CN113447821B - Methods for Assessing Battery State of Charge - Google Patents

Abstract

A method of evaluating a state of charge of a battery is disclosed. Wherein the method comprises the following steps: acquiring charge and discharge parameters, wherein the charge and discharge parameters at least comprise: an open circuit voltage prediction value; acquiring a state equation of a battery state of charge and an open-circuit voltage predicted value in a target battery, wherein the state equation is used for representing a mapping relation between the open-circuit voltage predicted value and the battery state of charge; and carrying out error correction on the predicted value of the open-circuit voltage and the actual value of the open-circuit voltage based on the state equation and a Kalman filtering algorithm model, wherein the Kalman filtering algorithm model is used for representing the relation between the predicted value of the voltage and the actual value of the open-circuit voltage. The method and the device solve the technical problems that in the related art, the requirement on hardware is high when the state of charge of the battery is evaluated, the initial value of the SOC cannot be determined, the hardware resource is wasted, and the evaluation result is inaccurate.

Description

Methods for Assessing Battery State of Charge

technical field

The present application relates to the field of battery detection, in particular, to a method for evaluating the state of charge of a battery.

Background technique

Lithium-ion battery packs have the characteristics of high power and high energy density, and are widely used in electric vehicles and large-scale energy storage systems on the grid side. Battery state of charge (State of Change, SOC), as one of the important indicators to measure the battery utilization potential, is an important basis for battery thermal management, balance management and safety and reliability management, so it has received extensive attention.

Commonly used battery SOC estimation can be roughly divided into two categories: model-based estimation methods and data-based estimation methods. The model-based estimation method is to obtain the parameters characterizing the battery behavior characteristics through battery modeling and identification of corresponding parameters, and then estimate the battery SOC according to the characteristic parameters. The battery model includes an electrochemical model and an equivalent circuit model (Equivalent Circuit Model, ECM). The electrochemical model can obtain the behavioral characteristics of the physical and chemical changes inside the battery by modeling the electrolyte concentration and lithium ion diffusion characteristics. Due to the need to solve partial differential equations and estimate unknown parameters during the establishment of electrochemical models, this method requires high system memory and computing resources, and is not suitable for real-time battery management systems (Battery Management System, BMS). Among the data-based estimation methods, the ampere-hour integration method is most commonly used. This method integrates the current over a period of time to obtain the battery capacity value. Because the initial value of the SOC cannot be determined, a large error is caused. Therefore, in the process of evaluating the state of charge of the battery in the related art, there are technical problems of high requirements on hardware resources and inaccurate evaluation results.

For the above problems, no effective solution has been proposed yet.

Contents of the invention

The embodiment of the present application provides a method for evaluating the state of charge of the battery to at least solve the waste of hardware resources caused by the high hardware requirements in the evaluation of the state of charge of the battery in the related art and the inability to determine the initial value of the SOC, and Technical issues with inaccurate assessment results.

According to an aspect of an embodiment of the present application, there is provided a method for evaluating the state of charge of a battery, including: obtaining charging and discharging parameters, wherein the charging and discharging parameters at least include: a predicted value of the open circuit voltage; obtaining the state of charge of the battery in the target battery and the state equation of the predicted value of the open circuit voltage, where the state equation is used to characterize the mapping relationship between the predicted value of the open circuit voltage and the state of charge of the battery shown; based on the state equation and the Kalman filter algorithm model, the predicted value of the open circuit voltage and the open circuit voltage The actual value is corrected for error, and the Kalman filter algorithm model is used to characterize the relationship between the voltage prediction value and the actual value of the open circuit voltage.

Optionally, before obtaining the predicted value of the open circuit voltage, the method further includes: determining a first equivalent circuit model corresponding to the charging process of the target battery; determining a second equivalent circuit model corresponding to the discharging process of the target battery; A first-order circuit response model corresponding to the second equivalent circuit model to determine the charge and discharge parameters.

Optionally, after determining the first-order circuit response model corresponding to the charge and discharge parameters according to the first equivalent circuit model and the second equivalent circuit model, the method further includes: determining the target value corresponding to the charge and discharge parameters by a graphical method; and based on a The first order circuit response model and the target value are used to obtain the predicted value of the open circuit voltage.

Optionally, determining the target values corresponding to the charging and discharging parameters by a graphical method includes: obtaining the first target change curve of the terminal voltage changing with time under the excitation of the charging pulse current; The segment determines the target value corresponding to the charge and discharge parameters.

Optionally, during the charging process, the predicted value of the open circuit voltage is u _OCV = U _out -Ae ^-t/τ , t>0; wherein, Uocv represents the open circuit voltage, Uout represents the terminal voltage, and A represents the voltage of the resistor during the charging process Drop e to the natural base, and τ is the time constant.

Optionally, determining the target values corresponding to the charge and discharge parameters by a graphical method includes: obtaining a second target change curve of the terminal voltage changing with time under the excitation of the discharge pulse current; using a predetermined curve in the second target change curve segment to determine the target value.

Optionally, during the discharge process, the predicted value of the open circuit voltage is i _OCV = U _out +Ae ^-t/τ , t>0; wherein, Uocv represents the open circuit voltage, Uout represents the terminal voltage, and A represents the voltage of the resistor during the charging process Drop e to the natural base, and τ is the time constant.

Optionally, based on the state equation and the Kalman filter algorithm model, error correction is performed on the predicted value of the open circuit voltage and the actual value of the open circuit voltage, including: obtaining the Kalman filter algorithm model; determining the matrix coefficient parameters of the state equation based on the Kalman filter algorithm model; Adjust the matrix coefficient parameters to correct the error between the predicted value of the open circuit voltage and the actual value of the open circuit voltage.

According to another aspect of the embodiments of the present application, there is also provided a device for evaluating the state of charge of a battery, including: a first acquisition module, configured to acquire charging and discharging parameters, wherein the charging and discharging parameters at least include: a predicted value of open circuit voltage; The second acquisition module is used to acquire the state equation of the battery state of charge and the predicted value of the open circuit voltage in the target battery, wherein the state equation is used to characterize the mapping relationship between the predicted value of the open circuit voltage and the shown battery state of charge; correction The module is used to correct the error between the predicted value of the open circuit voltage and the actual value of the open circuit voltage based on the state equation and the Kalman filter algorithm model, wherein the Kalman filter algorithm model is used to characterize the relationship between the predicted value of the voltage and the actual value of the open circuit voltage.

According to another aspect of the embodiment of the present application, there is also provided a non-volatile storage medium, the non-volatile storage medium includes a stored program, wherein, when the program is running, the device where the non-volatile storage medium is located is controlled to execute any A method of assessing a battery's state of charge.

According to another aspect of the embodiments of the present application, a processor is also provided, and the processor is configured to run a program, wherein, when the program is running, any method for estimating the state of charge of the battery is executed.

In the embodiment of the present application, the method of estimating SOC based on Kalman filter is adopted to obtain the charge and discharge parameters, wherein the charge and discharge parameters at least include: the predicted value of the open circuit voltage; the state of charge of the battery in the target battery and the predicted value of the open circuit voltage are obtained The equation of state, where the equation of state is used to characterize the mapping relationship between the predicted value of the open circuit voltage and the shown battery state of charge; based on the equation of state and the Kalman filter algorithm model to correct the error between the predicted value of the open circuit voltage and the actual value of the open circuit voltage , where the Kalman filter algorithm model is used to characterize the relationship between the predicted value of the voltage and the actual value of the open circuit voltage, to analyze the transient response of the battery charge and discharge under pulse excitation and to identify the ECM parameters, and to estimate the ECM parameters based on the Kalman filter The purpose of SOC, so as to realize the technical effect of saving hardware resources and accurately evaluating the state of charge of the battery, and then solve the problem caused by the high hardware requirements when evaluating the state of charge of the battery in the related technology, and the initial value of SOC cannot be determined. Waste of hardware resources, and technical problems with inaccurate evaluation results.

Description of drawings

The drawings described here are used to provide a further understanding of the application and constitute a part of the application. The schematic embodiments and descriptions of the application are used to explain the application and do not constitute an improper limitation to the application. In the attached picture:

FIG. 1 is a schematic flowchart of a method for evaluating a battery state of charge according to an embodiment of the present application;

Fig. 2 is a schematic diagram of an optional lithium-ion battery Thevenin model according to an embodiment of the present application;

Fig. 3 is a kind of optional lithium-ion battery pulse charging process curve according to the embodiment of the present application;

Fig. 4 is an optional lithium-ion battery pulse discharge process curve according to an embodiment of the present application;

Fig. 5 is a schematic structural diagram of an optional device for evaluating the state of charge of a battery according to an embodiment of the present application.

Detailed ways

In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiment of the application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiment of the application. Obviously, the described embodiment is only It is an embodiment of a part of the application, but not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by persons of ordinary skill in the art without creative efforts shall fall within the scope of protection of this application.

It should be noted that the terms "first" and "second" in the description and claims of the present application and the above drawings are used to distinguish similar objects, but not necessarily used to describe a specific sequence or sequence. It is to be understood that the data so used are interchangeable under appropriate circumstances such that the embodiments of the application described herein can be practiced in sequences other than those illustrated or described herein. Furthermore, the terms "comprising" and "having", as well as any variations thereof, are intended to cover a non-exclusive inclusion, for example, a process, method, system, product or device comprising a sequence of steps or elements is not necessarily limited to the expressly listed instead, may include other steps or elements not explicitly listed or inherent to the process, method, product or apparatus.

According to an embodiment of the present application, an embodiment of a method for evaluating the state of charge of a battery is provided. It should be noted that the steps shown in the flow chart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, Also, although a logical order is shown in the flowcharts, in some cases the steps shown or described may be performed in an order different from that shown or described herein.

Fig. 1 is a method for evaluating the state of charge of a battery according to an embodiment of the present application. As shown in Fig. 1, the method includes the following steps:

Step S102, acquiring charging and discharging parameters, wherein the charging and discharging parameters at least include: a predicted value of open circuit voltage;

Step S104, obtaining the state equation of the battery state of charge and the predicted value of the open circuit voltage in the target battery, wherein the state equation is used to represent the mapping relationship between the predicted value of the open circuit voltage and the indicated battery state of charge;

Step S106 , performing error correction on the predicted value of the open circuit voltage and the actual value of the open circuit voltage based on the state equation and the Kalman filter algorithm model, wherein the Kalman filter algorithm model is used to characterize the relationship between the predicted voltage value and the actual value of the open circuit voltage.

In the method for evaluating the state of charge of the battery, the charging and discharging parameters are obtained, wherein the charging and discharging parameters at least include: a predicted value of the open circuit voltage; obtaining the state equation of the battery state of charge and the predicted value of the open circuit voltage in the target battery, wherein the state equation It is used to characterize the mapping relationship between the predicted value of the open circuit voltage and the displayed battery state of charge; based on the state equation and the Kalman filter algorithm model, the error correction between the predicted value of the open circuit voltage and the actual value of the open circuit voltage is performed. Among them, the Kalman filter algorithm model It is used to characterize the relationship between the predicted voltage value and the actual value of the open circuit voltage. It achieves the purpose of analyzing the transient response of battery charge and discharge under pulse excitation and identifying the ECM parameters, and estimating the SOC based on the Kalman filter, thus realizing the saving Hardware resources, the technical effect of accurately evaluating the state of charge of the battery, and then solving the problem of wasting hardware resources caused by the inability to determine the initial value of the SOC due to the high requirements for hardware in the evaluation of the state of charge of the battery in related technologies, and the inconsistency of the evaluation results. Accurate technical questions.

In some optional embodiments of the present application, before obtaining the predicted value of the open circuit voltage, the first equivalent circuit model corresponding to the charging process of the target battery can be determined; and the second equivalent circuit model corresponding to the discharging process of the target battery can be determined; according to the first The first equivalent circuit model and the second equivalent circuit model determine a first-order circuit response model corresponding to the charging and discharging parameters.

In some embodiments of the present application, after the first-order circuit response model corresponding to the charge and discharge parameters is determined according to the first equivalent circuit model and the second equivalent circuit model, the target value corresponding to the charge and discharge parameters can be determined by a graphical method; and based on a The first order circuit response model and the target value are used to obtain the predicted value of the open circuit voltage.

In some embodiments of the present application, the target values corresponding to the charging and discharging parameters can be determined graphically, specifically, the first target change curve of the terminal voltage changing with time under the excitation of the charging pulse current can be obtained; through the first The predetermined curve segment in the target change curve determines the target value corresponding to the charging and discharging parameter.

It should be noted that during the charging process, the predicted value of the open circuit voltage is u _OCV = U _out -Ae ^-t/τ , t>0; where Uocv represents the open circuit voltage, Uout represents the terminal voltage, and A represents the resistance of the charging process The pressure drop e is the natural base number, and τ is the time constant.

In some embodiments of the present application, the target values corresponding to the charging and discharging parameters can be determined graphically, specifically, the second target change curve of the terminal voltage changing with time under the excitation of the discharge pulse current can be obtained; through the second The predetermined curve segment in the target change curve determines the target value.

It should be noted that during the discharge process, the predicted value of the open circuit voltage is u _OCV = U _out +Ae ^-t/τ , t>0; where Uocv represents the open circuit voltage, Uout represents the terminal voltage, and A represents the resistance during the charging process The pressure drop e is the natural base number, and τ is the time constant.

Specifically, considering the polarization effect inside the battery during charging and discharging of the battery, the equivalent resistance-capacitance Thevenin model of the battery is established. Figure 2 is an optional Thevenin model for lithium-ion batteries, as shown in Figure 2, the model includes: 1 .Equivalent resistance-capacitance model of the charging process (that is, the first equivalent circuit model, as shown on the left in the accompanying drawing) 2. Equivalent resistance-capacitance model of the discharge process (that is, the second equivalent circuit model, such as, in the accompanying drawing shown on the right).

Among them, Et represents the electromotive force, which is equal to the open circuit voltage U _OCV in value. R0 is the ohmic internal resistance of the battery, and the internal polarization resistance R _C , R _D of the battery and the capacitor form a resistance-capacitance circuit. Since the polarization internal resistance and capacitance parameters are different during the charging and discharging process of the battery, R _C -C _C , R _D -C are used _D represents the resistance and capacitance parameters during charging and discharging, respectively.

The first-order circuit response model obtained from the equivalent first-order resistance-capacitance model of the battery is:

U _out (t)＝U _ocv +u _C (t)+u _R (t) (1)

In the process of charging and disconnection without external excitation, the internal resistance and capacitance of the battery polarization constitute the zero-input response of the resistance-capacitance circuit, uC(0+)=Et-0. At t=0, since there is no jump in the capacitor voltage, uC(0+)=uC(0-)=U0, at this time the maximum current in the circuit is i(0+)=uC/R0. When the charging is disconnected, the battery polarization internal resistance RC and the polarization capacitor form a closed resistance-capacitance RC loop, and the capacitor is discharged through the internal resistance RC. When t→∞, UC→0, IC→0, during this process, the energy stored in the polarization capacitance is gradually consumed by the polarization internal resistance in the form of heat energy.

From the analysis of the circuit principle, when t>0:

The initial condition of this first-order homogeneous differential equation is UC(0+)=uC(0-)=U0, and then the solution of the differential equation is:

The current in the circuit and the voltage across the resistor are:

The voltage uC, uR and current i all change according to the same exponential law, and the speed of decay depends on the size of the negative characteristic root p=-1/RC. Define τ as the negative value of the reciprocal of the characteristic root p of the first-order differential equation, that is, τ=-1/p. The parameters of the battery equivalent circuit are determined by the circuit structure and component parameters. The component parameters depend on the aging degree of the battery. τ is a constant with time dimension, and its magnitude reflects the progress speed of the first-order transition process. Table 1 lists the attenuation value of τ capacitor voltage uC at different times.

Table 1 The value of uC at different times

There are three ways to solve the time constant τ: circuit parameter calculation, characteristic root calculation and graphic method determination. The graphical method can detect the zero-input response of the circuit according to the online operation data, and the battery charge and discharge parameters in this application can be obtained through the graphical method.

2.2 Online identification of battery charging parameters

Fig. 3 is a lithium-ion battery pulse charging process curve (that is, the first target change curve). In the lithium-ion battery system in the charging process, under the excitation of the charging pulse current I(t), the change trend of the cut-off voltage Uout is as shown in Fig. 3 (the pulse width of the pulse current I(t) is w, the pulse amplitude is I, and the repetition period is T). It can be seen from Figure 3 that the detection voltage Uout during the charging process is divided into three sections: AB section, corresponding to the charging process of the battery, the polarized capacitance of the battery has been fully charged, the OCV of the battery gradually tends to the OCV corresponding to the maximum SOC, and the terminal voltage gradually changes ; In section BC, the terminal voltage changes suddenly, corresponding to the disconnection process of the battery charging circuit, the polarization internal resistance of the battery suddenly loses the charging current, and the change characteristics detected at this time reflect the characteristics of internal polarization resistance; in section CD, the terminal voltage is exponentially type gradual change, corresponding to the zero response process of the polarized capacitance, the energy stored in the polarized battery is consumed by the polarization internal resistance R as heat energy.

Based on the analysis of the voltage change in the BC segment (ie, the predetermined curve segment in the first target change curve), the polarization internal resistance of the battery during charging can be obtained: Rc=U _BC /I. The change of the CD section curve reflects the disappearance process of R _C polarization, and the internal resistance of battery polarization can be obtained: R _C =(UC(0+)-UC(∞))/I. It is generally believed that after 3τ ~ 5τ time transition ends, the time corresponding to the capacitor voltage u _C dropping 95% within 3τ time can be calculated as τ. At this time, the potential difference U _CD can be regarded as the voltage drop caused by the internal polarization resistance of the battery R _C , and the internal polarization resistance can be obtained as: R _C = U _CD /I, and the polarization capacitance of the battery can be obtained as: C =τ/R _C .

When the resting time is 5τ, the terminal voltage Uout at this time is close to the battery OCV. According to the first-order circuit response, it can be obtained:

u _C ＝u _C (∞)+[u _C (0 ₊ )-u _C (∞)]e ^-t/τ (6)

After a long time, the battery OCV can be introduced as:

u _OCV ＝ U _out -Ae ^-t/τ ,t＞0 (7)

Wherein A represents the amplitude of the exponential curve, representing the voltage drop of the battery polarization internal resistance R _C during charging, which can be expressed as: A=IR _C .

2.3 Online identification of battery discharge parameters

Figure 4 Lithium-ion battery pulse discharge process curve (that is, the second target change curve), in the lithium-ion battery system during the discharge process, under the excitation of the discharge pulse current I(t), the change trend of the cut-off voltage Uout is shown in Figure 4 Show (the pulse width of the pulse current I(t) is w, the pulse amplitude is I, and the repetition period is T). It can be seen from Figure 4 that after the discharge stops, the next discharge Uout can be divided into two sections: the AB section, the battery continuous discharge stage, and the battery terminal voltage Uout continues to drop; the BC section (the predetermined curve section in the second target change curve) : The characteristics of the battery stop discharging are similar to those of pure resistance power supply, which means that the battery is affected by the ohmic internal resistance, and the internal resistance of the battery can be obtained R=U _BC /I; in the CD section, the voltage gradually rises, which is related to the disappearance of the polarization of the battery, which can be obtained Battery polarization internal resistance R _D = U _CD /I, when U _CD rises to 94% of the voltage at point C, the corresponding time is 3τ, from τ = R _{D CD} , we get _CD = τ/R _D . After 5τ time, the open-circuit voltage u _C is considered to be close to OCV, which can be deduced according to the complete response formula (6) of the first-order circuit:

u _OCV ＝ U _out +Ae ^-t/τ , t＞0 (8)

Wherein A represents the amplitude of the exponential curve, representing the voltage rise of the internal polarization resistance _RD of the battery after discharge, which can be expressed as: A=IR _D .

In some optional embodiments of the present application, error correction can be performed on the predicted value of the open circuit voltage and the actual value of the open circuit voltage based on the state equation and the Kalman filter algorithm model. Specifically, the Kalman filter algorithm model can be obtained; based on the Kalman filter algorithm The model determines the matrix coefficient parameters of the state equation; adjusts the matrix coefficient parameters to correct the error between the predicted value of the open circuit voltage and the actual value of the open circuit voltage.

Understandably, the SOC estimation error is caused by the estimation of the time constant τ and the OCV-SOC mapping relationship. The Kalman filter method can be used to make up the error between the actual state value and the observed value, and can correct the SOC estimation error caused by inaccurate OCV identification. According to the equivalent resistance-capacitance model and the battery ampere-hour integral model, the state equation is obtained:

Among them, SOC _t represents the battery SOC value at time t, μ represents the charging and discharging efficiency of the battery, and _Q0 represents the initial capacity of the battery. R represents the equivalent internal resistance of the battery, and R' and C represent the charge and discharge polarization internal resistance and polarization capacitance, respectively, and their values can be obtained through online parameter identification. U _{out, t} and I _t represent the actual measured variables at time t, respectively.

Establish the classic extended Kalman filter algorithm model:

X _t = AX _t-1 + Bu _k-1

Z _k ＝CX _k +v _k (10)

Among them, X _k represents the system state variable (actual quantity), Z _k represents the system observation variable (predicted quantity), v _k represents the system observation noise, u _k represents the system excitation. According to the algorithm model, the matrix coefficient parameters of the state space model for battery SOC estimation can be expressed as:

Fig. 5 is a device for evaluating the state of charge of a battery according to an embodiment of the present application. As shown in Fig. 5, the device includes:

The first acquisition module 40 is configured to acquire charging and discharging parameters, wherein the charging and discharging parameters at least include: a predicted value of open circuit voltage;

The second acquisition module 42 is configured to acquire the state equation of the battery state of charge and the predicted value of the open circuit voltage in the target battery, wherein the state equation is used to represent the mapping relationship between the predicted value of the open circuit voltage and the shown battery state of charge;

The correction module 44 is used to correct the error between the predicted value of the open circuit voltage and the actual value of the open circuit voltage based on the state equation and the Kalman filter algorithm model, wherein the Kalman filter algorithm model is used to characterize the difference between the predicted value of the voltage and the actual value of the open circuit voltage relation.

In the device for evaluating the state of charge of the battery, the first acquisition module 40 is used to acquire the charging and discharging parameters, wherein the charging and discharging parameters include at least: the predicted value of the open circuit voltage; the second acquiring module 42 is used to acquire the battery in the target battery The state equation of the state of charge and the predicted value of the open circuit voltage, wherein the state equation is used to characterize the mapping relationship between the predicted value of the open circuit voltage and the shown battery state of charge; the correction module 44 is used for the calculation based on the state equation and the Kalman filter algorithm The model corrects the error between the predicted value of the open circuit voltage and the actual value of the open circuit voltage. Among them, the Kalman filter algorithm model is used to characterize the relationship between the predicted value of the voltage and the actual value of the open circuit voltage, and achieves the transient state of battery charge and discharge under pulse excitation. Response analysis and identification of ECM parameters, and the purpose of estimating SOC based on Kalman filter, thus realizing the technical effect of saving hardware resources and accurately evaluating the battery state of charge, and then solving the problem of battery state of charge evaluation in related technologies. The hardware requirements are high, and the initial value of SOC cannot be determined, which causes waste of hardware resources and technical problems of inaccurate evaluation results.

According to another aspect of the embodiment of the present application, there is also provided a non-volatile storage medium, the non-volatile storage medium includes a stored program, wherein, when the program is running, the device where the non-volatile storage medium is located is controlled to execute any A method of assessing a battery's state of charge.

Specifically, the above-mentioned storage medium is used to store program instructions for performing the following functions, so as to realize the following functions:

Acquire charge and discharge parameters, wherein the charge and discharge parameters include at least: a predicted value of open circuit voltage; obtain the state equation of the state of charge of the battery in the target battery and the predicted value of open circuit voltage, wherein the state equation is used to characterize the predicted value of open circuit voltage and the shown The mapping relationship between the battery state of charge; based on the state equation and the Kalman filter algorithm model to correct the error between the predicted value of the open circuit voltage and the actual value of the open circuit voltage. relationship between values.

According to another aspect of the embodiments of the present application, a processor is also provided, and the processor is configured to run a program, wherein, when the program is running, any method for estimating the state of charge of the battery is executed.

Specifically, the above-mentioned processor is used to call program instructions in the memory to realize the following functions:

Acquire charge and discharge parameters, wherein the charge and discharge parameters include at least: a predicted value of open circuit voltage; obtain the state equation of the state of charge of the battery in the target battery and the predicted value of open circuit voltage, wherein the state equation is used to characterize the predicted value of open circuit voltage and the shown The mapping relationship between the battery state of charge; based on the state equation and the Kalman filter algorithm model to correct the error between the predicted value of the open circuit voltage and the actual value of the open circuit voltage. relationship between values.

The serial numbers of the above embodiments of the present application are for description only, and do not represent the advantages and disadvantages of the embodiments.

In the above-mentioned embodiments of the present application, the descriptions of each embodiment have their own emphases, and for parts not described in detail in a certain embodiment, reference may be made to relevant descriptions of other embodiments.

In the several embodiments provided in this application, it should be understood that the disclosed technical content can be realized in other ways. Wherein, the device embodiments described above are only illustrative. For example, the division of the units may be a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components may be combined or may be Integrate into another system, or some features may be ignored, or not implemented. In another point, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces, and the indirect coupling or communication connection of units or modules may be in electrical or other forms.

The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple units. Part or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, each unit may exist separately physically, or two or more units may be integrated into one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

If the integrated unit is realized in the form of a software function unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or part of the contribution to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium , including several instructions for enabling a computer device (which may be a personal computer, server or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes. .

The above description is only the preferred embodiment of the present application. It should be pointed out that for those of ordinary skill in the art, without departing from the principle of the present application, some improvements and modifications can also be made. These improvements and modifications are also It should be regarded as the protection scope of this application.

Claims (7)

1. A method for evaluating the state of charge of a battery, comprising: Acquiring charge and discharge parameters, wherein the charge and discharge parameters include at least: a predicted value of open circuit voltage; Obtaining an equation of state between the state of charge of the battery in the target battery and the predicted value of the open circuit voltage, wherein the equation of state is used to characterize the mapping relationship between the predicted value of the open circuit voltage and the indicated state of charge of the battery; Error correction is performed on the predicted value of the open circuit voltage and the actual value of the open circuit voltage based on the state equation and the Kalman filter algorithm model, wherein the Kalman filter algorithm model is used to characterize the predicted value of the voltage and the actual value of the open circuit voltage the relationship between the values; Before obtaining the predicted value of the open circuit voltage, the method further includes: determining a first equivalent circuit model corresponding to the target battery charging process; determining a second equivalent circuit model corresponding to the target battery discharging process; The circuit model and the second equivalent circuit model determine a first-order circuit response model corresponding to the charge and discharge parameters; determine the target value corresponding to the charge and discharge parameters by a graphical method; and based on the first-order circuit response model and the The target value is obtained to obtain the predicted value of the open circuit voltage; Wherein, determining the target value corresponding to the charging and discharging parameter by a graphical method includes: obtaining a first target change curve of the terminal voltage changing with time under the excitation of the charging pulse current; The predetermined curve section determines the target value corresponding to the charge and discharge parameters, and the first target change curve is the pulse charging process curve of the lithium-ion battery. The curve is divided into three sections, wherein the first section corresponds to the charging process of the battery, and the second section The first segment corresponds to the disconnection process of the battery charging circuit, and the third segment corresponds to the zero response process of the polarized capacitor; Determining the target value corresponding to the charging and discharging parameters through the predetermined curve segment in the first target change curve includes: obtaining the battery polarization resistance in the charging process of the battery through the second segment of the curve in the pulse charging process curve of the lithium-ion battery , wherein, the battery polarization resistance in the charging process of the battery is determined by the following method: Rc=U _BC /I, wherein, Rc represents the battery polarization resistance during battery charging, U _BC represents the potential difference of the open circuit voltage at both ends corresponding to the second curve, and I represents the pulse amplitude. 2. The method according to claim 1, characterized in that, during the charging process, the predicted value of the open circuit voltage is u _OCV = U _out -Ae ^-t/τ , t>0; Among them, Uocv represents the open circuit voltage, Uout represents the terminal voltage, A represents the voltage drop of the resistance during charging, e is the natural base, and τ is the time constant. 3. The method according to claim 1, characterized in that determining the target value corresponding to the charging and discharging parameters by a graphical method comprises: Obtaining a second target change curve of the terminal voltage changing with time under the excitation of the discharge pulse current; The target value is determined through a predetermined curve segment in the second target change curve. 4. The method according to claim 3, characterized in that, in the discharge process, the predicted value of the open circuit voltage is _uOCV = _Uout +Ae ^-t/τ , t>0; Among them, Uocv represents the open circuit voltage, Uout represents the terminal voltage, A represents the voltage drop of the resistance during charging, e is the natural base, and τ is the time constant. 5. The method according to claim 1, characterized in that, based on the state equation and the Kalman filter algorithm model, error correction is carried out to the predicted value of the open circuit voltage and the actual value of the open circuit voltage, including: Obtain the Kalman filter algorithm model; Determine the matrix coefficient parameters of the state equation based on the Kalman filter algorithm model; Adjusting the matrix coefficient parameters performs error correction on the predicted value of the open circuit voltage and the actual value of the open circuit voltage. 6. A device for evaluating the state of charge of a battery, comprising: A first acquisition module, configured to acquire charge and discharge parameters, wherein the charge and discharge parameters at least include: a predicted value of open circuit voltage; The second obtaining module is used to obtain the state equation of the state of charge of the battery in the target battery and the predicted value of the open circuit voltage, wherein the state equation is used to characterize the relationship between the predicted value of the open circuit voltage and the indicated state of charge of the battery The mapping relationship between; A correction module, configured to perform error correction on the predicted value of the open circuit voltage and the actual value of the open circuit voltage based on the state equation and the Kalman filter algorithm model, wherein the Kalman filter algorithm model is used to characterize the relationship between the predicted voltage value and the actual value of the open circuit voltage The relationship between the actual values of the open circuit voltage; Before obtaining the predicted value of the open circuit voltage, it also includes: determining the first equivalent circuit model corresponding to the target battery charging process; determining the second equivalent circuit model corresponding to the target battery discharging process; according to the first equivalent circuit model and The second equivalent circuit model determines the first-order circuit response model corresponding to the charge and discharge parameters; determines the target value corresponding to the charge and discharge parameters by a graphical method; and based on the first-order circuit response model and the target value , to obtain the predicted value of the open circuit voltage; Wherein, determining the target value corresponding to the charging and discharging parameter by a graphic method includes: obtaining a first target change curve of the terminal voltage changing with time under the excitation of the charging pulse current; The predetermined curve section determines the target value corresponding to the charge and discharge parameters, and the first target change curve is the pulse charging process curve of the lithium-ion battery. The curve is divided into three sections, wherein the first section corresponds to the charging process of the battery, and the second section The first segment corresponds to the disconnection process of the battery charging circuit, and the third segment corresponds to the zero response process of the polarized capacitor; Determining the target value corresponding to the charging and discharging parameters through the predetermined curve segment in the first target change curve includes: obtaining the battery polarization resistance in the charging process of the battery through the second segment of the curve in the pulse charging process curve of the lithium-ion battery , wherein, the battery polarization resistance of the battery charging process is determined by the following method: Rc=U _BC /I, wherein, Rc represents the battery polarization resistance of the battery charging process, and U _BC represents the open circuit voltage of the two ends corresponding to the second section of the curve Potential difference, I represents the pulse amplitude. 7. A non-volatile storage medium, characterized in that the non-volatile storage medium includes a stored program, wherein when the program is running, the device where the non-volatile storage medium is located is controlled to execute the claim The method for evaluating the state of charge of the battery described in any one of 1 to 5.

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