Lithium battery second-order equivalent model parameter off-line identification method and system

Abstract

The embodiment of the invention provides a lithium battery second-order equivalent model parameter off-line identification method and system, and belongs to the technical field of new energy batteries. The identification method comprises the following steps: constructing a second-order circuit equivalent model; acquiring an HPPC test condition to determine open-circuit voltage, SOC, terminal voltage and current data of the lithium battery; constructing an observation equation according to the second-order circuit equivalent model; performing linearization processing on the observation equation and obtaining a Jacobian matrix; constructing a least square method recognition model with a forgetting factor according to the Jacobian matrix; and obtaining identification parameters according to the open-circuit voltage, the SOC, the terminal voltage, the current data and the least square method identification model. According to the method, the polarization capacitance and the polarization resistance are fitted by the model through the least square method with the forgetting factor, the parameter identification precision is improved, and the high-precision SOC estimation requirement is met.

Description

Technical Field

[0001] This invention relates to the field of new energy battery technology, specifically to an offline identification method and system for second-order equivalent model parameters of lithium batteries. Background Technology

[0002] As a core component of new energy vehicles and energy storage systems, the accurate modeling of lithium batteries is crucial for the state estimation of the Battery Management System (BMS) (such as State of Charge (SOC) and State of Health (SOH)). The second-order equivalent circuit model, consisting of an internal resistance, a voltage source, and two series-connected parallel RC circuits, accurately describes the complex dynamic characteristics of the battery (ohmic internal resistance, polarization effect) and is easy to calculate, making it the mainstream modeling scheme. This model typically includes: ohmic internal resistance R0, polarization capacitors C1 and C2, and polarization resistors R1 and R2.

[0003] Current parameter identification methods suffer from the following problems: online identification relies on real-time data, requiring continuous acquisition and real-time calculation of battery charging and discharging data, resulting in high computational load and significant susceptibility to fluctuations in operating conditions; offline methods lack accuracy, and traditional offline methods based on a single operating condition (such as pulse testing) fail to adequately consider the dynamic coupling under multiple operating conditions, leading to large parameter dispersion. Furthermore, multi-parameter coupling exists during parameter identification, making decoupling difficult and resulting in significant parameter identification errors. Therefore, there is an urgent need for an offline identification method that balances accuracy and efficiency, achieving high-precision extraction of parameters from a second-order equivalent circuit model through multi-operating-condition data fusion and phased decoupling optimization. Summary of the Invention

[0004] The purpose of this invention is to provide an offline identification method and system for second-order equivalent model parameters of lithium batteries, which solves the problem of low accuracy in lithium battery parameter identification.

[0005] To achieve the above objectives, embodiments of the present invention provide an offline identification method for second-order equivalent model parameters of lithium batteries, the identification method comprising: Construct an equivalent model of a second-order circuit; Obtain HPPC test conditions to determine the open-circuit voltage, SOC, terminal voltage, and current data of the lithium battery; The observation equation is constructed based on the second-order circuit equivalent model. The observation equation is linearized and the Jacobian matrix is ​​obtained. Construct a least-squares recognition model with a forgetting factor based on the Jacobian matrix; Identification parameters are obtained based on the open-circuit voltage, SOC, terminal voltage, current data, and the least squares identification model.

[0006] Optionally, HPPC test conditions are acquired to determine the open-circuit voltage, SOC, terminal voltage, and current data of the lithium battery, including: HPPC testing of lithium batteries was conducted in a laboratory environment. Obtain voltage, current, and temperature data of lithium batteries at different temperatures, SOCs, and charge / discharge rates; The average voltage within the first time threshold after charging and discharging and the static condition is taken as the open circuit voltage corresponding to SOC. Plot the OCV-DOD curve based on the open-circuit voltage and SOC; The ohmic internal resistance is calculated by plotting the voltage response curve based on the terminal voltage.

[0007] Optionally, the voltage response curve is plotted based on the terminal voltage to calculate the ohmic internal resistance, including: Calculate the ohmic resistance according to formula (1). ,(1) in, This is the terminal voltage at rest before the voltage drops. This represents the terminal voltage at the fourth sampling point after discharge. It is the internal resistance of the Ohm.

[0008] Optionally, the observation equations are constructed based on the second-order circuit equivalent model, including: The observation equations are obtained according to formulas (2) to (4). (2) (3) (4) in, The first polarization time constant, For the first polarization resistor, This is the first polarization capacitor. The second polarization time constant, This is the second polarization resistor. This is the second polarization capacitor. Terminal voltage, Open circuit voltage, For current, The sampling period.

[0009] Optionally, the observation equation is linearized and the Jacobian matrix is ​​obtained, including: According to formula (5), the terminal voltage is expanded using a first-order Taylor series. (5) Obtain the Jacobian matrix according to formula (6). (6) in, for Jacobian matrix at time, This is the actual measured terminal voltage at the current moment. The partial derivatives of the parameters identified at the previous time step. These are the identification parameters at the current moment. These are the identification parameters from the previous moment. For Jacobian matrices, This refers to the first polarization capacitor identified at the previous moment. The first polarization resistor identified at the previous moment. The second polarization resistor identified at the previous moment. This is the second polarization capacitor identified in the previous moment. The sampling period.

[0010] Optionally, a least-squares recognition model with a forgetting factor is constructed based on the Jacobian matrix, including: The measured terminal voltage is obtained according to formula (7). (7) Update the identification parameters according to formula (8). (8) Calculate the gain matrix according to formula (9). (9) Update the covariance matrix according to formula (10). (10) in, This is the gain matrix at the current time. Forgetting factor, Let be the covariance matrix of the previous time step. Let be the covariance matrix at the current time.

[0011] Optionally, identification parameters are obtained based on open-circuit voltage, SOC, terminal voltage, current data, and the least squares identification model, including: Initialize the least squares recognition model with a forgetting factor, including initializing the forgetting factor, covariance matrix, and initial identification parameters; The least squares method is used to identify the parameters to obtain the identification parameters. The terminal voltage is predicted based on the identification parameters.

[0012] On the other hand, the present invention also provides an offline identification system for second-order equivalent model parameters of lithium batteries, the system including a processor for executing any of the identification methods described above.

[0013] In another aspect, the present invention also provides a computer-readable storage medium storing instructions for being read by a machine to cause the machine to perform any of the identification methods described above.

[0014] Through the above technical solution, this invention provides an offline identification method and system for second-order equivalent model parameters of lithium batteries. Firstly, the second-order equivalent circuit more realistically simulates the battery's polarization effect and dynamic response. The terminal voltage and current data obtained from HPPC testing provide a high-precision measured benchmark for model parameter identification, ensuring the reliability of the subsequent identification process. The observation equations constructed based on the second-order equivalent circuit model mathematically represent the battery's dynamic behavior in state-space form, laying a theoretical foundation for parameter identification. Compared with existing technologies, this invention's least squares identification model with a forgetting factor, constructed based on the Jacobian matrix, achieves real-time tracking of battery parameters by assigning higher weights to recent data. By fitting the polarization capacitance and polarization resistance using the least squares method with a forgetting factor, the accuracy of parameter identification is improved, meeting the requirements for high-precision SOC estimation.

[0015] Other features and advantages of the embodiments of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0016] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings: Figure 1 This is a flowchart of an identification method according to an embodiment of the present invention; Figure 2 This is a flowchart of an HPPC test according to an embodiment of the present invention; Figure 3 This is an OCV-DOD curve diagram according to an embodiment of the present invention; Figure 4 This is a voltage response curve diagram according to an embodiment of the present invention; Figure 5 This is a circuit diagram of a second-order equivalent circuit model according to an embodiment of the present invention; Figure 6 This is a flowchart of obtaining the Jacobian matrix according to one embodiment of the present invention; Figure 7This is a flowchart of constructing a least squares identification model according to one embodiment of the present invention; Figure 8 This is a flowchart of parameter identification according to one embodiment of the present invention; Figure 9 This is a comparison diagram of the measured terminal voltage and the predicted terminal voltage according to one embodiment of the present invention.

[0017] Explanation of reference numerals in the attached figures Detailed Implementation

[0018] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the scope of the present invention.

[0019] It should be noted that the acquisition, transmission, storage, use, and processing of data in the technical solution of this application all comply with relevant laws and regulations. In the embodiments of this application, certain existing industry solutions such as software, components, and models may be mentioned. These should be considered exemplary, intended only to illustrate the feasibility of implementing the technical solution of this application, and do not imply that the applicant has already used or necessarily used such solutions.

[0020] Figure 1 This is a flowchart of an identification method according to an embodiment of the present invention, in which the identification method includes: In step S1, an equivalent model of a second-order circuit is constructed.

[0021] In step S2, the HPPC test conditions are acquired to determine the open-circuit voltage, SOC, terminal voltage, and current data.

[0022] In step S3, the observation equations are constructed based on the equivalent model of the second-order circuit.

[0023] In step S4, the observation equation is linearized and the Jacobian matrix is ​​obtained.

[0024] In step S5, a least squares identification model with a forgetting factor is constructed based on the Jacobian matrix.

[0025] In step S6, identification parameters are obtained based on open-circuit voltage, SOC, terminal voltage, current data, and the least squares identification model.

[0026] In steps S1 to S6, the second-order equivalent circuit firstly simulates the battery's polarization effect and dynamic response more realistically. The terminal voltage and current data obtained from HPPC testing provide a high-precision measured benchmark for model parameter identification, ensuring the reliability of the subsequent identification process. The observation equations constructed based on the second-order equivalent circuit model mathematically represent the battery's dynamic behavior in state-space form, laying a theoretical foundation for parameter identification. Compared with existing technologies, this invention's least squares identification model with a forgetting factor, constructed based on the Jacobian matrix, achieves real-time tracking of battery parameters by assigning higher weights to recent data. By fitting the polarization capacitance and polarization resistance using the least squares method with a forgetting factor, the accuracy of parameter identification is improved, meeting the requirements for high-precision SOC estimation.

[0027] In this embodiment, to capture changes in battery parameters, in addition to testing at different temperatures and charge / discharge rates during HPPC testing, variable SOC testing is also performed based on changes in the battery's theoretical voltage curve. In one example of the invention, the specific steps for performing multi-condition HPPC testing could be... Figure 2 The method shown. Figure 2 In addition, the identification method also includes: In step S21, the lithium battery is subjected to HPPC testing in a laboratory environment.

[0028] In step S22, voltage, current, and temperature data of the lithium battery at different temperatures, SOCs, and charge / discharge rates are acquired. The charge / discharge current for the HPPC experiment is determined based on the capacity of the lithium iron phosphate battery. Considering that the polarization effect of the battery will be aggravated at high rates, making it difficult to fit the parameters, 0.33C, 0.5C, 1C, 1.5C, 2C, and 3C rates are selected as the experimental test rates. Based on the battery's recommended charge / discharge temperatures, 0℃, 5℃, 10℃, 15℃, 25℃, 35℃, and 45℃ are selected as the experimental test temperatures.

[0029] Taking discharge as an example, the OCV-DOD curve of lithium iron phosphate batteries is analyzed as follows: Figure 3 As shown, based on the magnitude of voltage change, upper and lower platforms are defined, with the area between the two platforms being the variation zone. Since the battery voltage does not change significantly in the platform region, the State of Charge (SOC) is tested in 5% increments within both platform regions. In the middle and end regions, the battery voltage changes more significantly with the charge level, so the SOC is tested in 1% increments within these ranges. This testing method fully reflects the battery's parameter variation patterns and better characterizes its features.

[0030] To improve the accuracy of subsequent parameter identification during testing, the data sampling frequency should be 100Hz for the first 30 seconds of discharge, and 10Hz for discharge data after 30 seconds. To reduce data redundancy, the sampling frequency for static condition data should be 1Hz. Furthermore, to improve parameter accuracy and reduce testing errors, the same condition should be tested three times.

[0031] In step S23, the average voltage within the first time threshold after the charging and discharging resting condition is taken as the open-circuit voltage corresponding to the SOC. The first time threshold is 10 seconds. Figure 4 The figure shows the voltage response curve during the HPPC discharge experiment. Segment AB represents the open-circuit voltage after the previous discharge stage ends and the battery is left to rest. This data can be used to calculate the battery's OCV data. Taking discharge as an example, the data from the full charge to the empty discharge portion of the discharge data is extracted as valid data for parameter identification. Because the depolarization is more complete at the end of the resting period, the average voltage within 10 seconds at the end of each resting period is taken as the OCV value. From this, the OCV-SOC data table for each operating condition can be obtained.

[0032] In step S24, the OCV-DOD curve is plotted based on the open-circuit voltage and SOC.

[0033] In step S25, a voltage response curve is plotted based on the terminal voltage to calculate the ohmic internal resistance.

[0034] In steps S21 to S25, the battery is tested under hybrid pulse power characteristic (HPPC) conditions in a laboratory environment, and data such as voltage, current, and temperature are recorded at different temperatures, states of charge (SOC), and charge / discharge rates. Target data for fully charged or fully discharged conditions are selected from the data, and the average voltage within the last 10 seconds of the resting condition after charging / discharging is taken as the open-circuit voltage (OCV) corresponding to that SOC, laying the foundation for subsequent parameter identification.

[0035] Furthermore, the ohmic internal resistance of the battery can be calculated using the falling edge of the discharge voltage or the rising edge of the charge voltage. For example... Figure 4 The sudden drop in voltage in segment BC is caused by the ohmic internal resistance R0 at the initial moment of discharge. Data from this stage can be used to calculate the battery's ohmic internal resistance. Since the effect of the ohmic internal resistance on the battery occurs at the moment of discharge, the data sampling frequency at that moment needs to be increased; otherwise, the characteristic curve of the ohmic internal resistance cannot be captured. In this embodiment, the effective data segment for calculating the ohmic internal resistance is from data point U0 just before discharge to the fourth sampling data point U after discharge. I4 The voltage difference during this time period is calculated as the voltage drop across the ohmic internal resistance. Then, combined with the average current I during this period, the ohmic internal resistance R0 of the battery is calculated. Specifically, this includes: Calculate the ohmic resistance according to formula (1). ,(1) in, This is the terminal voltage at rest before the voltage drops. The terminal voltage at point C, the fourth sampling point after discharge, represents the point where the voltage changes from a sudden change to a slow change. For ohmic internal resistance, This represents the average current.

[0036] In one example of this invention, the equivalent circuit model of a lithium battery can typically be constructed by a resistor and multiple parallel resistors and capacitors connected in series. As the number of capacitors increases, the accuracy and complexity of the equivalent circuit model of the lithium battery also increase. Therefore, considering both the accuracy and complexity of the equivalent circuit model of the lithium battery, a second-order equivalent circuit model can be constructed. Specifically, as... Figure 5 As shown, the constructed second-order equivalent circuit model includes an ideal voltage source Uoc, an ohmic internal resistance R0, a first polarization resistor R1, a second polarization resistor R2, a first polarization capacitor C1, and a second polarization capacitor C2. One end of the ohmic internal resistance R0 is connected to the ideal voltage source Uoc; one end of the first polarization resistor R1 is connected to the other end of the ohmic resistor R0; the other end of the first polarization resistor R1 is connected to one end of the second polarization resistor R2; one end of the first polarization capacitor C1 is connected to the other end of the ohmic internal resistance R0; the other end of the first polarization capacitor C1 is connected to one end of the second polarization capacitor C2; and the other end of the second polarization resistor R2 is connected to the other end of the second polarization capacitor C2. Further, observation equations are constructed based on the second-order circuit equivalent model, including: The observation equations are obtained according to formulas (2) to (4). (2) (3) (4) in, The first polarization time constant, For the first polarization resistor, This is the first polarization capacitor. The second polarization time constant, This is the second polarization resistor. This is the second polarization capacitor. Terminal voltage, Open circuit voltage, For current, This is the sampling period. Taking discharge as an example, such as... Figure 4The voltage change in segment CD is caused by the battery's first polarization capacitor C1, second polarization capacitor C2, first polarization resistor R1, and second polarization resistor R2. This stage can be fitted using the least squares method to the polarization capacitors and resistors. The parameters to be identified in the above formula are... .

[0037] Furthermore, in this implementation, in recursive least squares (RLS), each iteration requires... State matrix at time step Calculations yielded The state matrix at time t, also known as the Jacobian matrix. Specifically, as shown... Figure 6 As shown, it includes: In step S41, the terminal voltage is expanded using a first-order Taylor series according to formula (5). (5) (11) (12) (13) (14) In step S42, the Jacobian matrix is ​​obtained according to formula (6). (6) in, for Jacobian matrix at time, This is the actual measured terminal voltage at the current moment. The partial derivatives of the parameters identified at the previous time step. These are the identification parameters at the current moment. These are the identification parameters from the previous moment. For Jacobian matrices, This refers to the first polarization capacitor identified at the previous moment. The first polarization resistor identified at the previous moment. The second polarization resistor identified at the previous moment. This is the second polarization capacitor identified in the previous moment. The sampling period is For sampling period and correspond.

[0038] In this embodiment, the steps for constructing the least squares identification model can be of various kinds known to those skilled in the art. In one example of the present invention, in order to improve data tracking performance and convergence speed, a forgetting factor is introduced during the parameter identification process. To reallocate the weights of old and new data during the recursive process, the specific steps for constructing a least squares identification model can be... Figure 7 The method shown. Figure 7 In addition, the identification method also includes: In step S51, the measured terminal voltage is obtained according to formula (7). (7) In step S52, the identification parameters are updated according to formula (8). (8) In step S53, the gain matrix is ​​calculated according to formula (9). (9) In step S54, the covariance matrix is ​​updated according to formula (10). (10) in, This is the gain matrix at the current time. Forgetting factor, Let be the covariance matrix of the previous time step. Let be the covariance matrix at the current time.

[0039] In this embodiment, the parameter identification step can be one of several methods known to those skilled in the art. In one example of the present invention, the specific parameter identification step can be... Figure 8 The method shown. Figure 8 In addition, the identification method also includes: In step S61, the least squares identification model with forgetting factor is initialized, including initializing the forgetting factor, covariance matrix, and initial identification parameters.

[0040] In step S62, parameter identification is performed using the least squares method to obtain the identification parameters. In the specific parameter identification process, a forgetting factor needs to be set first. Considering the accuracy of parameter identification and the convergence speed, the forgetting factor is set to 0.99. Then, the initial guess values ​​required for least squares are set. Covariance Matrix Perform parameter fitting. It is worth noting that, in Figure 4 In the polarization parameter identification process, the effective data should be the data of 80% CD segment, that is, the data segment starts from the fourth sampling data point UI4 after discharge and ends at the end of the total number of data points of 80% CD segment. In other words, the effective data is from 3375 seconds to 4599.8 seconds. This operation can avoid the overfitting problem caused by taking all data points and the underfitting problem caused by taking a small number of data points.

[0041] In step S63, the terminal voltage is predicted based on the identification parameters.

[0042] In steps S61 to S63, a larger number of iterations can be set to improve the accuracy of the fitted data. After fitting, the required polarization capacitance and polarization resistance can be obtained, and the terminal voltage can be predicted based on the polarization capacitance and polarization resistance. This invention reduces parameter coupling interference through staged calculation. The purpose of using staged calculation is to reduce parameter estimation coupling and improve the accuracy of parameter identification. The ohmic internal resistance of the battery is crucial to the accuracy of battery model estimation. This patent first calculates and confirms the ohmic internal resistance of the battery separately, avoiding the coupling effect with polarization internal resistance and polarization capacitance when estimating by least squares, thus improving the accuracy of ohmic internal resistance calculation. Subsequently, the pre-calculated ohmic internal resistance is substituted into least squares to estimate polarization internal resistance and polarization capacitance, which also improves the accuracy of polarization parameter estimation to a certain extent.

[0043] To verify the accuracy of the identification parameters, RMSE and MAE are calculated on the identified results to ensure that the parameters do not exhibit outliers and maintain high overall accuracy throughout the identification process. If there are data points with large errors, the initial guess values ​​for the corresponding data can be adjusted for correction, ensuring that the calculated RMSE after identification is less than 0.01V and the MAE is less than 0.005V. Specifically, this may include: The estimated error voltage is calculated according to formula (15). (15) in, To estimate the error voltage, This is to estimate the voltage, i.e., the predicted terminal voltage. All parameters in the final output should be the average of three test identifications, such as... Figure 9 This is a comparison chart of the measured voltage and the estimated voltage, that is, a comparison chart of the measured terminal voltage and the predicted terminal voltage. For discharge, the overall error during the discharge stage is stable within 10mV.

[0044] On the other hand, the present invention also provides an offline identification system for second-order equivalent model parameters of lithium batteries, the system including a processor for executing any of the identification methods described above.

[0045] In another aspect, the present invention also provides a computer-readable storage medium storing instructions for being read by a machine to cause the machine to perform any of the identification methods described above.

[0046] Through the above technical solution, this invention provides an offline identification method and system for second-order equivalent model parameters of lithium batteries. Firstly, the second-order equivalent circuit more realistically simulates the battery's polarization effect and dynamic response. The terminal voltage and current data obtained from HPPC testing provide a high-precision measured benchmark for model parameter identification, ensuring the reliability of the subsequent identification process. The observation equations constructed based on the second-order equivalent circuit model mathematically represent the battery's dynamic behavior in state-space form, laying a theoretical foundation for parameter identification. Compared with existing technologies, this invention's least squares identification model with a forgetting factor, constructed based on the Jacobian matrix, achieves real-time tracking of battery parameters by assigning higher weights to recent data. By fitting the polarization capacitance and polarization resistance using the least squares method with a forgetting factor, the accuracy of parameter identification is improved, meeting the requirements for high-precision SOC estimation.

[0047] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0048] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0049] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0050] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0051] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0052] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0053] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0054] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0055] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for offline identification of parameters of a second-order equivalent model of a lithium battery, characterized in that, The identification method includes: Construct an equivalent model of a second-order circuit; Obtain HPPC test conditions to determine the open-circuit voltage, SOC, terminal voltage, and current data of the lithium battery; The observation equation is constructed based on the second-order circuit equivalent model. The observation equation is linearized and the Jacobian matrix is ​​obtained. Construct a least-squares recognition model with a forgetting factor based on the Jacobian matrix; Identification parameters are obtained based on the open-circuit voltage, SOC, terminal voltage, current data, and the least squares identification model.

2. The identification method according to claim 1, characterized in that, Obtain HPPC test conditions to determine the open-circuit voltage, state of charge (SOC), terminal voltage, and current data of the lithium battery, including: HPPC testing of lithium batteries was conducted in a laboratory environment. Obtain voltage, current, and temperature data of lithium batteries at different temperatures, SOCs, and charge / discharge rates; The average voltage within the first time threshold after charging and discharging and the static condition is taken as the open circuit voltage corresponding to SOC. Plot the OCV-DOD curve based on the open-circuit voltage and SOC; The ohmic internal resistance is calculated by plotting the voltage response curve based on the terminal voltage.

3. The identification method according to claim 2, characterized in that, The voltage response curve is plotted based on the terminal voltage to calculate the ohmic internal resistance, including: Calculate the ohmic resistance according to formula (1). ，（1）， in, This is the terminal voltage at rest before the voltage drops. This represents the terminal voltage at the fourth sampling point after discharge. It is the internal resistance of the Ohm.

4. The identification method according to claim 3, characterized in that, The observation equations are constructed based on the equivalent model of the second-order circuit, including: The observation equations are obtained according to formulas (2) to (4). ，（2） ，（3） ，（4） in, The first polarization time constant, For the first polarization resistor, This is the first polarization capacitor. The second polarization time constant, This is the second polarization resistor. This is the second polarization capacitor. Terminal voltage, Open circuit voltage, For current, The sampling period.

5. The identification method according to claim 4, characterized in that, The observation equation is linearized and the Jacobian matrix is ​​obtained, including: According to formula (5), the terminal voltage is expanded using a first-order Taylor series. ，（5） Obtain the Jacobian matrix according to formula (6). ，（6） in, for Jacobian matrix at time, This is the actual measured terminal voltage at the current moment. The partial derivatives of the parameters identified at the previous time step. These are the identification parameters at the current moment. These are the identification parameters from the previous moment. For Jacobian matrices, This refers to the first polarization capacitor identified at the previous moment. The first polarization resistor identified at the previous moment. The second polarization resistor identified at the previous moment. This is the second polarization capacitor identified in the previous moment. The sampling period.

6. The identification method according to claim 5, characterized in that, Based on the Jacobian matrix, a least-squares recognition model with a forgetting factor is constructed, including: The measured terminal voltage is obtained according to formula (7). ，（7） Update the identification parameters according to formula (8). ，（8） Calculate the gain matrix according to formula (9). ，（9） Update the covariance matrix according to formula (10). ，（10） in, This is the gain matrix at the current time. Forgetting factor, Let be the covariance matrix of the previous time step. Let be the covariance matrix at the current time.

7. The identification method according to claim 6, characterized in that, Identification parameters are obtained based on open-circuit voltage, state of charge (SOC), terminal voltage, current data, and the least squares identification model, including: Initialize the least squares recognition model with a forgetting factor, including initializing the forgetting factor, covariance matrix, and initial identification parameters; The least squares method is used to identify the parameters to obtain the identification parameters. The terminal voltage is predicted based on the identification parameters.

8. An offline identification system for parameters of a second-order equivalent model of a lithium battery, characterized in that, The system includes a processor for performing the identification method as claimed in any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that are read by a machine to cause the machine to perform the identification method as described in any one of claims 1 to 7.

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