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CN-122017573-A - Offline identification method and system for parameters of second-order equivalent model of lithium battery

CN122017573ACN 122017573 ACN122017573 ACN 122017573ACN-122017573-A

Abstract

The embodiment of the invention provides a method and a system for identifying parameters of a second-order equivalent model of a lithium battery in an off-line manner, and belongs to the technical field of new energy batteries. The identification method comprises the steps of constructing a second-order circuit equivalent model, obtaining HPPC test working conditions to determine open-circuit voltage, SOC, terminal voltage and current data of a lithium battery, constructing an observation equation according to the second-order circuit equivalent model, carrying out linearization processing on the observation equation and obtaining a jacobian matrix, constructing a least square method identification model with forgetting factors 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 invention, the polarized capacitor and the polarization resistor are fitted by the model through the least square method with the forgetting factor, so that the identification precision of parameters is improved, and the high-precision SOC estimation requirement is met.

Inventors

  • ZHANG WEI
  • JI XIANG
  • ZENG GUOJIAN
  • YANG YANHUI
  • ZHAO ZHIPENG

Assignees

  • 安徽锐能科技有限公司

Dates

Publication Date
20260512
Application Date
20251205

Claims (9)

  1. 1. The method for identifying the parameters of the second-order equivalent model of the lithium battery in an off-line manner is characterized by comprising the following steps of: constructing a second-order circuit equivalent model; Acquiring HPPC test working conditions 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; Linearizing the observation equation and obtaining a jacobian matrix; Constructing a least square method identification model with forgetting factors according to the jacobian matrix; And acquiring identification parameters according to the open-circuit voltage, the SOC, the terminal voltage, the current data and the least square method identification model.
  2. 2. The method of claim 1, wherein obtaining HPPC test conditions to determine open circuit voltage, SOC, terminal voltage, and current data for the lithium battery comprises: Performing HPPC test on the lithium battery in a laboratory environment; Acquiring voltage, current and temperature data of the lithium battery at different temperatures, SOCs and charge-discharge multiplying powers; Taking the average voltage in the first time threshold at the tail end of the static working condition after charge and discharge as the open circuit voltage corresponding to the SOC; Drawing an OCV-DOD curve according to the open circuit voltage and the SOC; And drawing a voltage response curve according to the terminal voltage to calculate the ohmic internal resistance.
  3. 3. The identification method according to claim 2, wherein plotting the voltage response curve according to the terminal voltage to calculate the ohmic internal resistance comprises: The ohmic internal resistance is calculated according to formula (1), ,(1), Wherein, the For the terminal voltage in a stationary state before the voltage drops, For the terminal voltage of the fourth sampling data sampling point after discharge, Is ohmic in resistance.
  4. 4. A method of identification as claimed in claim 3 wherein constructing an observation equation from the second-order circuit equivalent model comprises: Obtaining an observation equation according to formulas (2) to (4), ,(2) ,(3) ,(4) Wherein, the For the first polarization time constant, For the first polarization resistance, For the first polarized capacitance of the capacitor, For the second polarization time constant, For the second polarization resistance, the first polarization resistance, For the second polarized capacitance of the capacitor, As a voltage at the end of the line, Is the open circuit voltage of the power supply, In the event of a current flow, Is the sampling period.
  5. 5. The method of claim 4, wherein linearizing the observation equation and obtaining a jacobian matrix comprises: performing a first-order taylor series expansion on the terminal voltage according to the formula (5), ,(5) The jacobian matrix is obtained according to equation (6), ,(6) Wherein, the Is that The jacobian matrix of the time of day, For the terminal voltage actually measured at the present moment, For the partial derivative of the identification parameter at the previous time, As an identification parameter at the current moment of time, As the identification parameter of the last moment in time, In the form of a jacobian matrix, For the first polarized capacitor identified at the previous time, For the first polarization resistor identified at the previous time, The second polarization resistor identified at the previous time, For the second polarized capacitor identified at the previous time, Is the sampling period.
  6. 6. The method of claim 5, wherein constructing a least squares recognition model with forgetting factors from the jacobian matrix comprises: Obtaining the actually measured terminal voltage according to the formula (7), ,(7) Updating the identification parameters according to formula (8), ,(8) The gain matrix is calculated according to equation (9), ,(9) The covariance matrix is updated according to equation (10), ,(10) Wherein, the For the gain matrix at the current moment, As a forgetting factor, For the covariance matrix of the previous moment, Is the covariance matrix of the current moment.
  7. 7. The identification method according to claim 6, wherein acquiring identification parameters from open circuit voltage, SOC, terminal voltage, current data, and the least squares identification model comprises: Initializing a least square method identification model with forgetting factors, wherein the least square method identification model comprises the forgetting factors, a covariance matrix and initial identification parameters; performing parameter identification according to the least square method identification module to obtain identification parameters; And predicting terminal voltage according to the identification parameters.
  8. 8. An offline identification system for parameters of a second-order equivalent model of a lithium battery, characterized in that the system comprises a processor for executing the identification method according to any one of claims 1 to 7.
  9. 9. A computer-readable storage medium storing instructions for being read by a machine to cause the machine to perform the identification method of any one of claims 1 to 7.

Description

Offline identification method and system for parameters of second-order equivalent model of lithium battery Technical Field The invention relates to the technical field of new energy batteries, in particular to a method and a system for identifying parameters of a second-order equivalent model of a lithium battery in an off-line manner. Background Lithium batteries are used as core components of new energy automobiles and energy storage systems, and accurate modeling of the lithium batteries is important for state estimation (such as state of charge (SOC) and state of health (SOH)) of a Battery Management System (BMS). The second-order equivalent circuit model consists of an internal resistance, a voltage source and two RC parallel circuits connected in series, so that the complex dynamic characteristics (ohmic internal resistance and polarization effect) of the battery can be accurately described, the calculation is easy, and the model becomes a mainstream modeling scheme. The model generally comprises an ohmic internal resistance R0, polarization capacitances C1 and C2 and polarization resistances R1 and R2. The conventional parameter identification method has the problems that the online identification relies on real-time data, the battery charge and discharge data is required to be continuously collected and calculated in real time, the calculation load is high, the influence of working condition fluctuation is large, the off-line method is insufficient in precision, the traditional off-line method based on single working condition (such as pulse test) is easy to cause large parameter dispersion due to insufficient consideration of dynamic characteristic coupling under multiple working conditions, multi-parameter coupling exists during parameter identification, decoupling difficulty is large, and parameter identification errors are remarkable. Therefore, there is a need for an offline identification method that combines precision and efficiency, and realizes high-precision extraction of parameters of a second-order equivalent circuit model through multi-task data fusion and phased decoupling optimization. Disclosure of Invention The embodiment of the invention aims to provide a method and a system for identifying parameters of a second-order equivalent model of a lithium battery in an off-line manner, which solve the problem of low accuracy in identifying the parameters of the lithium battery. In order to achieve the above objective, an embodiment of the present invention provides an offline identification method for parameters of a second-order equivalent model of a lithium battery, the identification method comprising: constructing a second-order circuit equivalent model; Acquiring HPPC test working conditions 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; Linearizing the observation equation and obtaining a jacobian matrix; Constructing a least square method identification model with forgetting factors according to the jacobian matrix; And acquiring identification parameters according to the open-circuit voltage, the SOC, the terminal voltage, the current data and the least square method identification model. Optionally, obtaining HPPC test conditions to determine open circuit voltage, SOC, terminal voltage, and current data of the lithium battery includes: Performing HPPC test on the lithium battery in a laboratory environment; Acquiring voltage, current and temperature data of the lithium battery at different temperatures, SOCs and charge-discharge multiplying powers; Taking the average voltage in the first time threshold at the tail end of the static working condition after charge and discharge as the open circuit voltage corresponding to the SOC; Drawing an OCV-DOD curve according to the open circuit voltage and the SOC; And drawing a voltage response curve according to the terminal voltage to calculate the ohmic internal resistance. Optionally, plotting a voltage response curve according to the terminal voltage to calculate the ohmic internal resistance includes: The ohmic internal resistance is calculated according to formula (1), ,(1), Wherein, the For the terminal voltage in a stationary state before the voltage drops,For the terminal voltage of the fourth sampling data sampling point after discharge,Is ohmic in resistance. Optionally, constructing an observation equation according to the second-order circuit equivalent model includes: Obtaining an observation equation according to formulas (2) to (4), ,(2) ,(3) ,(4) Wherein, the For the first polarization time constant,For the first polarization resistance,For the first polarized capacitance of the capacitor,For the second polarization time constant,For the second polarization resistance, the first polarization resistance,For the second polarized capacitance of the capacitor,As a voltage at the end o