Method, device, equipment, medium and program product for calculating open circuit voltage

Abstract

The application discloses a kind of open-circuit voltage calculation method, device, equipment, medium and program product, involve power battery management technical field.The method comprises: obtaining the electromotive force curve of lithium ion battery, electromotive force curve includes the positive electrode electromotive force curve corresponding to the positive electrode material of lithium ion battery, and the negative electrode electromotive force curve corresponding to the negative electrode material of lithium ion battery;Obtain the working data curve of lithium ion battery, based on working data curve and electromotive force curve, the characteristic analysis of lithium ion battery is carried out, obtains the performance parameter corresponding to lithium ion battery, determines the corresponding relationship between reference residual power and the open-circuit voltage of lithium ion battery based on performance parameter.Through the above method, the accurate open-circuit voltage data of battery can be obtained, and the residual power of battery is accurately calculated based on open-circuit voltage data, improve the accuracy of battery state of charge estimation.

Description

TECHNICAL FIELD

[0001] Embodiments of the present application relate to the technical field of power battery management, and in particular to a calculation method and device of open-circuit voltage, equipment, medium and program product. BACKGROUND

[0002] Lithium ion batteries are widely used to power electric vehicles, and the remaining battery capacity is the most important reference for users when planning the battery usage route.

[0003] In related technologies, the estimation of the remaining battery capacity usually uses open-circuit voltage correction method and ampere-hour integral estimation method, or Kalman filter algorithm based on battery model, all of which need to use open-circuit voltage data of the battery to achieve, and the open-circuit voltage data is usually obtained by direct measurement.

[0004] It is easy to measure the open-circuit voltage of a new battery that has not been assembled, and the initial relationship curve between the open-circuit voltage and the state of charge can be easily obtained. However, it is difficult to obtain the relationship curve between the open-circuit voltage and the state of charge of the aging state of the battery during use. SUMMARY

[0005] Embodiments of the present application provide a calculation method, device, equipment, medium and program product of open-circuit voltage, which can calculate the open-circuit voltage of an aging lithium ion battery. The technical solution is as follows:

[0006] On the one hand, a calculation method of open-circuit voltage is provided, and the method comprises:

[0007] Obtaining an electromotive force curve of a lithium ion battery, the electromotive force curve comprising a positive electrode electromotive force curve corresponding to a positive electrode material of the lithium ion battery, and a negative electrode electromotive force curve corresponding to a negative electrode material of the lithium ion battery;

[0008] Obtaining a working data curve of the lithium ion battery, the working data curve comprising a charging curve, a discharging curve and a state of charge curve of the lithium ion battery, the state of charge curve being used to represent a reference remaining battery capacity of the lithium ion battery, the charging curve comprising a first current change curve and a first voltage change curve, and the discharging curve comprising a second current change curve and a second voltage change curve;

[0009] Performing feature analysis on the lithium ion battery based on the working data curve and the electromotive force curve to obtain performance parameters corresponding to the lithium ion battery, the performance parameters comprising intrinsic parameters for characterizing the state of charge of the lithium ion battery, charging parameters for characterizing charging characteristics of the lithium ion battery, and discharging parameters for characterizing discharging characteristics of the lithium ion battery;

[0010] The correspondence between the reference remaining charge and the open-circuit voltage of the lithium-ion battery is determined based on the performance parameters.

[0011] On the other hand, an open-circuit voltage calculation device is provided, the device comprising:

[0012] The acquisition module acquires the electromotive force curve of the lithium-ion battery, the electromotive force curve including the positive electrode electromotive force curve corresponding to the positive electrode material of the lithium-ion battery and the negative electrode electromotive force curve corresponding to the negative electrode material of the lithium-ion battery.

[0013] The acquisition module acquires the working data curves of the lithium-ion battery. The working data curves include the charging curve, discharging curve, and state of charge curve of the lithium-ion battery. The state of charge curve is used to represent the reference remaining capacity of the lithium-ion battery. The charging curve includes a first current change curve and a first voltage change curve. The discharging curve includes a second current change curve and a second voltage change curve.

[0014] The analysis module performs feature analysis on the lithium-ion battery based on the working data curve and the electromotive force curve to obtain the performance parameters corresponding to the lithium-ion battery. The performance parameters include intrinsic parameters for characterizing the state of charge of the lithium-ion battery, charging parameters for characterizing the charging characteristics of the lithium-ion battery, and discharge parameters for characterizing the discharging characteristics of the lithium-ion battery.

[0015] The determination module determines the correspondence between the reference remaining power and the open-circuit voltage of the lithium-ion battery based on the performance parameters.

[0016] On the other hand, a computer device is provided, the computer device including a processor and a memory, the memory storing at least one instruction, at least one program, code set or instruction set, the at least one instruction, the at least one program, the code set or instruction set being loaded and executed by the processor to implement the open circuit voltage calculation method as described in any of the embodiments of this application above.

[0017] On the other hand, a computer-readable storage medium is provided, wherein at least one instruction, at least one program, code set, or instruction set is stored therein, wherein the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by a processor to implement the open-circuit voltage calculation method as described in any of the embodiments of this application above.

[0018] On the other hand, a computer program product or computer program is provided, which includes computer instructions stored in a computer-readable storage medium. A processor of an electric vehicle reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the electric vehicle to perform the open-circuit voltage calculation method described in any of the above embodiments.

[0019] The beneficial effects of the technical solutions provided in this application include at least the following:

[0020] By designing a battery model, the electromotive force (EMF) curves corresponding to the positive and negative electrode materials of a lithium-ion battery are obtained. Based on the EMF curves, the open-circuit voltage curve of the lithium-ion battery is derived, along with its operating data curves. Using the battery model, the performance parameters of the lithium-ion battery are identified and analyzed, yielding corresponding performance parameters. Based on these parameters, the relationship between the reference remaining capacity and the open-circuit voltage curve is determined, allowing for the acquisition of real-time open-circuit voltage data. When a lithium-ion battery ages, the same method can be used to obtain accurate open-circuit voltage data, enabling accurate calculation of the remaining capacity and improving the accuracy of state-of-charge estimation. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart of a method for calculating open-circuit voltage provided in an exemplary embodiment of this application;

[0023] Figure 2 This is a schematic diagram of a positive electromotive force curve with the horizontal axis representing the state of charge (SOC) and the vertical axis representing the positive open-circuit electromotive force (OCP+) provided in an exemplary embodiment of this application;

[0024] Figure 3 This is a schematic diagram of a negative electromotive force curve with the horizontal axis representing the state of charge (SOC) and the vertical axis representing the negative open-circuit electromotive force (OCP-) provided in an exemplary embodiment of this application.

[0025] Figure 4 This is a schematic diagram of a current variation curve with time as the horizontal axis and current as the vertical axis, provided in an exemplary embodiment of this application.

[0026] Figure 5 This is a flowchart of a method for identifying performance parameters using a battery model, provided in another exemplary embodiment of this application;

[0027] Figure 6 This is a schematic diagram of a comparison error curve provided by another exemplary embodiment of this application, with the horizontal axis representing time and the vertical axis representing the error between the actual voltage and the model voltage;

[0028] Figure 7 This is a schematic diagram comparing the model OCV and model voltage (model V) calculated based on the battery model with the actual operating voltage (true V) provided in another exemplary embodiment of this application;

[0029] Figure 8 This is a flowchart of a method for determining the correspondence curve between the open-circuit voltage and the state of charge of a lithium-ion battery based on performance parameters, provided in another exemplary embodiment of this application.

[0030] Figure 9 This is a structural block diagram of an open-circuit voltage calculation device provided in an exemplary embodiment of this application;

[0031] Figure 10 This is a structural block diagram of an open-circuit voltage calculation device provided in another exemplary embodiment of this application;

[0032] Figure 11 This is a structural block diagram of a computer device provided in an exemplary embodiment of this application. Detailed Implementation

[0033] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0034] Batteries, as energy storage devices, are widely used in electric vehicles, grid auxiliary systems, high-rate fast charging stations, and other scenarios.

[0035] Lithium-ion batteries are commonly used in electric vehicles. They are rechargeable batteries that primarily function by moving lithium ions between the positive and negative electrodes. During charging and discharging, Li+ ions repeatedly insert and extract between the two electrodes: during charging, Li+ ions extract from the positive electrode, pass through the electrolyte, and insert into the negative electrode, leaving the negative electrode in a lithium-rich state; the reverse occurs during discharging.

[0036] For electric vehicles, a high-precision battery management system helps users make better use of battery resources. Therefore, in this increasingly competitive market, improving the accuracy of battery state calculations has become particularly important.

[0037] When batteries are used in electric vehicles, the state of charge (SOC) is a crucial indicator of the battery's condition. Electric vehicles characterize their driving range by estimating the battery pack's SOC. As a state parameter of the battery pack's capacity, SOC reflects the remaining capacity of the battery pack, numerically defined as the percentage of the battery's remaining capacity relative to its rated total capacity, typically expressed as a percentage.

[0038] State of charge (SOP) serves as the basis for calculating battery power (SOP) and battery capacity (SOE), and is the most important reference for battery usage route planning.

[0039] The remaining battery charge is not a directly measurable value; it needs to be estimated indirectly through other state monitoring values. Due to the errors in battery state detection and the nonlinearity of battery charge changes, the various current methods for estimating the state of charge all have some shortcomings. Therefore, improving the accuracy of state of charge calculation has become a key focus and challenge in battery management system research.

[0040] Common methods for estimating remaining battery capacity include one of the following:

[0041] (1) Open-circuit voltage correction method and ampere-hour integral estimation method;

[0042] (2) Kalman filtering algorithm based on preset battery model.

[0043] Both of the above methods can only be used if the battery's open circuit voltage (OCV) is known.

[0044] The terminal voltage of a battery in an open-circuit state is called the open-circuit voltage. The open-circuit voltage of a battery is equal to the difference between the potential of the positive electrode and the potential of the negative electrode when the battery is open-circuited, that is, when no current flows through the two electrodes.

[0045] Under normal circumstances, the open-circuit voltage of a battery is obtained through offline calibration, which can only obtain the open-circuit voltage of a fresh cell.

[0046] The open-circuit voltage of a battery is equal to the positive electromotive force minus the negative electromotive force. Therefore, the open-circuit voltage can be obtained from the corresponding electromotive forces (OCP) at the two terminals of the battery.

[0047] Since the electromotive force curves of the positive and negative electrodes are determined by the types of materials corresponding to the positive and negative electrodes of the battery, it is only necessary to determine the two starting and ending positions on the positive and negative electromotive force curves of the battery in order to determine the open circuit voltage curve of the battery.

[0048] However, batteries age after a period of use. At this time, the open-circuit voltage of the battery will also change. The state of charge of the battery calculated using the open-circuit voltage of a fresh cell will have errors. Furthermore, the open-circuit voltage curve corresponding to the open-circuit voltage of this aging state cannot be obtained by direct testing in actual use scenarios.

[0049] In this application embodiment, a method for calculating open-circuit voltage is proposed, which can calculate the corresponding relationship curve between the open-circuit voltage and the state of charge of an aged lithium-ion battery.

[0050] First, a battery model is designed to predict the model voltage of the lithium-ion battery during operation. The battery model contains some performance parameters to determine the open-circuit voltage and state-of-charge data of the lithium-ion battery.

[0051] The relationship between the positive and negative electromotive force and the state of charge of a lithium-ion battery is measured and fitted into a relationship curve with the state of charge on the horizontal axis and the positive or negative electromotive force on the vertical axis. The relationship curve is then substituted into the battery model.

[0052] The charging curve, discharging curve, and state of charge curve of a lithium-ion battery during operation are measured. The charging curve and discharging curve each include the current curve and voltage curve of the lithium-ion battery during operation. The charging curve and discharging curve are then input into the battery model.

[0053] After inputting the aforementioned relationship curves, charging curves, and discharging curves into the battery model, the parameters to be identified in the battery model are then identified using the least squares method based on the operating condition data of the battery model. The identified parameters are the performance parameters.

[0054] The curve showing the relationship between the open-circuit voltage and the state of charge of a lithium-ion battery was determined based on the performance parameters.

[0055] Based on the above-described terminology and application scenarios, the method for calculating the open-circuit voltage provided in this application will be explained. This method can calculate the open-circuit voltage of a battery. Taking the calculation of the open-circuit voltage of a lithium-ion battery as an example, Figure 1 A flowchart illustrating a method for calculating open-circuit voltage according to an exemplary embodiment of this application is shown, such as... Figure 1 As shown, the method includes the following steps.

[0056] Step 101: Obtain the electromotive force curve of the lithium-ion battery.

[0057] The electromotive force curves include the positive electrode electromotive force curve corresponding to the positive electrode material of the lithium-ion battery, and the negative electrode electromotive force curve corresponding to the negative electrode material of the lithium-ion battery.

[0058] It is worth noting that, in order to measure the open-circuit voltage of a lithium-ion battery, it is necessary to obtain the open-circuit potential (OCP) of the two terminals of the battery. The open-circuit voltage is obtained by subtracting the open-circuit potential of the positive terminal from the open-circuit potential of the negative terminal.

[0059] For lithium-ion batteries, the positive electrode material and the negative electrode material are usually different types. The commonly used material for the positive electrode of lithium-ion batteries is lithium iron phosphate, and the commonly used material for the negative electrode is graphite.

[0060] Optionally, the example uses lithium iron phosphate as the positive electrode material and graphite as the negative electrode material for lithium-ion batteries.

[0061] The open-circuit electromotive force data of mass-produced lithium iron phosphate and graphite were obtained by measuring the open circuit electromotive force data of these two materials, and the following two curves were obtained by fitting their state of charge data:

[0062] 1. The positive electromotive force curve with the horizontal axis representing the state of charge and the vertical axis representing the positive open-circuit electromotive force;

[0063] 2. The horizontal axis represents the state of charge, and the vertical axis represents the negative electromotive force curve with the negative open circuit electromotive force.

[0064] The above-mentioned positive electrode electromotive force curve and negative electrode electromotive force curve together constitute the electromotive force curve of a lithium-ion battery.

[0065] Indicative, such as Figure 2 As shown, Figure 2 It is a schematic diagram of a positive electromotive force curve with the horizontal axis representing the state of charge (SOC) and the vertical axis representing the positive open-circuit electromotive force (OCP+).

[0066] In the positive electrode electromotive force curve 200, the positive electrode initial charge state 201 (p_init) corresponds to a charge state value of 0, that is, when the remaining charge is 0; the positive electrode end charge state 202 (p_end) corresponds to a charge state value of 100, that is, when the remaining charge is 100%.

[0067] Where p_init refers to the SOC value corresponding to the low-end cutoff voltage of the positive open-circuit electromotive force, and p_end refers to the SOC value corresponding to the high-end cutoff voltage of the positive open-circuit electromotive force.

[0068] Indicative, such as Figure 3 As shown, Figure 3 It is a schematic diagram of a negative electromotive force curve with the horizontal axis representing the state of charge (SOC) and the vertical axis representing the negative open-circuit electromotive force (OCP-).

[0069] In the negative electrode electromotive force curve 300, the initial charge state 301 (n_init) of the negative electrode corresponds to a charge state value of 0, that is, when the remaining charge is 0; the ending charge state 302 (n_end) of the negative electrode corresponds to a charge state value of 100, that is, when the remaining charge is 100%.

[0070] Wherein, n_init refers to the SOC value corresponding to the low-end cutoff voltage of the negative open-circuit electromotive force, and n_end refers to the SOC value corresponding to the high-end cutoff voltage of the negative open-circuit electromotive force.

[0071] Step 102: Obtain the working data curve of the lithium-ion battery.

[0072] Several indicators are important when a lithium-ion battery is working, including but not limited to current data, voltage data, and state of charge data.

[0073] The working states of lithium-ion batteries include, but are not limited to, either the charging state or the discharging state.

[0074] The operating data curves for lithium-ion batteries include the charging curve, discharging curve, and state of charge (SOC) curve. The SOC curve represents the state of charge of the lithium-ion battery, i.e., the remaining charge.

[0075] The charging curve includes a first current change curve and a first voltage change curve. The first current change curve is used to represent the current change data during the charging of the lithium-ion battery, and the first voltage change curve is used to represent the voltage change data during the charging of the lithium-ion battery.

[0076] The discharge curve includes a second current change curve and a second voltage change curve. The second current change curve is used to represent the current change data during the discharge of the lithium-ion battery, and the second voltage change curve is used to represent the voltage change data during the discharge of the lithium-ion battery.

[0077] The first current change curve and the second current change curve can be continuous, and the first voltage change curve and the second voltage change curve can be continuous. That is, when the working state of the lithium-ion battery changes from the charging state to the discharging state, the above four curves can appear in the same curve image.

[0078] Indicative, such as Figure 4 As shown, Figure 4 It is a schematic diagram of a current change curve with time on the horizontal axis and current on the vertical axis.

[0079] When Time is in the first interval 401, that is, in the interval (0, 450), the working state of the lithium-ion battery is the charging state and the resting state, and the corresponding current values ​​are negative numbers and 0.

[0080] When Time is in the second interval 402, that is, in the interval (451, 1100), the working state of the lithium-ion battery is the discharge state and the resting state, and the corresponding current values ​​are positive numbers and 0.

[0081] In conclusion, Figure 4 In the curve, the curve located in the interval (0, 450) is the first current change curve; the curve located in the interval (451, 1100) is the second current change curve.

[0082] The charging and discharging curves above, with time on the horizontal axis and current or voltage on the vertical axis, represent the operating state of the lithium-ion battery at different times, as well as the corresponding current and voltage data for each operating state. Simultaneously with recording the current and voltage data, the state of charge (SOC) of the lithium-ion battery, i.e., the remaining battery capacity, is also recorded.

[0083] Optionally, the lithium-ion battery is a lithium iron phosphate battery.

[0084] For lithium iron phosphate (LFP) batteries, there are two plateau regions: one with a lower voltage and one with a higher voltage. The lower plateau is called the low plateau, and the higher plateau is called the high plateau. When extracting charging and discharging curves, the high-end voltage of the LFP battery must be higher than the high plateau, and the low-end voltage must be lower than the low plateau, in order to extract a curve range that includes more of the electromotive force characteristics.

[0085] Optionally, a portion of the charging and discharging curves can be extracted with reference to the state of charge (remaining battery capacity) of the lithium-ion battery during operation.

[0086] In addition, high-end batteries need to be able to stand for more than 30 minutes (i.e., after charging, the battery needs to stand for more than 30 minutes), and low-end batteries need to be able to stand for more than 30 minutes (i.e., after discharging, the battery needs to stand for more than 30 minutes).

[0087] It is worth noting that the operating state of a lithium-ion battery can be arbitrary, that is, it can switch from a charging state to a discharging state or vice versa. This embodiment does not limit this.

[0088] It is worth noting that the type of lithium-ion battery can be arbitrary, and the basis for extracting the charging and discharging curves can be the battery's state of charge, the battery's high-end voltage and low-end voltage, or other state data; this embodiment does not limit this. When the charging and discharging curves are extracted based on the battery's state of charge, the low-end state of charge is lower than a first threshold, and the high-end state of charge is higher than a second threshold; the first and second thresholds can be arbitrary; the duration of the battery's resting time at the high and low ends can be arbitrary, and this embodiment does not limit this.

[0089] Step 103: Perform characteristic analysis on the lithium-ion battery based on the working data curve and electromotive force curve to obtain the corresponding performance parameters of the lithium-ion battery.

[0090] The performance parameters include intrinsic parameters characterizing the state of charge (SOC) of a lithium-ion battery, charging parameters characterizing its charging characteristics, and discharge parameters characterizing its discharging characteristics. These performance parameters are used to characterize the features of each state of the battery and can also be used to calculate the open-circuit voltage of the lithium-ion battery.

[0091] Intrinsic parameters include: p_init (SOC value corresponding to the low-end cutoff voltage of the positive OCP), p_end (SOC value corresponding to the high-end cutoff voltage of the positive OCP), n_init (SOC value corresponding to the low-end cutoff voltage of the negative OCP), and n_end (SOC value corresponding to the high-end cutoff voltage of the negative OCP).

[0092] The charging parameters include: k_ch (charging current coefficient), b_ch (charging polarization voltage), and η_ch (charging high-side additional polarization voltage).

[0093] The discharge parameters include: k_disch (discharge current coefficient), b_disch (discharge polarization voltage), and η_disch (discharge low-side additional polarization voltage).

[0094] The introduction of parameters such as polarization voltage and additional polarization voltage is due to the possibility of polarization during battery use. Battery polarization refers to the phenomenon where current flows through the battery, causing the electrodes to deviate from their equilibrium electrode potential; this is called electrode polarization.

[0095] Based on the operating data curve and electromotive force curve, the lithium-ion battery is characterized. That is, the corresponding current data, voltage data, and state of charge data from the operating data curve and electromotive force curve are input into a preset model for characteristic analysis, and the aforementioned characterization parameters can be obtained through identification and analysis.

[0096] It is worth noting that, in some embodiments, the performance parameters of the lithium-ion battery include, but are not limited to, the above-mentioned parameter types. The performance parameters of the lithium-ion battery can be any number, including the characterization parameters, charging parameters, and discharging parameters. This embodiment does not limit this.

[0097] Step 104: Determine the correspondence between the reference remaining capacity and the open-circuit voltage of the lithium-ion battery based on the performance parameters.

[0098] After obtaining the performance parameters based on the above steps, the performance parameters are substituted into the preset model used for feature analysis to obtain the correspondence between the reference remaining power and the open circuit voltage. By assigning a value to the reference remaining power, the corresponding open circuit voltage can be obtained.

[0099] It is worth noting that the method for determining the correspondence between the reference remaining power and the open-circuit voltage of the lithium-ion battery based on performance parameters can be arbitrary, and this embodiment does not limit it.

[0100] In summary, the method provided in this embodiment obtains the electromotive force (EMF) curves corresponding to the positive and negative electrode materials of a lithium-ion battery, derives the open-circuit voltage curve based on the EMF curves, acquires the operating data curves of the lithium-ion battery, and uses a preset model of feature analysis to identify and analyze the performance parameters of the lithium-ion battery based on the EMF curves and operating data curves. This yields the corresponding performance parameters of the lithium-ion battery, and the relationship between the reference remaining capacity and the open-circuit voltage curve is determined based on these performance parameters, thus obtaining real-time open-circuit voltage data of the lithium-ion battery. When the lithium-ion battery ages, the above method can also be used to obtain accurate open-circuit voltage data, and the remaining capacity of the lithium-ion battery can be accurately calculated based on the open-circuit voltage data, improving the accuracy of battery state-of-charge estimation.

[0101] In some optional embodiments, the lithium-ion battery is characterized based on its operating data curve and electromotive force curve to obtain the corresponding performance parameters. A battery model can be designed to predict the model voltage of the lithium-ion battery during operation. The performance parameters are then identified based on the actual voltage and model voltage of the lithium-ion battery during operation, and the identified performance parameters are obtained. Figure 5 This is a flowchart of a method for identifying performance parameters using a battery model, as provided in an embodiment of this application. Figure 5 As shown, the method includes the following steps.

[0102] Step 501: Obtain the open-circuit voltage curve of the lithium-ion battery based on the electromotive force curve.

[0103] The electromotive force curve includes the positive electromotive force curve and the negative electromotive force curve.

[0104] The open-circuit voltage of a lithium-ion battery is the open-circuit electromotive force (EMF) of the positive electrode minus the open-circuit EMF of the negative electrode. Therefore, obtaining the open-circuit voltage curve of a lithium-ion battery based on the EMF curve means subtracting the positive electrode EMF curve from the negative electrode EMF curve to obtain the open-circuit voltage curve of the lithium-ion battery.

[0105] The horizontal axis of the open-circuit voltage curve represents the reference remaining charge, i.e., the state of charge, while the vertical axis represents the open-circuit voltage.

[0106] The open-circuit voltage curve is fitted to the following relationship:

[0107] OCV=pf(soc*(p_end-p_init)+p_init)-nf(soc*(n_end-n_init)+n_init)

[0108] The parameters involved in the expression represent the following meanings:

[0109] (1) OCV refers to open circuit voltage (OCV);

[0110] (2) pf(soc*(p_end-p_init)+p_init) refers to the expression corresponding to the positive electromotive force curve, which is fitted according to the positive electromotive force curve image;

[0111] (3) nf(soc*(n_end-n_init)+n_init) refers to the expression corresponding to the negative electrode electromotive force curve, which is fitted based on the negative electrode electromotive force curve image;

[0112] (4) SOC refers to the State of Charge (SOC), which is the reference remaining capacity of the battery.

[0113] (5) p_init refers to the SOC value corresponding to the low-end cutoff voltage of the positive open circuit electromotive force (OCP), p_end refers to the SOC value corresponding to the high-end cutoff voltage of the positive open circuit electromotive force, n_init refers to the SOC value corresponding to the low-end cutoff voltage of the negative open circuit electromotive force, and n_end refers to the SOC value corresponding to the high-end cutoff voltage of the negative open circuit electromotive force.

[0114] The p_init, p_end, n_init, and n_end in the above relationship are also performance parameters of lithium-ion batteries, i.e., intrinsic parameters.

[0115] The open-circuit voltage curve of a lithium-ion battery is obtained by subtracting the positive electrode electromotive force curve from the negative electrode electromotive force curve. At this point, the intrinsic parameters are unknown.

[0116] It is worth noting that the method of obtaining the open-circuit voltage curve of a lithium-ion battery based on the electromotive force curve can be arbitrary, and the horizontal axis and vertical axis reference data corresponding to the open-circuit voltage curve can be arbitrary. That is, the horizontal axis of the open-circuit voltage curve can be arbitrary data, and the vertical axis of the open-circuit voltage curve can be arbitrary data. This embodiment does not limit this.

[0117] It is worth noting that the expression pf() fitted from the positive electromotive force curve image can be of any form and can contain any parameters; the expression nf() fitted from the negative electromotive force curve image can be of any form and can contain any parameters; the expression corresponding to the open-circuit voltage curve fitted based on the positive and negative electromotive force curves can be of any form and can contain any parameters; this embodiment does not limit this.

[0118] Step 502: Input the working data curve and open-circuit voltage curve into the battery model to obtain the performance parameters corresponding to the lithium-ion battery.

[0119] The battery model designed in this embodiment corresponds to the equation: V=OCV+kI+b+η.

[0120] OCV=pf(soc*(p_end-p_init)+p_init)-nf(soc*(n_end-n_init)+n_init)

[0121] Where V is the model voltage of the lithium-ion battery, I is the actual measured current of the lithium-ion battery, k is the current coefficient, b is the polarization voltage, η is the additional polarization voltage, and OCV is the open-circuit voltage of the lithium-ion battery.

[0122] The operating data curves of a lithium-ion battery include its charging curve, discharging curve, and state of charge curve. In the above equations, V corresponds to the first voltage change curve in the charging curve and the second voltage change curve in the discharging curve, and I corresponds to the first current change curve in the charging curve and the second current change curve in the discharging curve.

[0123] Among them, current coefficient, polarization voltage, and additional polarization voltage are performance parameters of lithium-ion batteries.

[0124] When a lithium-ion battery is in a charging or discharging state, its performance parameters, such as current coefficient, polarization voltage, and additional polarization voltage, are also categorized as charging parameters or discharging parameters.

[0125] The charging parameters include: k_ch (charging current coefficient), b_ch (charging polarization voltage), and η_ch (charging high-side additional polarization voltage).

[0126] The discharge parameters include: k_disch (discharge current coefficient), b_disch (discharge polarization voltage), and η_disch (discharge low-side additional polarization voltage).

[0127] Input the operating data curve and open-circuit voltage curve into the battery model to obtain the corresponding performance parameters of the lithium-ion battery.

[0128] Inputting the open-circuit voltage curve into the battery model means substituting the relationship OCV = pf(soc*(p_end-p_init)+p_init)-nf(soc*(n_end-n_init)+n_init) into the battery model V = OCV + kI + b + η. The corresponding equation for the battery model is then:

[0129] V=pf(soc*(p_end-p_init)+p_init)-nf(soc*(n_end-n_init)+n_init)+kI+b+n.

[0130] The operating data curves are input into the battery model, which is divided into two parts based on the lithium-ion battery's operating state: charging state and discharging state. Specifically, the charging curve and discharging curve are input into the battery model to obtain the model voltage of the lithium-ion battery.

[0131] 1. When the lithium-ion battery is in the charging working state, the first current change curve in the charging curve is input into the battery model, and the formula is as follows: Vm1=pf(soc*(p_end-p_init)+p_init)-nf(soc*(n_end-n_init)+n_init)+k_ch*I+b_ch;

[0132] Wherein, Vm1 refers to the model voltage of the lithium-ion battery when it is charging.

[0133] The first voltage change curve in the charging curve of a lithium-ion battery represents the actual voltage of the battery under charging conditions. Assuming the actual voltage of the lithium-ion battery under charging conditions is V1, then V1 - Vm1 = η_ch, meaning the difference between the actual voltage and the model voltage during charging is the additional polarization voltage η_ch at the high-end of charging.

[0134] 2. When the lithium-ion battery is in the discharge working state, input the second current change curve in the discharge curve into the battery model, and substitute the following formula: Vm2=pf(soc*(p_end-p_init)+p_init)-nf(soc*(n_end-n_init)+n_init)+k_disch*I+b_disch;

[0135] Wherein, Vm2 refers to the model voltage of a lithium-ion battery under discharge conditions.

[0136] The second voltage change curve in the discharge curve corresponding to a lithium-ion battery represents the actual voltage of the lithium-ion battery under discharge conditions. Assuming the actual voltage of the lithium-ion battery under discharge conditions is V2, then V2 - Vm2 = η_disch, that is, the difference between the actual voltage during discharge and the model voltage is the additional polarization voltage η_disch at the low end of the discharge.

[0137] Indicative, such as Figure 6 As shown, Figure 6 It is a schematic diagram of the error curve comparing the actual voltage and the model voltage, with the horizontal axis representing time and the vertical axis representing the error between the actual voltage and the model voltage. In other words, it is a comparison error diagram between the model-calculated voltage and the actual operating voltage.

[0138] exist Figure 6 The two points with the largest errors are the first error at point 601 and the second error at point 602, neither of which exceeds 0.02V.

[0139] The difference between the model voltage predicted by the battery model designed in this embodiment and the actual voltage is always maintained within tens of millivolts, and most of the differences are small, with larger differences only at the endpoints. The model can reflect the battery behavior well, and the model voltage predicted by this battery model is highly accurate.

[0140] When inputting the working data curve into the corresponding model formula of the battery model, it is necessary to extract the working data curve that meets the requirements according to the value corresponding to the state of charge.

[0141] Optionally, a charging curve and a discharging curve are selected that have a low-end state of charge of less than 25% and a high-end state of charge of more than 90%, and that satisfy the condition that the low-end voltage is lower than the low plateau and the high-end voltage is higher than the high plateau.

[0142] In addition, high-end batteries need to be able to stand for more than 30 minutes (i.e., after charging, the battery needs to stand for more than 30 minutes), and low-end batteries need to be able to stand for more than 30 minutes (i.e., after discharging, the battery needs to stand for more than 30 minutes).

[0143] Optionally, when the value corresponding to the state of charge is in the middle range (12% <= SOC <= 80%), that is, when the low-end state of charge is higher than 12% and the high-end state of charge is lower than 80%, the error between the model voltage and the actual voltage is small, and the battery model can be optimized to V = OCV + kI + b.

[0144] Only at the high and low ends, due to the increased battery polarization, the battery model needs to be optimized to V=OCV+kI+b+η. That is, we only need to consider adding η_ch for the high-end case (SOC>80%) and adding η_disch for the low-end case (SOC<12%).

[0145] By following the steps above, when the lithium-ion battery is in the charging and discharging states, the corresponding operating data curves and open-circuit voltage curves are respectively input into the battery model to obtain all the performance parameters of the lithium-ion battery, including:

[0146] Charging parameters: k_ch (charging current coefficient), b_ch (charging polarization voltage), η_ch (charging high-side additional polarization voltage);

[0147] Discharge parameters: k_disch (discharge current coefficient), b_disch (discharge polarization voltage), η_disch (discharge low-side additional polarization voltage);

[0148] Intrinsic parameters: p_init (SOC value corresponding to the low-end cutoff voltage of the positive OCP), p_end (SOC value corresponding to the high-end cutoff voltage of the positive OCP), n_init (SOC value corresponding to the low-end cutoff voltage of the negative OCP), n_end (SOC value corresponding to the high-end cutoff voltage of the negative OCP).

[0149] It is worth noting that the formula for the battery model can be in any form, and the formula for the battery model can contain any parameters. When using the battery model to obtain performance parameters, you can input any range of operating data curves and open-circuit voltage curves, or other types of data. This embodiment does not limit this.

[0150] It is worth noting that the above method distinguishes between the charging and discharging states of the lithium-ion battery and uses different operating data curves for different operating states. In some embodiments, the operating state of the lithium-ion battery can be arbitrary, and operating data curves of any range can be used. This embodiment does not limit this.

[0151] It is worth noting that in the above method, when the working data curve is substituted into the model formula corresponding to the battery model, it is necessary to select the working data curve that meets the requirements according to the value corresponding to the state of charge. The charging curve and discharging curve that meet the requirements of the low-end state of charge being less than 25% and the high-end state of charge being greater than 90%, and the low-end voltage being lower than the low plateau and the high-end voltage being higher than the high plateau are selected. In some embodiments, the selected working data curve can be from other ranges, and the index of the selected working data curve can be arbitrary. This embodiment does not limit this.

[0152] It is worth noting that in the above steps, the optimization of the battery model involves retaining or removing the extra polarization voltage based on the state of charge data of the lithium-ion battery during its operating state. In some embodiments, the removal and retention of the extra polarization voltage can be based on other operating data. Alternatively, when retaining or removing the extra polarization voltage based on the state of charge data of the lithium-ion battery during its operating state, the corresponding state of charge data can be arbitrary. That is, there may be slight deviations between the low-end case and 12% and the high-end case and 80% for different battery types. This embodiment does not limit this.

[0153] Step 503: Based on the model voltage of the lithium-ion battery, the first voltage change curve in the charging curve and the second voltage change curve in the discharging curve, the performance parameters in the battery model are identified to obtain the performance parameters corresponding to the lithium-ion battery after identification.

[0154] In this embodiment, the parameter identification process refers to modifying the parameters in the battery model based on the difference between the model voltage value and the first voltage change curve, and the difference between the model voltage value and the second voltage change curve, thereby improving the accuracy of the battery model output results. The parameter identification process is implemented using the least squares method.

[0155] Parameter identification involves determining a set of parameter values ​​based on experimental data and a built model, ensuring that the numerical results calculated by the model best fit the test data (a curve fitting problem). This allows for prediction of production processes and provides theoretical guidance. When the error between the calculated numerical results and the test values ​​is large, the model is considered to be inconsistent with the actual process or has a significant discrepancy, leading to model modification and parameter reselection. Therefore, parameter identification is an inverse problem; the quality of parameter estimation determines the reliability of the model's explanation of real-world problems.

[0156] Methods for parameter identification of performance parameters in battery models include, but are not limited to, the following:

[0157] 1. Constrained least squares method;

[0158] 2. Genetic Algorithm.

[0159] Optionally, the parameter identification method used in this embodiment is the constrained least squares method.

[0160] The least squares method is a mathematical tool widely used in many data processing disciplines, including error estimation, uncertainty, system identification and prediction, and forecasting. It is a mathematical optimization technique that finds the best function fit for data by minimizing the sum of squared errors. The least squares method can be used to easily obtain unknown data while minimizing the sum of squared errors between the obtained data and the actual data. It is also commonly used for curve fitting and is the most frequently used method for solving curve fitting problems. Other optimization problems can also be expressed using the least squares method by minimizing energy or maximizing entropy.

[0161] Using the constrained least squares method, based on the model voltage and the first voltage change curve, the performance parameters in the battery model are identified to obtain the charging parameters; based on the model voltage and the second voltage change curve, the performance parameters in the battery model are identified to obtain the discharging parameters; based on the charging parameters, discharging parameters, operating data curves and open-circuit voltage curves, the intrinsic parameters in the performance parameters are obtained.

[0162] 1. When the lithium-ion battery is in the charging working state, the first current change curve in the charging curve is input into the battery model, and the formula is as follows: Vm1=pf(soc*(p_end-p_init)+p_init)-nf(soc*(n_end-n_init)+n_init)+k_ch*I+b_ch;

[0163] Wherein, Vm1 refers to the model voltage of the lithium-ion battery when it is charging.

[0164] The first voltage change curve in the charging curve of a lithium-ion battery represents the actual voltage of the battery under charging conditions. Assuming the actual voltage of the lithium-ion battery under charging conditions is V1, then V1 - Vm1 = η_ch, meaning the difference between the actual voltage and the model voltage during charging is the additional polarization voltage η_ch at the high-end of charging.

[0165] Based on Vm1 and V1, the constrained least squares method is used to fit Vm1 and V1, and the charging parameters in the battery model are identified to obtain the identified charging parameters: k_ch (charging current coefficient), b_ch (charging polarization voltage), and η_ch (charging high-side additional polarization voltage).

[0166] 2. When the lithium-ion battery is in the discharge working state, input the second current change curve in the discharge curve into the battery model, and substitute the following formula: Vm2=pf(soc*(p_end-p_init)+p_init)-nf(soc*(n_end-n_init)+n_init)+k_disch*I+b_disch;

[0167] Wherein, Vm2 refers to the model voltage of a lithium-ion battery under discharge conditions.

[0168] The second voltage change curve in the discharge curve corresponding to a lithium-ion battery represents the actual voltage of the lithium-ion battery under discharge conditions. Assuming the actual voltage of the lithium-ion battery under discharge conditions is V2, then V2 - Vm2 = η_disch, that is, the difference between the actual voltage during discharge and the model voltage is the additional polarization voltage η_disch at the low end of the discharge.

[0169] Based on Vm2 and V2, the constrained least squares method is used to fit Vm2 and V2, and the discharge parameters in the battery model are identified to obtain the identified discharge parameters: k_disch (discharge current coefficient), b_disch (discharge polarization voltage), and η_disch (discharge low-end additional polarization voltage).

[0170] Based on the above steps, after obtaining k_ch (charging current coefficient), b_ch (charging polarization voltage), η_ch (charging high-end additional polarization voltage), k_disch (discharging current coefficient), b_disch (discharging polarization voltage), and η_disch (discharging low-end additional polarization voltage), the six performance parameters identified by parameter identification are substituted into the battery model to further obtain four intrinsic parameters identified by parameter identification: p_init (SOC value corresponding to the low-end cutoff voltage of the positive electrode OCP), p_end (SOC value corresponding to the high-end cutoff voltage of the positive electrode OCP), n_init (SOC value corresponding to the low-end cutoff voltage of the negative electrode OCP), and n_end (SOC value corresponding to the high-end cutoff voltage of the negative electrode OCP).

[0171] Indicative Figure 7 This is a schematic diagram comparing the open-circuit voltage, model voltage, and actual voltage calculated based on a battery model.

[0172] Figure 7 It includes three curves: the model open-circuit voltage curve (OCV) 710, the model voltage curve (model v) 720, and the actual voltage curve (true v) 730.

[0173] It is worth noting that the method for identifying the performance parameters of lithium-ion batteries can be arbitrary, including but not limited to one of the two methods mentioned above. When using a battery model to identify parameters, the current data and voltage data input are the charging curve and discharging curve in the lithium-ion battery operating data curve. This embodiment does not limit this.

[0174] In summary, by designing a battery model, the electromotive force (EMF) curves corresponding to the positive and negative electrode materials of a lithium-ion battery are obtained. Based on these EMF curves, the open-circuit voltage curve of the lithium-ion battery is derived, along with its operating data curves. Using the battery model, the performance parameters of the lithium-ion battery are identified and analyzed, yielding corresponding performance parameters. Based on these performance parameters, the relationship between the reference remaining capacity and the open-circuit voltage curve is determined, allowing for the acquisition of real-time open-circuit voltage data. When a lithium-ion battery ages, the same method can be used to obtain accurate open-circuit voltage data, enabling accurate calculation of the remaining capacity and improving the accuracy of battery state-of-charge estimation.

[0175] The method provided in this embodiment obtains the open-circuit voltage curve of a lithium-ion battery based on the electromotive force curve. The horizontal axis of the open-circuit voltage curve represents the reference remaining capacity, and the vertical axis represents the open-circuit voltage. The working data curve and the open-circuit voltage curve are input into the battery model to obtain the corresponding performance parameters of the lithium-ion battery. Based on the performance parameters, accurate open-circuit voltage data of the lithium-ion battery can be obtained, and the remaining capacity of the lithium-ion battery can be accurately calculated based on the open-circuit voltage data, thereby improving the accuracy of battery state of charge estimation.

[0176] The method provided in this embodiment obtains the open-circuit voltage curve of a lithium-ion battery by subtracting the positive electrode electromotive force curve from the negative electrode electromotive force curve. This method can obtain the open-circuit voltage curve of a lithium-ion battery even when the intrinsic parameters among the performance parameters are unknown, and further fits the relationship expression of the open-circuit voltage curve based on the curve image.

[0177] The method provided in this embodiment obtains the model voltage of the lithium-ion battery by inputting the charging curve and the discharging curve into the battery model; based on the model voltage of the lithium-ion battery, the first voltage change curve in the charging curve and the second voltage change curve in the discharging curve, the performance parameters in the battery model are identified to obtain the performance parameters corresponding to the lithium-ion battery after identification. The identified performance parameters can more accurately represent the characteristics of the lithium-ion battery.

[0178] The method provided in this embodiment identifies the performance parameters in the battery model based on the model voltage and the first voltage change curve to obtain the charging parameters; it then identifies the performance parameters in the battery model based on the model voltage and the second voltage change curve to obtain the discharging parameters; and finally, it obtains the intrinsic parameters based on the charging parameters, discharging parameters, operating data curves, and open-circuit voltage curves. By obtaining the identified performance parameters in sequence, the efficiency of parameter identification is improved, and the characteristics of lithium-ion batteries can be represented more accurately.

[0179] In some optional embodiments, after obtaining the performance parameters after parameter identification, the corresponding curve between the open-circuit voltage and the state of charge of the lithium-ion battery can be determined based on the performance parameters, and a more accurate state of charge of the lithium-ion battery can be obtained. Figure 8 This is a flowchart of a method for determining the correspondence curve between the open-circuit voltage and the state of charge of a lithium-ion battery based on performance parameters, provided by an exemplary embodiment of this application. The method includes the following steps.

[0180] Step 801: Based on the intrinsic parameters and open-circuit voltage curve, obtain the correspondence between the reference remaining capacity and the open-circuit voltage of the lithium-ion battery.

[0181] Calculate the open-circuit voltage of the current aging state. After obtaining the identified intrinsic parameters p_init, p_end, n_init, and n_end according to the above steps, substitute them into the formula for open-circuit voltage:

[0182] OCV=pf(soc*(p_end-p_init)+p_init)-nf(soc*(n_end-n_init)+n_init).

[0183] Here, OCV refers to the open-circuit voltage of the lithium-ion battery, and SOC refers to the state of charge of the lithium-ion battery, which is the reference remaining capacity.

[0184] By assigning a value to the SOC, the corresponding OCV result curve can be obtained.

[0185] Optionally, the SOC is assigned values ​​sequentially from 0 to 100%, increasing by 1% each time. That is, the SOC is assigned values ​​sequentially in the order of SOC = 0, 1%, 2%, 3%...100%, obtaining the OCV value corresponding to each SOC, and these values ​​are fitted into an SOC-OCV curve with the horizontal axis representing the state of charge and the vertical axis representing the open-circuit voltage. This SOC-OCV curve represents the correspondence between the reference remaining charge and the open-circuit voltage of the lithium-ion battery.

[0186] It is worth noting that when assigning values ​​to the state of charge (SOC) to obtain the SOC-OCV curve with the horizontal axis representing the state of charge and the vertical axis representing the open-circuit voltage, the method of assigning values ​​to the state of charge can be arbitrary, and the increment of the value each time the state of charge is assigned can be arbitrary. The correspondence between the reference remaining charge and the open-circuit voltage of the lithium-ion battery obtained by the above method can be of any form, and this embodiment does not limit this.

[0187] It is worth noting that, based on the open-circuit voltage curve, in addition to the relationship with the reference remaining capacity of the lithium-ion battery, the relationship with other data can also be obtained through other arbitrary methods or based on other arbitrary data curves, including but not limited to the relationship with the battery power (SOP) of the lithium-ion battery obtained based on the open-circuit voltage curve, or with the battery capacity (SOE). This embodiment does not limit this.

[0188] Step 802: Update the open-circuit voltage curve based on the correspondence to obtain the corrected open-circuit voltage curve.

[0189] Lithium-ion batteries age after a period of use. The state of charge (SOC) of a lithium-ion battery, or the reference remaining charge, is calculated based on a fresh battery. There is an error between this and the actual remaining charge during use. In other words, the current reference remaining charge is actually an inaccurate remaining charge.

[0190] Based on the correspondence between the reference remaining capacity and the open-circuit voltage of the lithium-ion battery obtained by the above method, the open-circuit voltage curve is updated to obtain a corrected open-circuit voltage curve. The horizontal axis of the corrected open-circuit voltage curve represents the actual remaining capacity, and the vertical axis represents the open-circuit voltage.

[0191] The corrected open-circuit voltage curve can reflect the actual open-circuit voltage data and remaining capacity data of the aged lithium-ion battery. Based on the actual open-circuit voltage data, other data of the lithium-ion battery under working conditions can also be calculated.

[0192] In summary, by designing a battery model and using it to identify and analyze the performance parameters of lithium-ion batteries, the corresponding performance parameters of the lithium-ion batteries can be obtained. Based on these performance parameters, the relationship between the reference remaining capacity and the open-circuit voltage curve of the lithium-ion battery can be determined, allowing for the acquisition of real-time open-circuit voltage data. Furthermore, based on accurate real-time open-circuit voltage data, other data about the lithium-ion battery can be further obtained. When lithium-ion batteries age, the above method can also be used to obtain accurate open-circuit voltage data, and based on this data, the remaining capacity of the lithium-ion battery can be accurately calculated, improving the accuracy of battery state-of-charge estimation.

[0193] Figure 9 This application provides an exemplary embodiment of a device for calculating open-circuit voltage, such as... Figure 9 As shown, the device includes:

[0194] The acquisition module 910 is used to acquire the electromotive force curve of the lithium-ion battery. The electromotive force curve includes the positive electrode electromotive force curve corresponding to the positive electrode material of the lithium-ion battery and the negative electrode electromotive force curve corresponding to the negative electrode material of the lithium-ion battery.

[0195] The acquisition module 910 is further configured to acquire the working data curve of the lithium-ion battery. The working data curve includes the charging curve, discharging curve, and state of charge curve of the lithium-ion battery. The state of charge curve is used to represent the reference remaining capacity of the lithium-ion battery. The charging curve includes a first current change curve and a first voltage change curve. The discharging curve includes a second current change curve and a second voltage change curve.

[0196] Analysis module 920 is used to perform feature analysis on the lithium-ion battery based on the working data curve and the electromotive force curve to obtain the performance parameters corresponding to the lithium-ion battery. The performance parameters include intrinsic parameters for characterizing the state of charge of the lithium-ion battery, charging parameters for characterizing the charging characteristics of the lithium-ion battery, and discharge parameters for characterizing the discharging characteristics of the lithium-ion battery.

[0197] The determination module 930 is used to determine the correspondence between the reference remaining power and the open-circuit voltage of the lithium-ion battery based on the performance parameters.

[0198] In an optional embodiment, the analysis module 920, as... Figure 10 As shown, it includes:

[0199] The acquisition unit 921 is used to obtain the open-circuit voltage curve of the lithium-ion battery based on the electromotive force curve, wherein the horizontal axis of the open-circuit voltage curve is the reference remaining charge and the vertical axis is the open-circuit voltage.

[0200] The input unit 922 is also used to input the working data curve and the open circuit voltage curve into the battery model to obtain the performance parameters corresponding to the lithium-ion battery. The equation corresponding to the battery model is: V=OCV+kI+b+η;

[0201] Wherein, V is the model voltage of the lithium-ion battery, I is the actual measured current of the lithium-ion battery, k is the current coefficient, b is the polarization voltage, η is the additional polarization voltage, and OCV is the open-circuit voltage of the lithium-ion battery; wherein, the current coefficient, the polarization voltage, and the additional polarization voltage are performance parameters of the lithium-ion battery.

[0202] In an optional embodiment, the acquisition unit 921 is further configured to subtract the positive electrode electromotive force curve of the lithium-ion battery from the negative electrode electromotive force curve to obtain the open circuit voltage curve of the lithium-ion battery.

[0203] In an optional embodiment, the apparatus further includes:

[0204] Input module 940 is used to input the charging curve and the discharging curve into the battery model to obtain the model voltage of the lithium-ion battery;

[0205] The identification module 950 is used to identify the performance parameters in the battery model based on the model voltage of the lithium-ion battery, the first voltage change curve in the charging curve and the second voltage change curve in the discharging curve, so as to obtain the performance parameters corresponding to the lithium-ion battery after identification.

[0206] Parameter identification refers to modifying the parameters in the battery model based on the difference between the numerical value corresponding to the model voltage and the first voltage change curve, and the difference between the numerical value corresponding to the model voltage and the second voltage change curve, so as to improve the accuracy of the output results of the battery model. The parameter identification process is implemented using the least squares method.

[0207] In an optional embodiment, the identification module 950 is further configured to perform parameter identification on the performance parameters in the battery model based on the model voltage and the first voltage change curve to obtain the charging parameters among the performance parameters; perform parameter identification on the performance parameters in the battery model based on the model voltage and the second voltage change curve to obtain the discharging parameters among the performance parameters; and obtain the intrinsic parameters among the performance parameters based on the charging parameters, the discharging parameters, the operating data curve, and the open-circuit voltage curve.

[0208] In an optional embodiment, the determining module 930 is further configured to obtain a correspondence between the reference remaining charge and the open-circuit voltage of the lithium-ion battery based on the intrinsic parameters and the open-circuit voltage curve; update the open-circuit voltage curve based on the correspondence to obtain the corrected open-circuit voltage curve, wherein the horizontal axis of the corrected open-circuit voltage curve is the actual remaining charge and the vertical axis is the open-circuit voltage.

[0209] In summary, by designing a battery model, the electromotive force (EMF) curves corresponding to the positive and negative electrode materials of a lithium-ion battery are obtained. Based on these EMF curves, the open-circuit voltage curve of the lithium-ion battery is derived, along with its operating data curves. Using the battery model, the performance parameters of the lithium-ion battery are identified and analyzed, yielding corresponding performance parameters. Based on these performance parameters, the relationship between the reference remaining capacity and the open-circuit voltage curve is determined, allowing for the acquisition of real-time open-circuit voltage data. When a lithium-ion battery ages, the same method can be used to obtain accurate open-circuit voltage data, enabling accurate calculation of the remaining capacity and improving the accuracy of battery state-of-charge estimation.

[0210] It should be noted that the open-circuit voltage calculation device provided in the above embodiments is only an example of the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the open-circuit voltage calculation device and the open-circuit voltage calculation method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0211] Figure 11 A schematic diagram of the structure of a computer device provided in an exemplary embodiment of this application is shown.

[0212] Specifically, computer device 1100 includes a central processing unit (CPU) 1101, a system memory 1104 including random access memory (RAM) 1102 and read-only memory (ROM) 1103, and a system bus 1105 connecting the system memory 1104 and the central processing unit 1101. Computer device 1100 also includes a mass storage device 1106 for storing the operating system 1113, application programs 1114, and other program modules 1115.

[0213] Mass storage device 1106 is connected to central processing unit 1101 via a mass storage controller (not shown) connected to system bus 1105. Mass storage device 1106 and its associated computer-readable media provide non-volatile storage for computer device 1100. That is, mass storage device 1106 may include computer-readable media (not shown) such as hard disk or compact disc read-only memory (CD-ROM) drive.

[0214] Without loss of generality, computer-readable media can include computer storage media and communication media. Computer storage media includes volatile and non-volatile, removable and non-removable media implemented using any method or technology for storing information such as computer-readable instructions, data structures, program modules, or other data. Computer storage media include RAM, ROM, erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other solid-state storage technologies, CD-ROM, digital versatile disc (DVD) or other optical storage, magnetic tape cassettes, magnetic tape, disk storage, or other magnetic storage devices. Of course, those skilled in the art will recognize that computer storage media are not limited to the above-mentioned types. The system memory 1104 and mass storage device 1106 described above can be collectively referred to as memory.

[0215] According to various embodiments of this application, the computer device 1100 can also be connected to a remote computer on a network, such as the Internet. That is, the computer device 1100 can be connected to the network 1112 via the network interface unit 1111 connected to the system bus 1105, or the network interface unit 1111 can be used to connect to other types of networks or remote computer systems (not shown).

[0216] The aforementioned memory also includes one or more programs, which are stored in the memory and configured to be executed by the CPU.

[0217] Embodiments of this application also provide a computer device, which includes a processor and a memory. The memory stores at least one instruction, at least one program, code set, or instruction set. The at least one instruction, at least one program, code set, or instruction set is loaded and executed by the processor to implement the image recognition model training method provided in the above-described method embodiments.

[0218] Embodiments of this application also provide a computer-readable storage medium storing at least one instruction, at least one program, code set, or instruction set, wherein the at least one instruction, at least one program, code set, or instruction set is loaded and executed by a processor to implement the image recognition model training method provided in the above-described method embodiments.

[0219] Embodiments of this application also provide a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the training method for the image recognition model described in any of the above embodiments.

[0220] Optionally, the computer-readable storage medium may include: read-only memory (ROM), random access memory (RAM), solid-state drives (SSDs), or optical discs, etc. The random access memory may include resistive random access memory (ReRAM) and dynamic random access memory (DRAM). The sequence numbers of the embodiments in this application are merely descriptive and do not represent the superiority or inferiority of the embodiments.

[0221] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware or by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0222] The above description is merely an optional embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for calculating open-circuit voltage, characterized in that, The method includes: Obtain the electromotive force curve of the lithium-ion battery, the electromotive force curve including the positive electrode electromotive force curve corresponding to the positive electrode material of the lithium-ion battery and the negative electrode electromotive force curve corresponding to the negative electrode material of the lithium-ion battery. The working data curves of the lithium-ion battery are obtained. The working data curves include the charging curve, discharging curve, and state of charge curve of the lithium-ion battery. The state of charge curve is used to represent the reference remaining capacity of the lithium-ion battery. The charging curve includes a first current change curve and a first voltage change curve. The discharging curve includes a second current change curve and a second voltage change curve. Based on the working data curve and the electromotive force curve, the lithium-ion battery is characterized to obtain the corresponding performance parameters. These performance parameters include intrinsic parameters characterizing the state of charge (SOC), charging parameters characterizing the charging characteristics, and discharging parameters characterizing the discharging characteristics. Specifically, the open-circuit voltage curve of the lithium-ion battery is obtained based on the electromotive force curve, with the horizontal axis representing the reference remaining charge and the vertical axis representing the open-circuit voltage. The working data curve and the open-circuit voltage curve are input into a battery model to obtain the corresponding performance parameters of the lithium-ion battery. The equation corresponding to the battery model is: V = OCV + kI + b + η; where V is the model voltage of the lithium-ion battery, I is the actual measured current of the lithium-ion battery, k is the current coefficient, b is the polarization voltage, η is the additional polarization voltage, and OCV is the open-circuit voltage of the lithium-ion battery. The current coefficient, polarization voltage, and additional polarization voltage are considered performance parameters of the lithium-ion battery. The charging curve and the discharging curve are input into the... A battery model is used to obtain the model voltage of the lithium-ion battery. Based on the model voltage of the lithium-ion battery, the first voltage change curve in the charging curve, and the second voltage change curve in the discharging curve, parameter identification is performed on the performance parameters in the battery model to obtain the identified performance parameters corresponding to the lithium-ion battery. Parameter identification refers to modifying the parameters in the battery model based on the differences between the model voltage value and the first voltage change curve, and the differences between the model voltage value and the second voltage change curve, to improve the accuracy of the battery model's output results. The parameter identification process is implemented using the least squares method. Specifically, based on the model voltage and the first voltage change curve, parameter identification is performed on the performance parameters in the battery model to obtain the charging parameters; based on the model voltage and the second voltage change curve, parameter identification is performed on the performance parameters in the battery model to obtain the discharging parameters; based on the charging parameters, the discharging parameters, the operating data curve, and the open-circuit voltage curve, the intrinsic parameters in the performance parameters are obtained. The correspondence between the reference remaining charge and the open-circuit voltage of the lithium-ion battery is determined based on the performance parameters.

2. The method according to claim 1, characterized in that, The process of obtaining the open-circuit voltage curve of the lithium-ion battery based on the electromotive force curve includes: The open-circuit voltage curve of the lithium-ion battery is obtained by subtracting the positive electrode electromotive force curve from the negative electrode electromotive force curve.

3. The method according to claim 1, characterized in that, Determining the correspondence between the reference remaining capacity and the open-circuit voltage of the lithium-ion battery based on the performance parameters includes: Based on the intrinsic parameters and the open-circuit voltage curve, the correspondence between the reference remaining capacity and the open-circuit voltage of the lithium-ion battery is obtained; The open-circuit voltage curve is updated based on the correspondence to obtain a corrected open-circuit voltage curve, where the horizontal axis of the corrected open-circuit voltage curve represents the actual remaining power and the vertical axis represents the open-circuit voltage.

4. A device for calculating open-circuit voltage, characterized in that, The device includes: The acquisition module acquires the electromotive force curve of the lithium-ion battery, the electromotive force curve including the positive electrode electromotive force curve corresponding to the positive electrode material of the lithium-ion battery and the negative electrode electromotive force curve corresponding to the negative electrode material of the lithium-ion battery. The acquisition module acquires the working data curves of the lithium-ion battery. The working data curves include the charging curve, discharging curve, and state of charge curve of the lithium-ion battery. The state of charge curve is used to represent the reference remaining capacity of the lithium-ion battery. The charging curve includes a first current change curve and a first voltage change curve. The discharging curve includes a second current change curve and a second voltage change curve. The analysis module performs feature analysis on the lithium-ion battery based on the working data curve and the electromotive force curve to obtain the corresponding performance parameters of the lithium-ion battery. These performance parameters include intrinsic parameters characterizing the state of charge of the lithium-ion battery, charging parameters characterizing the charging characteristics of the lithium-ion battery, and discharge parameters characterizing the discharging characteristics of the lithium-ion battery. Specifically, the open-circuit voltage curve of the lithium-ion battery is obtained based on the electromotive force curve, where the horizontal axis of the open-circuit voltage curve represents the reference remaining charge, and the vertical axis represents the open-circuit voltage. The working data... The curves and the open-circuit voltage curve are input to the battery model to obtain the performance parameters corresponding to the lithium-ion battery. The equation corresponding to the battery model is: V = OCV + kI + b + η; where V is the model voltage of the lithium-ion battery, I is the actual measured current of the lithium-ion battery, k is the current coefficient, b is the polarization voltage, η is the additional polarization voltage, and OCV is the open-circuit voltage of the lithium-ion battery; where the current coefficient, the polarization voltage, and the additional polarization voltage are performance parameters of the lithium-ion battery; where the charging curve and the discharging curve are input to... The battery model is used to obtain the model voltage of the lithium-ion battery. Based on the model voltage of the lithium-ion battery, the first voltage change curve in the charging curve, and the second voltage change curve in the discharging curve, the performance parameters in the battery model are identified to obtain the identified performance parameters corresponding to the lithium-ion battery. Parameter identification refers to modifying the parameters in the battery model based on the differences between the model voltage value and the first voltage change curve, and the differences between the model voltage value and the second voltage change curve, to improve the accuracy of the battery model's output results. The parameter identification process uses the least squares method. Specifically, based on the model voltage and the first voltage change curve, the performance parameters in the battery model are identified to obtain the charging parameters; based on the model voltage and the second voltage change curve, the performance parameters in the battery model are identified to obtain the discharging parameters; based on the charging parameters, the discharging parameters, the operating data curve, and the open-circuit voltage curve, the intrinsic parameters in the performance parameters are obtained. The determination module determines the correspondence between the reference remaining power and the open-circuit voltage of the lithium-ion battery based on the performance parameters.

5. A computer device, characterized in that, The computer device includes a processor and a memory, the memory storing at least one program, which is loaded and executed by the processor to implement the method for calculating open-circuit voltage as described in any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that, The storage medium stores at least one program, which is loaded and executed by a processor to implement the method for calculating the open-circuit voltage as described in any one of claims 1 to 3.

7. A computer program product, characterized in that, It includes a computer program that, when executed by a processor, implements the method for calculating the open-circuit voltage as described in any one of claims 1 to 3.

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