisfies the set range, the iterative fitting is stopped, and the fitting value of each polarization parameter is output as an identification value; If the fitting error does not meet the set range, each of the polarization parameters is reassigned, and the iterative fitting is continued until the fitting error meets the set range.

3. The battery parameter identification method according to claim 2, characterized in that: If the fitting error satisfies the set range, the iterative fitting is stopped, and the fitting value of each polarization parameter is output as an identification value, which specifically includes: If the fitting error of any polarization parameter in the polarization parameters does not exceed 5% in two consecutive rounds of fitting, the fitting value of the latest round of the corresponding polarization parameter is selected as the final identification value, and the iterative fitting is stopped.

4. The battery parameter identification method according to claim 2, characterized in that: If the fitting error does not meet the set range, each of the polarization parameters is reassigned, and the iterative fitting is continued until the fitting error meets the set range, specifically including: If the fitting error of any polarization parameter in the polarization parameters exceeds 10% in two consecutive rounds of fitting, the average of the fitting values ​​of the two consecutive corresponding polarization parameters is selected as the fitting value assignment for the next round, and the fitting operation is repeated; If the fitting error of any polarization parameter in the polarization parameters does not exceed 10% in two consecutive rounds of fitting, the fitting value of the corresponding polarization parameter in the previous round is selected as the assignment for the next round of fitting, and the fitting operation is repeated.

5. The battery parameter identification method according to claim 1, characterized in that: After completing the battery parameter identification, it also includes: The open circuit voltage values, ohmic internal resistance values ​​corresponding to different SOCs, and the identification values ​​of different polarization parameters in the second-order RC model under different SOCs are imported into Simulink in the form of MAP to build a one-dimensional dynamic simulation model of the power battery; the one-dimensional dynamic simulation model of the power battery is used to estimate the state of the battery and simulate the electrical characteristics of the battery.

6. The battery parameter identification method according to claim 5, characterized in that: After obtaining the one-dimensional dynamic simulation model of the power battery, it also includes: Determining a terminal voltage simulation result of the current power battery according to the one-dimensional dynamic simulation model of the power battery; The actual value of the terminal voltage of the current power battery obtained by experimental sampling is obtained, and the terminal voltage simulation result is compared with the actual value of the terminal voltage to verify the one-dimensional dynamic simulation model of the power battery.

7. A computer system comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the battery parameter identification method according to any one of claims 1 to 6.

8. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the battery parameter identification method described in any one of claims 1 to 6 is implemented.

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