Battery internal resistance calculation method and device

Abstract

The invention discloses a battery internal resistance calculation method and device, and the method comprises the steps: obtaining the voltage and current of a battery in real time, inputting the voltage and current into a pre-constructed first online parameter identification model, and obtaining a first battery working condition parameter; inputting the voltage, the current and the first battery working condition parameter into a pre-constructed Kalman filtering model to obtain a state of charge (SOC) estimated value and a polarization voltage estimated value; constructing a second online parameter identification model based on the state equation and the observation equation; inputting the SOC estimated value, the polarization voltage estimated value, the voltage and the current into the second online parameter identification model to obtain a second battery working condition parameter; and determining the total internal resistance of the battery according to the second battery working condition parameter. The battery internal resistance calculation method can estimate accurate battery internal resistance.

Description

Technical Field

[0001] This invention relates to the field of power technology, and in particular to a method and apparatus for calculating the internal resistance of a battery. Background Technology

[0002] Battery internal resistance refers to the resistance encountered when current flows through the battery's interior during operation. It includes ohmic internal resistance and polarization internal resistance, and is an important indicator of battery performance. Since battery internal resistance gradually increases with battery aging, a Battery Management System (BMS) dynamically estimates this internal resistance. This can be used to predict battery health, identify abnormal aging processes, monitor battery consistency to prevent overcharging and over-discharging, thereby slowing down battery aging and preventing safety accidents.

[0003] Equivalent circuit models for lithium batteries include Rint, first-order RC, and second-order RC. The Rint model treats the battery as an ideal voltage source connected in series with an internal resistance. The first-order RC model includes one resistor and one capacitor, and the second-order RC model includes two resistors and two capacitors, etc. Currently, classic battery internal resistance algorithms include: total internal resistance algorithms based on the Rint model, offline parameter identification (also known as the least squares method) based on first-order / multi-order RC models, and online parameter identification (also known as the recursive least squares method). Offline parameter identification fits the entire model at once, estimating model parameters by minimizing the sum of squared residuals. Online parameter identification, based on offline parameter identification, updates the parameter estimates step by step using a recursive formula. Online parameter identification has advantages such as real-time monitoring, high accuracy, high adaptability, and ease of operation. However, in practical applications with large current fluctuations and high noise levels, online parameter identification cannot accurately identify the internal resistance of lithium iron phosphate batteries with voltage plateau regions. Summary of the Invention

[0004] The purpose of this invention is to provide a battery internal resistance calculation method, apparatus, and electronic device that can solve the problem in the prior art where online parameter identification cannot accurately identify the internal resistance of lithium iron phosphate batteries with voltage plateau regions in practical application scenarios with large current fluctuations and high noise.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0006] This invention provides a method for calculating the internal resistance of a battery, wherein the method includes:

[0007] The voltage and current of the battery are acquired in real time, and the voltage and current are input into a pre-constructed first online parameter identification model to obtain the first battery operating condition parameters, wherein the first battery operating condition parameters include: first open circuit voltage, first ohmic internal resistance, first polarization internal resistance and first polarization capacitance;

[0008] The voltage, the current, and the first battery operating parameters are input into a pre-constructed Kalman filter model to obtain the state of charge (SOC) estimate and polarization voltage estimate; wherein, the Kalman filter model includes: state equations and observation equations;

[0009] Based on the state equation and observation equation, a second online parameter identification model is constructed;

[0010] The SOC estimate, the polarization voltage estimate, the voltage, and the current are input into the second online parameter identification model to obtain the second battery operating condition parameters; wherein, the second battery operating condition parameters include: second open circuit voltage, second ohmic internal resistance, second polarization internal resistance, and second polarization capacitance;

[0011] The total internal resistance of the battery is determined based on the second battery operating parameters.

[0012] Optionally, before the step of acquiring the battery voltage and current in real time and inputting the voltage and current into a pre-built first online parameter identification model to obtain the first battery operating condition parameters, the method further includes:

[0013] The state of charge (SOC) of the battery, determined by the ampere-hour integration method and the open-circuit voltage method, is substituted into the SOC-OCV table fitting function to obtain the target open-circuit voltage (OCV).

[0014] The mean absolute error of the least squares fitted voltage is calculated based on the OCV.

[0015] Choose the forgetting factor that minimizes the mean absolute error;

[0016] Construct a first online parameter identification model that includes the forgetting factor.

[0017] Optionally, the Kalman filter model uses SOC and polarization voltage as state vectors;

[0018] The Kalman filter model uses the ampere-hour integral method and the polarization voltage discrete time-domain calculation formula of the first online parameter identification model as the state equation;

[0019] The Kalman filter model uses the voltage observation equation, which is equal to the product of the open-circuit voltage, the polarization voltage, and the current and the ohmic internal resistance.

[0020] Optionally, the step of determining the total internal resistance of the battery based on the second battery operating parameters includes:

[0021] The sum of the second ohmic internal resistance and the second polarization internal resistance is determined as the total internal resistance of the battery.

[0022] This invention also provides a battery internal resistance calculation device, wherein the device includes:

[0023] The first operating condition parameter calculation module is used to acquire the battery voltage and current in real time, and input the voltage and current into the pre-constructed first online parameter identification model to obtain the first battery operating condition parameters, wherein the first battery operating condition parameters include: first open circuit voltage, first ohmic internal resistance, first polarization internal resistance and first polarization capacitance.

[0024] The estimation calculation module is used to input the voltage, the current, and the first battery operating condition parameters into a pre-constructed Kalman filter model to obtain the state of charge (SOC) estimate and the polarization voltage estimate; wherein, the Kalman filter model includes: state equations and observation equations;

[0025] The first construction module is used to construct a second online parameter identification model based on the state equation and the observation equation;

[0026] The second operating condition parameter calculation module is used to input the SOC estimate, the polarization voltage estimate, the voltage, and the current into the second online parameter identification model to obtain the second battery operating condition parameters; wherein, the second battery operating condition parameters include: second open circuit voltage, second ohmic internal resistance, second polarization internal resistance, and second polarization capacitance;

[0027] The internal resistance calculation module is used to determine the total internal resistance of the battery based on the second battery operating parameters.

[0028] Optionally, the device further includes:

[0029] The target open-circuit voltage determination module is used to obtain the battery voltage and current in real time in the first operating condition parameter calculation module, and input the voltage and current into the pre-constructed first online parameter identification model to obtain the first battery operating condition parameters. Before that, the battery state of charge (SOC) determined based on the ampere-hour integration method and the open-circuit voltage method is substituted into the SOC-OCV table fitting function to obtain the target open-circuit voltage (OCV).

[0030] The mean absolute error calculation module is used to calculate the mean absolute error of the least squares fitted voltage based on the OCV.

[0031] The selection module is used to select the forgetting factor that minimizes the mean absolute error.

[0032] The second building module is used to build a first online parameter identification model that includes the forgetting factor.

[0033] Optionally, the Kalman filter model uses SOC and polarization voltage as state vectors;

[0034] The Kalman filter model uses the ampere-hour integral method and the polarization voltage discrete time-domain calculation formula of the first online parameter identification model as the state equation;

[0035] The Kalman filter model uses the voltage observation equation, which is equal to the product of the open-circuit voltage, the polarization voltage, and the current and the ohmic internal resistance.

[0036] Optionally, the internal resistance calculation module is specifically used to: determine the sum of the second ohmic internal resistance and the second polarization internal resistance as the total internal resistance of the battery.

[0037] The battery internal resistance calculation scheme disclosed in this invention acquires the battery's voltage and current in real time, and inputs the voltage and current into a pre-constructed first online parameter identification model to obtain first battery operating condition parameters. The voltage, current, and first battery operating condition parameters are then input into a pre-constructed Kalman filter model to obtain a State of Charge (SOC) estimate and a polarization voltage estimate. Based on the state equation and observation equation, a second online parameter identification model is constructed. The SOC estimate, polarization voltage estimate, voltage, and current are input into the second online parameter identification model to obtain second battery operating condition parameters. Based on the second battery operating condition parameters, the total battery internal resistance is determined. Through the battery internal resistance calculation scheme disclosed in this invention, the extended Kalman filter can achieve the purpose of filtering noise and large current fluctuations using the ampere-hour integration method. The estimated SOC, input into the second online parameter identification model, can accurately estimate the total battery internal resistance. Attached Figure Description

[0038] Figure 1 This is a flowchart illustrating the steps of a battery internal resistance calculation method according to an embodiment of this application;

[0039] Figure 2 This is a flowchart illustrating the steps of another battery internal resistance calculation method according to an embodiment of this application;

[0040] Figure 3 This is a structural block diagram illustrating a battery internal resistance calculation device according to an embodiment of this application. Detailed Implementation

[0041] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0042] This invention provides estimated internal resistance parameters for the battery BMS management system. Based on the battery's external characteristics and traditional internal resistance estimation methods, a ternary / lithium iron phosphate battery internal resistance algorithm is designed, which integrates the recursive least squares method with forgetting factor and the extended Kalman filter method. This algorithm overcomes the problem that traditional online parameter identification cannot accurately identify the internal resistance of lithium iron phosphate batteries with voltage plateau regions in practical application scenarios with large current fluctuations and high noise.

[0043] To address the aforementioned problems, this invention provides an efficient and feasible solution. Based on a first-order RC equivalent circuit model, a recursive least squares method with a forgetting factor is constructed. The voltage and current are input to the first-line parameter identification model, and the identification result is used as the input to the extended Kalman filter algorithm to estimate the state of charge (SOC). The SOC, voltage, and current are then used as the input to the second recursive least squares method (i.e., as the input to the first-line parameter identification model) to identify the ohmic internal resistance and polarization internal resistance, thereby achieving the goal of accurately estimating the internal resistance of ternary / lithium iron phosphate batteries.

[0044] The battery internal resistance calculation method provided in this application will be described in detail below with reference to the accompanying drawings, through specific embodiments and application scenarios.

[0045] As attached Figure 1 As shown, the battery internal resistance calculation method of this application embodiment includes the following steps:

[0046] Step 101: Acquire the battery voltage and current in real time, and input the voltage and current into the pre-built first online parameter identification model to obtain the first battery operating condition parameters.

[0047] The first battery operating parameters include: first open-circuit voltage, first ohmic internal resistance, first polarization internal resistance, and first polarization capacitor.

[0048] Based on the discrete-time expression of a first-order RC (Resistor-Capacitance) circuit model, a recursive least squares model with a forgetting factor is constructed, ultimately forming the first online parameter identification model. A first-order RC circuit consists of a resistor and a capacitor. According to the resistor-capacitor arrangement, it can be divided into RC series circuits and RC parallel circuits; a simple RC parallel circuit cannot resonate because the resistor does not store energy, while an LC parallel circuit can resonate. RC circuits are widely used in analog circuits and pulse digital circuits. An RC parallel circuit, if connected in series, attenuates low-frequency signals; if connected in parallel, it attenuates high-frequency signals.

[0049] The first online parameter identification model is an existing model. For the specific structure and working principle of this first online parameter identification model, please refer to the existing relevant explanations, and it will not be repeated here.

[0050] In one optional embodiment, before acquiring the battery voltage and current in real time and inputting the voltage and current into a pre-built first online parameter identification model to obtain first battery operating condition parameters, the method includes:

[0051] The state of charge (SOC) of the battery, determined by the ampere-hour integration method and the open-circuit voltage method, is substituted into the SOC-OCV table fitting function to obtain the target open-circuit voltage (OCV). The mean absolute error of the least squares fitting voltage is calculated based on the OCV. The forgetting factor that minimizes the mean absolute error is selected. A first online parameter identification model containing the forgetting factor is constructed.

[0052] This method of correcting the battery's SOC based on the ampere-hour integration method can improve the accuracy of the output results of the first online parameter identification model.

[0053] Step 102: Input the voltage, current, and first battery operating condition parameters into the pre-built Kalman filter model to obtain the estimated state of charge (SOC) and polarization voltage.

[0054] The Kalman filter model includes state equations and observation equations. The Kalman filter model uses SOC and polarization voltage as state vectors; its state equations are based on the ampere-hour integral method and the discrete-time calculation formula for polarization voltage from the first online parameter identification model; and its voltage observation equations are based on the product of voltage, open-circuit voltage, polarization voltage, and current and ohmic resistance. In the Kalman filter model, SOC and polarization voltage are the state vectors, and open-circuit voltage is the dependent variable with SOC as the independent variable.

[0055] The Kalman filter model is a mathematical model based on the Kalman filtering method. The Kalman filtering method is a process of optimally estimating the state of a linear system. Extended Kalman filtering, based on this, is applicable to nonlinear systems. The working principle of the Kalman filtering method is as follows:

[0056] The state is estimated based on the state equation from the previous time step; the state covariance is updated using the state transition matrix and process noise; the Kalman gain is updated using the state covariance, observation matrix, and observation noise; the state is corrected using the Kalman gain, the deviation between the observed data and the predicted values ​​of the observation equation; and the state covariance is updated based on the Kalman gain and the observation matrix.

[0057] Since the state update process contains noise and the observed data contains noise, the Kalman optimal estimation can be regarded as a filtering process.

[0058] Step 103: Construct a second online parameter identification model based on the state equation and the observation equation.

[0059] The second online parameter identification model is an online parameter identification model that incorporates SOC. When constructing the model, it is based on the state equation and observation equation in the Kalman filter model. After discretization by Laplace transform, a recursive least squares model with a forgetting factor is constructed, and finally the second online parameter identification model is generated.

[0060] Step 104: Input the SOC estimate, polarization voltage estimate, voltage and current into the second online parameter identification model to obtain the second battery operating condition parameters.

[0061] The second battery operating parameters include: second open-circuit voltage, second ohmic internal resistance, second polarization internal resistance, and second polarization capacitor.

[0062] The second online parameter identification model has a similar structure and working principle to the first online parameter identification model, and can be referred to the existing relevant descriptions without further details.

[0063] Step 105: Determine the total internal resistance of the battery based on the second battery operating parameters.

[0064] In actual implementation, when determining the total internal resistance of the battery based on the second battery operating parameters, the sum of the second ohmic internal resistance and the second polarization internal resistance can be used as the total internal resistance of the battery.

[0065] The battery internal resistance calculation method provided in this application acquires the battery's voltage and current in real time, and inputs the voltage and current into a pre-constructed first online parameter identification model to obtain first battery operating condition parameters. The voltage, current, and first battery operating condition parameters are then input into a pre-constructed Kalman filter model to obtain a State of Charge (SOC) estimate and a polarization voltage estimate. Based on the state equation and observation equation, a second online parameter identification model is constructed. The SOC estimate, polarization voltage estimate, voltage, and current are input into the second online parameter identification model to obtain second battery operating condition parameters. Based on the second battery operating condition parameters, the total battery internal resistance is determined. Through the battery internal resistance calculation method disclosed in this invention, the extended Kalman filter can achieve the purpose of filtering noise and large current fluctuations using the ampere-hour integration method. The estimated SOC, input into the second online parameter identification model, can accurately estimate the total battery internal resistance.

[0066] The following reference Figure 2 The battery internal resistance calculation method provided in the embodiments of this application will be described.

[0067] The battery internal resistance calculation method provided in this application includes the following stages:

[0068] Phase 1: Constructing the online parameter identification model (i.e., the first online parameter identification model):

[0069] Based on the discrete-time expression of the first-order RC model, a recursive least squares model with a forgetting factor is constructed to generate an online parameter identification model.

[0070] This online parameter identification model uses real-time voltage and current as input data to perform online identification, and finally outputs open-circuit voltage, ohmic internal resistance, polarization internal resistance, and polarization capacitance.

[0071] Phase 2: Constructing the Extended Kalman Filter (EKF) model: Using the output of Phase 1 as input, and simultaneously inputting voltage and current, the final output is the estimated values ​​of SOC and polarization voltage.

[0072] The extended Kalman filter model is constructed as follows: with SOC and polarization voltage as state vectors, the ampere-hour integral method and the polarization voltage discrete time-domain calculation formula based on the first-order RC model as state equations, and voltage = open-circuit voltage + polarization voltage + current * ohmic internal resistance as observation equation, where open-circuit voltage is the dependent variable with SOC as the independent variable.

[0073] Phase 3: Constructing an online parameter identification model incorporating SOC (i.e., the second online parameter identification model):

[0074] Based on the state equation and observation equation of stage 2, a recursive least squares model with a forgetting factor is constructed through Laplace transform discretization, generating an online parameter identification model that incorporates SOC. Using SOC, voltage, and current as inputs, the model outputs open-circuit voltage, ohmic internal resistance, polarization internal resistance, and polarization capacitance. The total internal resistance is then obtained by adding the ohmic internal resistance and the polarization internal resistance.

[0075] Traditional battery internal resistance estimation relies solely on single online parameter identification, either using voltage and current as input data, or using SOC, voltage, and current as inputs. However, online parameter identification using voltage and current as inputs suffers from limited input data features and cannot accurately identify parameters with excessively low SOC in lithium iron phosphate batteries. This is due to factors such as noise and large current fluctuations. However, inaccurate parameters do not affect the accuracy of voltage fitting. The accuracy of the extended Kalman filter depends solely on the accuracy of the fitted voltage. The battery internal resistance calculation method provided in this specific embodiment achieves filtering through the ampere-hour integration method using extended Kalman filtering. The calculated SOC significantly aids in online parameter identification. The accurate total battery internal resistance is estimated using the online parameter identification method that inputs SOC, voltage, and current through a second online parameter identification model.

[0076] Figure 3 A structural block diagram of a battery internal resistance calculation device according to an embodiment of this application.

[0077] The battery internal resistance calculation device provided in this application includes the following functional modules:

[0078] The first operating condition parameter calculation module 301 is used to acquire the voltage and current of the battery in real time, and input the voltage and current into the pre-constructed first online parameter identification model to obtain the first battery operating condition parameters, wherein the first battery operating condition parameters include: first open circuit voltage, first ohmic internal resistance, first polarization internal resistance and first polarization capacitance.

[0079] The estimation calculation module 302 is used to input the voltage, the current and the first battery operating condition parameters into a pre-constructed Kalman filter model to obtain the state of charge (SOC) estimate and the polarization voltage estimate; wherein, the Kalman filter model includes: state equations and observation equations;

[0080] The first construction module 303 is used to construct a second online parameter identification model based on the state equation and the observation equation;

[0081] The second operating condition parameter calculation module 304 is used to input the SOC estimate, the polarization voltage estimate, the voltage and the current into the second online parameter identification model to obtain the second battery operating condition parameters; wherein, the second battery operating condition parameters include: second open circuit voltage, second ohmic internal resistance, second polarization internal resistance and second polarization capacitance;

[0082] The internal resistance calculation module 305 is used to determine the total internal resistance of the battery based on the second battery operating condition parameters.

[0083] Optionally, the device further includes:

[0084] The target open-circuit voltage determination module is used to obtain the battery voltage and current in real time in the first operating condition parameter calculation module, and input the voltage and current into the pre-constructed first online parameter identification model to obtain the first battery operating condition parameters. Before that, the battery state of charge (SOC) determined based on the ampere-hour integration method and the open-circuit voltage method is substituted into the SOC-OCV table fitting function to obtain the target open-circuit voltage (OCV).

[0085] The mean absolute error calculation module is used to calculate the mean absolute error of the least squares fitted voltage based on the OCV.

[0086] The selection module is used to select the forgetting factor that minimizes the mean absolute error.

[0087] The second building module is used to build a first online parameter identification model that includes the forgetting factor.

[0088] Optionally, the Kalman filter model uses SOC and polarization voltage as state vectors;

[0089] The Kalman filter model uses the ampere-hour integral method and the polarization voltage discrete time-domain calculation formula of the first online parameter identification model as the state equation;

[0090] The Kalman filter model uses the voltage observation equation, which is equal to the product of the open-circuit voltage, the polarization voltage, and the current and the ohmic internal resistance.

[0091] Optionally, the internal resistance calculation module is specifically used to: determine the sum of the second ohmic internal resistance and the second polarization internal resistance as the total internal resistance of the battery.

[0092] The battery internal resistance calculation device provided in this application embodiment acquires the battery voltage and current in real time, and inputs the voltage and current into a pre-constructed first online parameter identification model to obtain first battery operating condition parameters; inputs the voltage, current, and first battery operating condition parameters into a pre-constructed Kalman filter model to obtain a state of charge (SOC) estimate and a polarization voltage estimate; constructs a second online parameter identification model based on the state equation and observation equation; inputs the SOC estimate, polarization voltage estimate, voltage, and current into the second online parameter identification model to obtain second battery operating condition parameters; and determines the total battery internal resistance based on the second battery operating condition parameters. Through the battery internal resistance calculation scheme disclosed in this invention embodiment, the extended Kalman filter can achieve the purpose of filtering noise and large current fluctuations using the ampere-hour integration method. The estimated SOC, input into the second online parameter identification model, can accurately estimate the total battery internal resistance.

[0093] This invention also provides an electronic device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus.

[0094] Memory, used to store computer programs;

[0095] When the processor executes the program stored in the memory, it implements each step of the battery internal resistance calculation method executed by the host computer in the above method embodiment.

[0096] The communication bus mentioned above can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not mean that there is only one bus or one type of bus.

[0097] The communication interface is used for communication between the aforementioned terminal and other devices.

[0098] The memory may include random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0099] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0100] In another embodiment of the present invention, a computer-readable storage medium is also provided, which stores instructions that, when executed on a computer, cause the computer to implement the battery internal resistance calculation method steps described in any of the above embodiments.

[0101] It should be noted that, in this document, 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 a 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.

[0102] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

[0103] It should be noted that, in this document, 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 a 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.

[0104] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for calculating the internal resistance of a battery, characterized in that, The method includes: The voltage and current of the battery are acquired in real time, and the voltage and current are input into a pre-constructed first online parameter identification model to obtain the first battery operating condition parameters, wherein the first battery operating condition parameters include: first open circuit voltage, first ohmic internal resistance, first polarization internal resistance and first polarization capacitance; The voltage, the current, and the first battery operating parameters are input into a pre-constructed Kalman filter model to obtain the state of charge (SOC) estimate and polarization voltage estimate; wherein, the Kalman filter model includes: state equations and observation equations; Based on the state equation and observation equation, a second online parameter identification model is constructed; The SOC estimate, the polarization voltage estimate, the voltage, and the current are input into the second online parameter identification model to obtain the second battery operating condition parameters; wherein, the second battery operating condition parameters include: second open circuit voltage, second ohmic internal resistance, second polarization internal resistance, and second polarization capacitance; The total internal resistance of the battery is determined based on the second battery operating parameters.

2. The method according to claim 1, characterized in that, Before the step of acquiring the battery voltage and current in real time and inputting the voltage and current into a pre-built first online parameter identification model to obtain the first battery operating condition parameters, the method further includes: The state of charge (SOC) of the battery, determined by the ampere-hour integration method and the open-circuit voltage method, is substituted into the SOC-OCV table fitting function to obtain the target open-circuit voltage (OCV). The mean absolute error of the least squares fitted voltage is calculated based on the OCV. Choose the forgetting factor that minimizes the mean absolute error; Construct a first online parameter identification model that includes the forgetting factor.

3. The method according to claim 1, characterized in that: The Kalman filter model uses SOC and polarization voltage as state vectors; The Kalman filter model uses the ampere-hour integral method and the polarization voltage discrete time-domain calculation formula of the first online parameter identification model as the state equation; The Kalman filter model uses the voltage observation equation, which is equal to the product of the open-circuit voltage, the polarization voltage, and the current and the ohmic internal resistance.

4. The method according to claim 1, characterized in that, The step of determining the total internal resistance of the battery based on the second battery operating parameters includes: The sum of the second ohmic internal resistance and the second polarization internal resistance is determined as the total internal resistance of the battery.

5. A battery internal resistance calculation device, characterized in that, The device includes: The first operating condition parameter calculation module is used to acquire the battery voltage and current in real time, and input the voltage and current into the pre-constructed first online parameter identification model to obtain the first battery operating condition parameters, wherein the first battery operating condition parameters include: first open circuit voltage, first ohmic internal resistance, first polarization internal resistance and first polarization capacitance. The estimation calculation module is used to input the voltage, the current, and the first battery operating condition parameters into a pre-constructed Kalman filter model to obtain the state of charge (SOC) estimate and the polarization voltage estimate; wherein, the Kalman filter model includes: state equations and observation equations; The first construction module is used to construct a second online parameter identification model based on the state equation and the observation equation; The second operating condition parameter calculation module is used to input the SOC estimate, the polarization voltage estimate, the voltage, and the current into the second online parameter identification model to obtain the second battery operating condition parameters; wherein, the second battery operating condition parameters include: second open circuit voltage, second ohmic internal resistance, second polarization internal resistance, and second polarization capacitance; The internal resistance calculation module is used to determine the total internal resistance of the battery based on the second battery operating parameters.

6. The apparatus according to claim 5, characterized in that, The device further includes: The target open-circuit voltage determination module is used to obtain the battery voltage and current in real time in the first operating condition parameter calculation module, and input the voltage and current into the pre-constructed first online parameter identification model to obtain the first battery operating condition parameters. Before that, the battery state of charge (SOC) determined based on the ampere-hour integration method and the open-circuit voltage method is substituted into the SOC-OCV table fitting function to obtain the target open-circuit voltage (OCV). The mean absolute error calculation module is used to calculate the mean absolute error of the least squares fitted voltage based on the OCV. The selection module is used to select the forgetting factor that minimizes the mean absolute error. The second building module is used to build a first online parameter identification model that includes the forgetting factor.

7. The apparatus according to claim 5, characterized in that: The Kalman filter model uses SOC and polarization voltage as state vectors; The Kalman filter model uses the ampere-hour integral method and the polarization voltage discrete time-domain calculation formula of the first online parameter identification model as the state equation; The Kalman filter model uses the voltage observation equation, which is equal to the product of the open-circuit voltage, the polarization voltage, and the current and the ohmic internal resistance.

8. The apparatus according to claim 5, characterized in that, The internal resistance calculation module is specifically used to: determine the sum of the second ohmic internal resistance and the second polarization internal resistance as the total internal resistance of the battery.

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