Battery parameter identification method and device, electronic equipment and medium

Abstract

The invention provides a battery parameter identification method and device, electronic equipment and a medium, and the method comprises the steps: determining an output voltage value of an equivalent circuit model according to equivalent battery parameters in the equivalent circuit model of a battery; calculating a voltage error value at the current moment according to the measured voltage value and the output voltage value of the battery; when the voltage error value is greater than a preset error value, determining an impedance update gain vector according to a preset step gain vector, a preset impedance gain vector and the voltage error value at the current moment, obtaining an updated equivalent battery parameter based on the impedance update gain vector, and returning to the execution step; and when the voltage error value is not greater than a preset error value, determining the equivalent battery parameter as a target equivalent battery parameter of the battery at the current moment. According to the method and the device, the calculation complexity is reduced while accurate recognition of the battery parameters is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicle battery, in particular to a battery parameter identification method and device, electronic equipment and medium. BACKGROUND

[0002] Lithium-ion batteries have dominated the electric vehicle, consumer electronics and energy storage fields due to their high energy density, long cycle life and environmental advantages. To achieve efficient battery management and accurate state evaluation, equivalent circuit models have become the core modeling method of battery management systems due to their simple structure, efficient calculation and clear physical meaning.

[0003] However, as a typical time-varying nonlinear system, the internal impedance and polarization characteristics of lithium batteries will dynamically change with temperature, current rate, SOC and aging degree, making it difficult for fixed parameter models to maintain high precision modeling capability in all scenarios. Therefore, there is an urgent need for a technical solution that can identify and dynamically update model parameters in real time. SUMMARY

[0004] Therefore, the purpose of the present application is to provide a battery parameter identification method and device, electronic equipment and medium, which aims to overcome at least one of the above-mentioned defects.

[0005] In a first aspect, the present application provides a battery parameter identification method, which comprises: (A) determining the output voltage value of the equivalent circuit model according to the equivalent battery parameters in the equivalent circuit model of the battery; (B) calculating the voltage error value at the current time according to the measured voltage value of the battery and the output voltage value; (C) when the voltage error value is greater than a preset error value, determining an impedance update gain vector according to a preset step gain vector, a preset impedance gain vector and the voltage error value at the current time, obtaining updated equivalent battery parameters based on the impedance update gain vector, and returning to step (A); (D) when the voltage error value is not greater than the preset error value, determining the equivalent battery parameters as the target equivalent battery parameters of the battery at the current time.

[0006] In one possible implementation, step (A) comprises: (A1) determining the equivalent battery parameters of the equivalent circuit model at the i-th update at the current time; (A2) determining the output voltage value of the equivalent circuit model according to the equivalent battery parameters, the current at the current time and the open circuit voltage of the battery.

[0007] In a possible implementation, the equivalent battery parameter of the i-th update of the equivalent circuit model at the current time is determined in the following manner: when i is greater than 1, the equivalent battery parameter of the i-th update is determined as the updated equivalent battery parameter of the (i-1)-th update; when i is not greater than 1, the equivalent battery parameter of the i-th update is determined as the target equivalent battery parameter at the last time.

[0008] In a possible implementation, step (C) comprises: (C1) when the voltage error value is greater than the preset error value, obtaining the impedance update gain vector according to the preset impedance gain vector, the preset step gain vector, the voltage error value at the current time and the current value at the current time; (C2) obtaining the updated equivalent battery parameter according to the impedance update gain vector and the equivalent battery parameter, and setting i = i + 1 to return to step (A).

[0009] In a possible implementation, the equivalent circuit model comprises a power supply, a first resistor, a second resistor, a third resistor, a first capacitor and a second capacitor, wherein a positive electrode of the power supply is connected to one end of the first resistor, the other end of the first resistor is connected to one end of the second resistor, the other end of the second resistor is connected to one end of the third resistor, the other end of the third resistor is connected to a negative electrode of the power supply, the first capacitor is connected in parallel with the second resistor, and the second capacitor is connected in parallel with the third resistor.

[0010] In a possible implementation, the number of step gain factors in the preset step gain vector is the same as the number of the equivalent battery parameters, and the number of impedance gain factors in the preset impedance gain vector is the same as the number of the equivalent battery parameters, wherein the equivalent battery parameters comprise the first resistor, the second resistor, the third resistor, the first capacitor and the second capacitor.

[0011] In a second aspect, the present application provides a battery parameter identification device, which comprises: a determination module configured to determine an output voltage value of an equivalent circuit model of a battery according to equivalent battery parameters in the equivalent circuit model; a calculation module configured to calculate a voltage error value at a current time according to a measured voltage value of the battery and the output voltage value; a first judgment module configured to, when the voltage error value is greater than a preset error value, determine an impedance update gain vector according to a preset step gain vector, a preset impedance gain vector and the voltage error value at the current time, obtain an updated equivalent battery parameter based on the impedance update gain vector, and return to the determination module; and a second judgment module configured to, when the voltage error value is not greater than the preset error value, determine the equivalent battery parameters as target equivalent battery parameters of the battery at the current time.

[0012] In a possible implementation, the determining module is further configured to: determine an equivalent battery parameter of the equivalent circuit model in the i-th update at the current time; and determine an output voltage value of the equivalent circuit model according to the equivalent battery parameter, a current at the current time, and an open circuit voltage of the battery.

[0013] In a third aspect, the present application also provides an electronic device, comprising: a processor, a memory and a bus, the memory stores machine readable instructions executable by the processor, when the electronic device is running, the processor and the memory communicate through the bus, and the machine readable instructions are executed by the processor to perform the steps of the above method.

[0014] In a fourth aspect, the present application also provides a computer readable storage medium, the computer readable storage medium stores a computer program, and the computer program is executed by the processor to perform the steps of the above method.

[0015] The present application provides a battery parameter identification method and device, an electronic device and a medium, wherein the method comprises: determining an output voltage value of an equivalent circuit model according to an equivalent battery parameter in the equivalent circuit model of a battery; calculating a voltage error value at a current time according to a measured voltage value and the output voltage value of the battery; when the voltage error value is greater than a preset error value, determining an impedance update gain vector according to a preset step gain vector, a preset impedance gain vector and the voltage error value at the current time, obtaining an updated equivalent battery parameter based on the impedance update gain vector, and returning to the step; and when the voltage error value is not greater than the preset error value, determining the equivalent battery parameter as a target equivalent battery parameter of the battery at the current time. Through the present application, the precise identification of the battery parameter is realized while reducing the computational complexity.

[0016] In order to make the above objectives, features and advantages of the present application more apparent, the following will describe a preferred embodiment in detail, and the accompanying drawings will be described as follows. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments. It should be understood that the following drawings only show some embodiments of the present application, and therefore should not be regarded as a limitation to the scope. For those skilled in the art, other related drawings can also be obtained without creative labor on the basis of these drawings.

[0018] Figure 1 A flowchart of a battery parameter identification method provided by the embodiments of the present application; Figure 2 A structure diagram of an equivalent circuit model provided by the embodiments of the present application; Figure 3 A flowchart for determining an output voltage value of an equivalent circuit model provided by an embodiment of the present application; Figure 4 A flowchart for obtaining an i-th updated updated equivalent battery parameter provided by an embodiment of the present application; Figure 5 One of the schematic diagrams of the identification result of the equivalent parameter resistance provided by an embodiment of the present application; Figure 6 The second schematic diagram of the identification result of the equivalent parameter resistance provided by an embodiment of the present application; Figure 7 The third schematic diagram of the identification result of the equivalent parameter resistance provided by an embodiment of the present application; Figure 8 A structural schematic diagram of the battery parameter identification device provided by an embodiment of the present application; Figure 9 A structural schematic diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0019] To make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. The components of the embodiments of the present application described and shown in the drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the claimed present application, but only represents selected embodiments of the present application. Based on the embodiments of the present application, every other embodiment obtained by a person skilled in the art without creative work belongs to the scope of protection of the present application.

[0020] First, the application scenarios applicable to the present application are introduced. The present application can be applied to vehicle batteries.

[0021] It is found through research that lithium-ion batteries have been widely used in electric vehicles, portable electronic devices, and energy storage systems due to their high energy density, long cycle life, and environmental protection characteristics. In order to effectively manage and estimate the state of lithium batteries, an accurate battery model needs to be established. The equivalent circuit model (ECM) has become the most widely used model type in battery management systems (BMS) due to its simple structure, low computational complexity, and clear physical meaning. However, lithium batteries are a typical time-varying nonlinear system, and their internal characteristics change significantly with working conditions such as temperature, current size, state of charge, and aging degree. This makes it difficult for battery models based on fixed parameters to maintain high accuracy simulation performance in the full operating condition range. Therefore, real-time identification and updating of battery model parameters are key technologies to improve the accuracy and reliability of battery state estimation.

[0022] Currently, the commonly used online battery parameter identification methods mainly include: recursive least squares (RLS) method: this method continuously updates the parameter estimation value through recursion, but the computational complexity is relatively high, and matrix inversion operation is required. Extended Kalman filter (EKF) method: this method considers the parameters as the system state and realizes parameter identification through the prediction-correction mechanism, but the algorithm is complex, the computational overhead is large, and it is sensitive to the initial conditions and noise statistical characteristics. Alternating coefficient identification method: this method simplifies the identification process by alternately updating different groups of parameters, but it may ignore the coupling relationship between parameters, affecting the identification accuracy.

[0023] However, the existing lithium battery parameter identification methods have the following main problems: 1. High computational complexity: traditional RLS and EKF methods involve a large number of matrix operations and inversion operations, which are computationally intensive and difficult to run efficiently and in real time in resource-constrained embedded systems. 3. Parameter oscillation problem: many algorithms use absolute quantity adjustment mechanism, which leads to the dramatic fluctuation of parameters in the identification process, affecting the stability and reliability of the identification results. 3. Insufficient information utilization: most methods rely only on the electrical behavior data of the battery for parameter identification, and fail to fully utilize other observable information of the battery, limiting the identification accuracy. 4. Unreasonable identification strategy: some methods perform comprehensive identification on all model parameters, but in fact the sensitivity of different parameters to the working conditions varies greatly, and full-parameter identification increases the computational burden and may introduce additional errors. 5. Lack of robustness: when the battery works in complex conditions, the existing algorithms lack the ability to suppress noise and interference, and the identification results are easily affected by random factors and deviate from the true value. 6. Poor real-time performance: high-precision identification algorithms often require a long convergence time, making it difficult to meet the real-time requirements of battery management systems, affecting the timeliness of state estimation and control decisions.

[0024] Based on this, the embodiment of the application provides a battery parameter identification method, device, electronic equipment and medium, aiming to realize accurate identification of battery parameters while reducing the calculation complexity.

[0025] Please refer to Figure 1 , Figure 1 The flow chart of the battery parameter identification method provided by the embodiment of the application is shown in FIG. 1. Figure 1 The battery parameter identification method provided by the embodiment of the application includes the following steps. S101, determining the output voltage value of the equivalent circuit model according to the equivalent battery parameters in the equivalent circuit model of the battery.

[0026] Here, the output voltage value refers to the simulation terminal voltage value of the equivalent circuit model, and the equivalent circuit model is shown in FIG. 2. Figure 2 As shown in FIG. 2, U OCV represents the open circuit voltage value of the battery, U L is the terminal voltage value, and the equivalent battery parameters include the first resistance R0, the second resistance R1, the third resistance R2, the first capacitance C1, and the second capacitance C2 in the equivalent circuit model, wherein the first resistance R0 is the ohmic internal resistance of the battery, the second resistance R1 and the first capacitance C1 are in parallel to describe the electrochemical polarization effect caused by the charge transfer impedance and the ion conduction impedance, and the third resistance R2 and the second capacitance C2 describe the concentration polarization effect caused by the ion diffusion process inside the battery.

[0027] There are five equivalent parameters to be identified in the equivalent circuit model, The to-be-identified parameters form a vector as .

[0028] The specific process of determining the output voltage value of the equivalent circuit model will be introduced below. Figure 3

[0029] Figure 3 The flow chart of determining the output voltage value of the equivalent circuit model provided by the embodiment of the application is shown in FIG. 3.

[0030] S201, determining the equivalent battery parameters of the i-th update of the equivalent circuit model at the current time.

[0031] The equivalent battery parameters of the i-th update of the equivalent circuit model at the current time are determined in the following manner: when i is greater than 1, the equivalent battery parameters of the i-th update are determined as the updated equivalent battery parameters of the (i-1)-th update; and when i is not greater than 1, the equivalent battery parameters of the i-th update are determined as the target equivalent battery parameters at the last time. ​

[0032] S202, determine the output voltage value of the equivalent circuit model according to the equivalent battery parameter, the current at the current moment and the open circuit voltage of the battery.

[0033] Wherein, the expressions of U0, U1 and U2 in the equivalent circuit model are as follows:

[0034]

[0035]

[0036] According to Kirchhoff's law, the following relationship can be obtained:

[0037] Wherein, is the ohmic voltage drop value, is the charge transfer polarization voltage value, is the charge transfer polarization voltage change rate, is the ion diffusion polarization voltage value, is the ion diffusion polarization voltage change rate, is the open circuit voltage value, is the output voltage value of the equivalent circuit model, that is, the terminal voltage value.

[0038] Return Figure 1 S102, calculate the voltage error value at the current moment according to the measured voltage value and the output voltage value of the battery.

[0039] Specifically, the voltage error value at the current moment can be obtained by the following relationship:

[0040] Wherein, is the voltage error value updated for the ith time at the t moment, is the measured voltage value of the battery at the t moment, the measured voltage value refers to the actual terminal voltage value of the battery, is the output voltage value updated for the ith time at the t moment.

[0041] S103, when the voltage error value is greater than the preset error value, determine the impedance update gain vector according to the preset step gain vector, the preset impedance gain vector and the voltage error value at the current moment, obtain the updated equivalent battery parameter based on the impedance update gain vector, and return to execute step S101.

[0042] Here, the preset error value is set to 0.01V, in order to reduce the error The impedance update gain vector is introduced , the to-be-identified vector updating to obtain the updated equivalent battery parameter.

[0043] The specific process of obtaining the updated equivalent battery parameter is introduced below. Figure 4 The specific process of obtaining the updated equivalent battery parameter is introduced below.

[0044] Figure 4 The flowchart of obtaining the i-th updated equivalent battery parameter provided by the embodiments of the present application.

[0045] S301, when the voltage error value is greater than the preset error value, obtaining an impedance update gain vector according to the preset impedance gain vector, the preset step gain vector, the voltage error value at the current time and the current value at the current time.

[0046] Specifically, the can be updated by the following formula:

[0047] wherein, the preset impedance gain vector is The initial values of the preset impedance gain vector and the preset step gain vector are both 1, and the update of the preset impedance gain vector is performed by introducing the preset step gain vector

[0048] wherein, the value of each step gain factor of the preset step gain vector is the same, and can be comprehensively adjusted according to the actual working condition of the battery and the sampling time.

[0049] S302, obtaining the updated equivalent battery parameter according to the impedance update gain vector and the equivalent battery parameter, and setting i=i+1, returning to step S101.

[0050] Returning to Figure 1 S104, when the voltage error value is not greater than the preset error value, determining the equivalent battery parameter as the target equivalent battery parameter of the battery at the current time.

[0051] Embodiment 1.

[0052] In this embodiment, a typical 3C soft-pack lithium ion battery is taken as the research object to verify the parameter identification method based on the least mean square filter. The test environment temperature is set to 25±2℃, and the initial SOC of the battery is 100%, and the discharge current is constant at 1C.

[0053] ​​​Experimental setup: Battery type: 3C soft-pack lithium-ion battery, rated capacity: 3900mAh, discharge conditions: constant current 1C discharge (i.e., 3.9A), data sampling frequency: 1Hz, test temperature: 25±2℃, initial parameter settings: initial first resistor R0 value set to 120mΩ, initial second resistor R1 value set to 6mΩ, initial third resistor R2 value set to 1mΩ, initial first capacitor C1 value set to 3600F, initial second capacitor C2 value set to 1600F. Step gain parameters: γ=1, preset error value: 0.01V.

[0054] like Figure 5 As shown, Figure 5 This is one of the schematic diagrams showing the identification results of the equivalent resistance parameters provided in the embodiments of this application. Under 1C discharge conditions, the key parameters of the battery equivalent circuit model were successfully identified based on the minimum mean square filtering algorithm proposed in this application. From the parameter identification results, the parameter of the first resistance R0 (green line) shows a slight upward trend in the early stages of discharge, and then remains relatively stable at approximately 100mΩ within the SOC range of 60%-20%. When the SOC is below 20%, the value of the first resistance R0 begins to increase significantly, which is consistent with the electrochemical characteristic of increased internal resistance in lithium batteries at low SOC levels.

[0055] The green line representing the second resistor R1 parameter shows a fluctuating upward trend throughout the discharge process, increasing from approximately 5mΩ initially to approximately 7mΩ at the end of the discharge, reflecting the characteristic that the battery polarization impedance increases with the depth of discharge.

[0056] The change in the third resistance parameter R2, indicated by the blue line, exhibits a more pronounced nonlinear characteristic. It remains relatively stable at approximately 1 mΩ in the middle SOC range (40%-60%), while increasing in the high and low SOC ranges. This aligns with the variation in ion diffusion impedance of lithium batteries within these ranges.

[0057] like Figure 6 As shown, Figure 6 The second schematic diagram of the identification results of the equivalent parameter resistance provided in the embodiment of this application shows that the changes in the parameters of the first capacitor C1 (blue line) and the second capacitor C2 (orange line) are relatively stable in the middle SOC range (40%-60%), at approximately 3000F and 1500F respectively; while they increase in the high SOC and low SOC ranges.

[0058] like Figure 7 As shown, Figure 7The third identification result schematic diagram of the equivalent parameter resistance provided by the embodiment of the application, the comparison diagram of the blue line output voltage value and the orange line measured voltage value shows that the measured voltage value based on the model of the identified parameter is highly consistent with the measured voltage value, and the maximum error is less than 0.01V, and the identification effect is good. Especially in the discharge platform region (SOC 80%-20%), the model accuracy is higher, which is of great significance to the SOC estimation in the actual battery management system. The experimental results prove that the battery parameter identification method based on the least mean square filtering of the application can effectively capture the dynamic changes of the battery parameters in the discharge process, and has the advantages of high calculation efficiency and good parameter stability. The entire identification process does not require complex matrix operations, but can be completed through simple addition, subtraction, multiplication and division operations, and is very suitable for implementation in the battery management system with limited computing resources.

[0059] Based on the same inventive concept, the embodiment of the application also provides a battery parameter identification device corresponding to the battery parameter identification method. Since the principle of solving problems in the device of the embodiment of the application is similar to the above-mentioned battery parameter identification method of the embodiment of the application, the implementation of the device can be referred to the implementation of the method, and the repeated parts will not be described here.

[0060] Please refer to Figure 8 , Figure 8 The structure schematic diagram of a battery parameter identification device provided by the embodiment of the application is shown in Figure 8 The battery parameter identification device 400 comprises: A determination module 401 is configured to determine an output voltage value of an equivalent circuit model of a battery according to equivalent battery parameters in the equivalent circuit model of the battery.

[0061] A calculation module 402 is configured to calculate a voltage error value at a current time according to a measured voltage value of the battery and the output voltage value.

[0062] A first judgment module 403 is configured to, when the voltage error value is greater than a preset error value, determine an impedance update gain vector according to a preset step gain vector, a preset impedance gain vector and the voltage error value at the current time, obtain updated equivalent battery parameters based on the impedance update gain vector, and return to the determination module.

[0063] A second judgment module is configured to, when the voltage error value is not greater than the preset error value, determine the equivalent battery parameters as target equivalent battery parameters of the battery at the current time.

[0064] Please refer to Figure 9 , Figure 9 The structure schematic diagram of an electronic device provided by the embodiment of the application is shown in Figure 9As shown in FIG. 5, the electronic device 500 includes a processor 510, a memory 520 and a bus 530.

[0065] The memory 520 stores machine readable instructions executable by the processor 510, when the electronic device 500 is running, the processor 510 communicates with the memory 520 through the bus 530, the machine readable instructions are executed by the processor 510, can execute the steps in the above method embodiments, for details, see the method embodiments, here will not be repeated.

[0066] The embodiments of the present application also provide a computer readable storage medium, the computer readable storage medium stores a computer program, the computer program is run by the processor, can execute the steps of the method in the above method embodiments, for details, see the method embodiments, here will not be repeated.

[0067] Those skilled in the art can clearly understand that, for the convenience and brevity of the above description, the specific working process of the system, device and unit described above can refer to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0068] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented by other ways. The device embodiments described above are only schematic, for example, the division of the units is only a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some communication interface, device or unit, which can be electrical, mechanical or other forms.

[0069] The units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, they can be located in one place, or they can be distributed on a plurality of network units. According to actual needs, part or all of the units can be selected to achieve the purpose of the embodiment scheme.

[0070] In addition, each functional unit in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit.

[0071] If the functions are realized in the form of software function units and sold or used as independent products, they can be stored in a nonvolatile computer readable storage medium executable by a processor. Based on this understanding, the technical solutions of the present application essentially or the parts of the prior art that make contributions or parts of the technical solutions can be embodied in the form of software products. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in the embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (Read-Only Memory, ROM), a random access memory (Random Access Memory, RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0072] Finally, it should be noted that: the above-described embodiments are only specific embodiments of the present application, which are used to illustrate the technical solutions of the present application, but not to limit them. The protection scope of the present application is not limited thereto. Although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can make modifications or easily think of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed by the present application, or make equivalent replacements to some technical features. The modifications, changes or replacements do not make the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A battery parameter identification method, characterized by, The method comprises: (A) determining an output voltage value of an equivalent circuit model of a battery according to equivalent battery parameters in the equivalent circuit model of the battery; (B) calculating a voltage error value at a current time according to a measured voltage value of the battery and the output voltage value; (C) when the voltage error value is greater than a preset error value, determining an impedance update gain vector according to a preset step gain vector, a preset impedance gain vector and the voltage error value at the current time, obtaining updated equivalent battery parameters based on the impedance update gain vector, and returning to step (A) for execution; (D) when the voltage error value is not greater than the preset error value, determining the equivalent battery parameters as target equivalent battery parameters of the battery at the current time.

2. The method of claim 1, wherein, Step (A) comprises: (A1) determining equivalent battery parameters of the equivalent circuit model at the current time after the i-th update; (A2) determining the output voltage value of the equivalent circuit model according to the equivalent battery parameters, a current at the current time and an open circuit voltage of the battery.

3. The method of claim 2, wherein, The equivalent battery parameters of the equivalent circuit model at the current time after the i-th update are determined in the following manner: When i is greater than 1, the equivalent battery parameters after the i-th update are determined as updated equivalent battery parameters after the (i-1)-th update; When i is not greater than 1, the equivalent battery parameters after the i-th update are determined as target equivalent battery parameters at the last time.

4. The method of claim 3, wherein, Step (C) comprises: (C1) when the voltage error value is greater than the preset error value, obtaining the impedance update gain vector according to the preset impedance gain vector, the preset step gain vector, the voltage error value at the current time and a current value at the current time; (C2) obtaining updated equivalent battery parameters according to the impedance update gain vector and the equivalent battery parameters, setting i=i+1 and returning to step (A) for execution.

5. The method of claim 1, wherein, The equivalent circuit model comprises a power supply, a first resistor, a second resistor, a third resistor, a first capacitor and a second capacitor, wherein a positive electrode of the power supply is connected to one end of the first resistor, the other end of the first resistor is connected to one end of the second resistor, the other end of the second resistor is connected to one end of the third resistor, the other end of the third resistor is connected to a negative electrode of the power supply, the first capacitor is connected in parallel with the second resistor, and the second capacitor is connected in parallel with the third resistor.

6. The method of claim 5, wherein, The number of step gain factors in the preset step gain vector is the same as the number of the equivalent battery parameters, and the number of impedance gain factors in the preset impedance gain vector is the same as the number of the equivalent battery parameters, wherein the equivalent battery parameters comprise the first resistor, the second resistor, the third resistor, the first capacitor and the second capacitor.

7. A battery parameter identification device, characterized by, The device comprises: a determining module configured to determine an output voltage value of an equivalent circuit model of a battery according to equivalent battery parameters in the equivalent circuit model of the battery; a calculating module configured to calculate a voltage error value at a current time according to a measured voltage value of the battery and the output voltage value; The first judging module is configured to, when the voltage error value is greater than a preset error value, determine an impedance update gain vector according to a preset step gain vector, a preset impedance gain vector and the voltage error value at the current time, obtain updated equivalent battery parameters based on the impedance update gain vector, and return the determining module; The second judging module is configured to, when the voltage error value is not greater than the preset error value, determine the equivalent battery parameters as target equivalent battery parameters of the battery at the current time.

8. The apparatus of claim 7, wherein, The determining module is further configured to: determine equivalent battery parameters of the equivalent circuit model updated for the i-th time at the current time; determine an output voltage value of the equivalent circuit model according to the equivalent battery parameters, a current at the current time and an open circuit voltage of the battery.

9. An electronic device, comprising: The processor, the memory and the bus, the memory stores machine readable instructions executable by the processor, when the electronic device runs, the processor and the memory communicate through the bus, the processor executes the machine readable instructions, to execute the steps of the method as claimed in any one of claims 1 to 6. The computer readable storage medium stores a computer program, the computer program is run by the processor to execute the steps of the method as claimed in any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that, ​

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