SOC-OCV curve online estimation method of battery

Abstract

The invention provides an SOC-OCV curve online estimation method of a battery, and belongs to the field of energy storage batteries, and the method comprises the steps: representing a state transition equation and a measurement equation of a Thevenin equivalent second-order RC model of the battery, and enabling the state transition equation and the measurement equation to be related to a first polarization capacitor C1, a first polarization resistor R1, a second polarization capacitor C2, a second polarization resistor R2 and an ohmic internal resistance R0 of the Thevenin equivalent second-order RC model; obtaining an SOC-{R0, R1, R2, C1, C2} table of the battery; and looking up a table to obtain a first polarization capacitance C1, a first polarization resistance R1, a second polarization capacitance C2, a second polarization resistance R2 and an ohmic internal resistance R0 under each SOC, and substituting the first polarization capacitance C1, the first polarization resistance R1, the second polarization capacitance C2, the second polarization resistance R2 and the ohmic internal resistance R0 into the state transition equation and the measurement equation to calculate and obtain an open-circuit voltage Uoc of the battery corresponding to each SOC, thereby obtaining an SOC-OCV curve of the battery. And the SOC-OCV curve of the battery can be obtained on line in real time.

Description

Technical Field

[0001] The present application relates to the field of energy storage batteries, and in particular to an online estimation method for a SOC-OCV curve of a battery. Background Art

[0002] In recent years, in order to solve the energy crisis, green energy is being vigorously developed. Electric vehicles replacing traditional fuel vehicles are a typical example of promoting this development and utilization.

[0003] Batteries that power electric vehicles have become a hot topic for related industries and academia. Lithium (ion) batteries are being widely used due to a series of advantages, such as higher power density, faster charging speed, smaller memory effect, lower maintenance cost, high open circuit voltage and long life. Lithium (ion) batteries are also used in many other scenarios.

[0004] During the use of the battery, it is very important to monitor the internal parameters of the battery cell to ensure that it operates in a safe and operational area and to let the user know the status of the battery.

[0005] The state of charge (SOC) of a cell and / or a battery pack is a representation of the (remaining) capacity of the cell and battery pack. The open circuit voltage U oc It is also an important parameter. Obtaining the relationship curve between the battery's SOC-OCV (state of charge-open circuit voltage) is an effective way to monitor the battery status.

[0006] However, in different working environments, the relationship curve between the battery state of charge and its open circuit voltage is different, so there are certain difficulties in estimating the SOC-OCV curve. Traditional estimation methods have problems such as complex steps and inability to be implemented online. Summary of the invention

[0007] The present application provides an online estimation method for the SOC-OCV curve of a battery, comprising: S1: establishing a Thevenin equivalent second-order RC model of a battery; S2: characterizing a state transfer equation and a measurement equation of the Thevenin equivalent second-order RC model, wherein the state transfer equation and the measurement equation are related to a first polarization capacitor C1, a first polarization resistor R1, a second polarization capacitor C2, a second polarization resistor R2, and an ohmic internal resistance R0 of the Thevenin equivalent second-order RC model; S3: obtaining the first polarization capacitor C1, the first polarization resistor R1, the second polarization capacitor C2, the second polarization resistor R2, and the ohmic internal resistance R0 of the Thevenin equivalent second-order RC model of the battery at different SOCs, and forming a SOC-{R0, R1, R2, C1, C2} table; S4: calculating the SOC-{R0, R1, R2, C1, C2} table according to the SOC-{R0, R1, R2, C1, C2} table;

[0008] The first polarization capacitor C1, the first polarization resistor R1, the second polarization capacitor C2, the second polarization resistor R2, and the ohmic internal resistance R0 under each SOC are obtained by looking up the table, and the first polarization capacitor C1, the first polarization resistor R1, the second polarization capacitor C2, the second polarization resistor R2, and the ohmic internal resistance R0 obtained by looking up the table are substituted into the state transfer equation and the measurement equation to calculate the open circuit voltage U of the battery corresponding to each SOC oc , thereby obtaining the SOC-OCV curve of the battery.

[0009] Furthermore, step S2 includes: S21: obtaining the open circuit voltage state transfer equation of the battery based on Kirchhoff's circuit law; S22: approximating the ohmic internal resistance R0 of the battery to obtain the ohmic internal resistance R0 expression; S23: obtaining the state transfer equation and the measurement equation characterizing the Thevenin equivalent second-order RC model based on Kirchhoff's circuit law, the open circuit voltage state transfer equation, and the ohmic internal resistance R0 expression, wherein the state transfer equation and the measurement equation are also related to the observation noise.

[0010] Furthermore, the open circuit voltage state transition equation is related to U oc,k-1 and U oc,k-2 Related, where U oc,k-1 is the open circuit voltage of the battery at the k-1th moment, U oc,k-2 is the open circuit voltage of the battery at the k-2th moment.

[0011] Furthermore, the open circuit voltage state transition equation is:

[0012]

[0013] Among them U oc,kis the open circuit voltage of the battery at the kth moment.

[0014] Furthermore, the ohmic internal resistance R0 is expressed as:

[0015] R 0,k =R 0,k-1 +r k-1 (15)

[0016] where r k-1 is the error, R 0,k-1 is the ohmic internal resistance at the k-1th moment.

[0017] Furthermore, the state transfer equation is:

[0018]

[0019] The measurement equation is:

[0020] y k =U k =C k x k +v k (17)

[0021] in:

[0022]

[0023] C k =[1011 I k ] (20)

[0024] Among them U 1,k is the polarization voltage across the first polarization capacitor C1 and the first polarization resistor R1 connected in parallel in the Thevenin equivalent second-order RC model at the kth moment, U 2,k is the polarization voltage across the second polarization capacitor C2 and the second polarization resistor R2 connected in parallel in the Thevenin equivalent second-order RC model at the kth moment, U 1,k-1 is the polarization voltage across the first polarization capacitor C1 and the first polarization resistor R1 connected in parallel in the Thevenin equivalent second-order RC model at the k-1th moment, U 2,k-2 is the polarization voltage across the second polarization capacitor C2 and the second polarization resistor R2 connected in parallel in the Thevenin equivalent second-order RC model at the k-2th moment, R 0,k is the ohmic internal resistance at the kth moment, I k-1 is the battery current at the k-1th moment, U k is the battery terminal voltage of the Thevenin equivalent second-order RC model at the kth moment, W k is the state noise, V kis the observation noise, and Δt is the algorithm calculation time, which can be set to 1s.

[0025] Furthermore, the state noise W k Is a constant.

[0026] Furthermore, step S23 also includes: using wavelet transform and Gaussian scale mixture distribution to describe the unknown observation noise distribution.

[0027] Furthermore, step S3 includes: estimating the SOC of the battery by the ampere-hour integration method, and measuring the first polarization capacitor C1, the first polarization resistor R1, the second polarization capacitor C2, the second polarization resistor R2, and the ohmic internal resistance R0 at each SOC to form the SOC-{R0, R1, R2, C1, C2} table.

[0028] Furthermore, the battery is a lithium battery.

[0029] The features and technical advantages of the present disclosure have been summarized quite extensively above so that the detailed description disclosed below may be better understood. Additional features and advantages of the present disclosure will be described below, which constitute the subject matter of the claims of the present disclosure. It will be appreciated by those skilled in the art that the disclosed concepts and specific embodiments may be easily used as a basis for modifying or designing other structures or processes for achieving the same purpose of the present disclosure. It will also be appreciated by those skilled in the art that such equivalent structures do not depart from the spirit and scope of the present disclosure as set forth in the appended claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] For a more complete understanding of the present disclosure and its advantages, reference is now made to the following description in conjunction with the accompanying drawings, in which:

[0031] Figure 1 A schematic diagram of a process flow of an online SOC-OCV curve estimation method for a battery according to an embodiment of the present application is shown;

[0032] Figure 2 A schematic diagram of a typical second-order Thevenin equivalent circuit model of a battery is shown;

[0033] Figure 3 A flow chart showing a state transfer equation and a measurement equation for a Thevenin equivalent second-order RC model characterizing a battery according to an embodiment of the present invention is shown.

[0034] Unless otherwise indicated, corresponding parts and symbols in the different figures generally refer to corresponding parts. These figures are drawn to clearly illustrate the relevant aspects of the various embodiments and are not necessarily drawn to scale. DETAILED DESCRIPTION

[0035] The following will be combined with the accompanying drawings to clearly and completely describe the technical solutions in this application. Obviously, the described embodiments are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0036] The present application provides a method for online estimation of the SOC-OCV curve of a battery (such as a lithium battery). Figure 1 The flowchart of the online estimation method of the SOC-OCV curve of a battery according to an embodiment of the present application is shown, and the method includes:

[0037] S1: Establish the Thevenin equivalent second-order RC model of the battery;

[0038] S2: a state transfer equation and a measurement equation characterizing the Thevenin equivalent second-order RC model, wherein the state transfer equation and the measurement equation are related to a first polarization capacitor C1, a first polarization resistor R1, a second polarization capacitor C2, a second polarization resistor R2, and an ohmic internal resistance R0 of the Thevenin equivalent second-order RC model;

[0039] S3: Obtain the first polarization capacitor C1, the first polarization resistor R1, the second polarization capacitor C2, the second polarization resistor R2, and the ohmic internal resistance R0 of the Thevenin equivalent second-order RC model of the battery at different SOCs to form a SOC-{R0, R1, R2, C1, C2} table;

[0040] S4: According to the SOC-{R0, R1, R2, C1, C2} table, the first polarization capacitor C1, the first polarization resistor R1, the second polarization capacitor C2, the second polarization resistor R2, and the ohmic internal resistance R0 are obtained by looking up the table, and the first polarization capacitor C1, the first polarization resistor R1, the second polarization capacitor C2, the second polarization resistor R2, and the ohmic internal resistance R0 obtained by looking up the table are substituted into the state transfer equation and the measurement equation to calculate the open circuit voltage U of the battery corresponding to each SOC oc , thereby obtaining the SOC-OCV curve of the battery.

[0041] As we all know, after the battery is charged and discharged, when its charge and discharge current instantly becomes 0, its terminal voltage will also jump at this moment, and then slowly tend to stabilize. This transient phenomenon is called "hysteresis or polarization effect."

[0042] The traditional Thevenin equivalent circuit model is generally composed of circuit elements such as resistors, capacitors and power supplies, and uses an RC network to describe the dynamic characteristics of the battery caused by lithium ion polarization. For example: first-order RC model, second-order RC model and high-order RC model, among which the second-order RC model is widely used, which can be referred to Figure 2 The typical Thevenin equivalent second-order RC model schematic diagram of the battery shown. Figure 2 As shown, U oc is the open circuit voltage of the battery; R0 is the ohmic internal resistance of the battery; C1, C2, R1, R2 are model parameters that describe the transient process of the battery terminal voltage U and the battery current I caused by the polarization of ions during the charge / discharge process. They are also called polarization capacitors and resistors. The first polarization capacitor C1 and the first polarization resistor R1 are connected in parallel, and the voltage across them is expressed as U1. The second polarization capacitor C2 and the second polarization resistor R2 are connected in parallel, and the voltage across them is expressed as U2. U1 and U2 are also called polarization voltages. Figure 2 As shown, the Thevenin equivalent second-order RC model includes the open circuit voltage U oc , a loop formed by a first polarized capacitor C1 and a first polarized resistor R1 connected in parallel, a second polarized capacitor C2 and a second polarized resistor R2 connected in parallel, the ohmic internal resistance R0 of the battery, and the battery terminal voltage U, and the current flowing through the loop is I.

[0043] Then, based on Figure 2 The Thevenin equivalent second-order RC model shown can obtain the state transfer equation and measurement equation that characterize it, wherein the state transfer equation and the measurement equation are related to the first polarized capacitor C1, the first polarized resistor R1, the second polarized capacitor C2, the second polarized resistor R2, and the ohmic internal resistance R0, which is the above-mentioned step S2.

[0044] Specifically, step S2 includes: S21: obtaining the open circuit voltage state transfer equation of the battery based on Kirchhoff's circuit law; S22: approximating the ohmic internal resistance R0 of the battery to obtain the ohmic internal resistance R0 expression; S23: obtaining the state transfer equation and the measurement equation characterizing the Thevenin equivalent second-order RC model based on Kirchhoff's circuit law, the open circuit voltage state transfer equation, and the ohmic internal resistance R0 expression, wherein the state transfer equation and the measurement equation are also related to the observation noise. Figure 3 The flowchart shown is a schematic diagram of the state transfer equation and the measurement equation of the Thevenin equivalent second-order RC model for characterizing a battery according to one embodiment of the present invention.

[0045] Specifically, S21: the process of obtaining the open circuit voltage state transition equation of the battery based on Kirchhoff's circuit law is described as follows. Figure 2The Thevenin equivalent second-order RC model of the battery in the middle can be obtained according to Kirchhoff's law:

[0046] U k =U oc,k +U 1,k +U 2,k +I k R 0,k (1)

[0047] Increase the observation noise v k After that, formula (1) can be changed to formula (2):

[0048] U k =U oc,k +U 1,k +U 2,k +I k R 0,k +v k (2)

[0049] Among them, U k is the battery terminal voltage at the kth moment, U oc,k is the open circuit voltage at the kth moment, U 1,k is the polarization voltage across the first polarization capacitor C1 and the first polarization resistor R1 at the kth moment, U 2,k is the polarization voltage across the second polarized capacitor C2 and the second polarized resistor R2 at the kth moment, I k is the battery current at the kth moment, R 0,k is the ohmic internal resistance at the kth moment, v k is the observation noise of the battery at the kth moment.

[0050] Since the open circuit voltage U oc,k It is time-varying, so it is a function that changes with time k. According to Weierstrass approximation theory, any continuous function on a closed interval can be approximated to the expected accuracy by a polynomial function. Therefore, assuming that the open circuit voltage U in a closed interval oc,k , can be described by an L-order polynomial, namely:

[0051]

[0052] Where: p(l) is the coefficient of the uniform approximation polynomial. If the battery terminal voltage U at the kth moment oc,k It can be determined by its previous value U oc,k-1 ,U oc,k-2 ,...,U oc,k-D To make a prediction, we have:

[0053]

[0054] Obviously, the coefficient h(d) (d = 1, ..., D) in equation (4) constitutes a FIR filter. Substituting equation (4) into equation (3), we can get:

[0055]

[0056] According to formula (5), we can get:

[0057]

[0058] Eliminate p(l) in equation (6). When the following constraints are met, equation (6) always holds.

[0059]

[0060] Where: Formula (7) is the constraint condition of the coefficient h(d), but h(d) cannot be uniquely determined by formula (7) alone, and other constraints are required.

[0061]

[0062] When it is minimum, the filter coefficient h(d) can be calculated by Lagrange number multiplication.

[0063] When L=1:

[0064] h(d)=4D-6d+2 / D(D-1) (7)

[0065] When L=2:

[0066] h(d)=9D 2 +(9-36d)D+30d 2 -18d+6 / D 3 -D 2 +2D (8)

[0067] At the open circuit voltage U oc In the modeling, when L = 1, D = 2 is selected. Substituting into equation (3) and equation (9), we can get:

[0068] U oc,k =h(0)U oc,k-1 +h(1)U oc,k-2 (9)

[0069] Where: h(k) = 2-3k (10) Therefore, formula (11) can be written as: U oc,k =2U oc,k-1 -U oc,k-2 (11) Then, equation (13) can be written as the following state transfer equation:

[0070]

[0071] In this way, the open circuit voltage state transition equation of the battery is obtained based on Kirchhoff's circuit law. As shown in formula (14), the open circuit voltage state transition equation of the battery is related to U oc,k-1 and U oc,k-2 Related, where U oc,k-1 is the open circuit voltage of the battery at the k-1th moment, U oc,k-2 is the open circuit voltage of the battery at the k-2th moment.

[0072] Step S22: The process of approximating the ohmic internal resistance R0 of the battery to obtain the expression of the ohmic internal resistance R0 is described as follows. Similar to the open circuit voltage of the battery, the ohmic internal resistance R0 (time-varying) of the battery can also be approximated by a polynomial function on any closed interval. Since the model sampling interval is very short, the ohmic internal resistance R0 can be well approximated by a 0th order polynomial in a closed interval formed by the sampling interval, that is, the random walk model is used to characterize the time-varying ohmic internal resistance R0 as follows:

[0073] R 0,k =R 0,k-1 +r k-1 (13)

[0074] where r k-1 is the error, R 0,k-1 is the ohmic internal resistance of the battery at the k-1th moment.

[0075] Then, step S23 is performed, that is, based on Kirchhoff's circuit law, the open circuit voltage state transfer equation of the battery, and the ohmic internal resistance R0 expression, the state transfer equation and the measurement equation characterizing the Thevenin equivalent second-order RC model are obtained, wherein the state transfer equation and the measurement equation are also related to the observation noise v k Related.

[0076] Specifically, combining equations (2), (14) and (15), the state transfer equation and measurement equation of the Thevenin equivalent second-order RC model of the battery can be expressed as equations (16) and (17), respectively:

[0077]

[0078] y k =U k =C k x k +v k (17)

[0079] in:

[0080]

[0081]

[0082] C k =[1011 I k ] (20)

[0083] Among them U 1,k is the polarization voltage across the first polarization capacitor C1 and the first polarization resistor R1 connected in parallel in the Thevenin equivalent second-order RC model at the kth moment, U 2,k is the polarization voltage across the second polarization capacitor C2 and the second polarization resistor R2 connected in parallel in the Thevenin equivalent second-order RC model at the kth moment, U 1,k-1 is the polarization voltage across the first polarization capacitor C1 and the first polarization resistor R1 connected in parallel in the Thevenin equivalent second-order RC model at the k-1th moment, U 2,k-2 is the polarization voltage across the second polarization capacitor C2 and the second polarization resistor R2 connected in parallel in the Thevenin equivalent second-order RC model at the k-2th moment, R 0,k is the ohmic internal resistance at the kth moment, I k-1 is the battery current at the k-1th moment, U k is the battery terminal voltage of the Thevenin equivalent second-order RC model at the kth moment. The above parameters can be obtained by offline testing, and of course can also be obtained by other methods, which is not limited in this application. k is the state noise, V k is the observation noise, and Δt is the algorithm calculation time, which can be set to 1s.

[0084] From the state transfer equation (16), it can also be seen that the open circuit voltage U oc,k and the open circuit voltage U of the battery at the k-1th moment oc,k-1 And the open circuit voltage U of the battery at the k-2th moment oc,k-2 Related.

[0085] In actual implementation, it is found through research that the state noise W k It can be set as a constant. Then from equations (16) and (17), it can be seen that if you want to get the accurate open circuit voltage U of the battery oc,k , we need to get accurate observation noise V k As described above, the open circuit voltage U of the battery is oc,k is Gaussian distributed, then the observation noise V k It also needs to be Gaussian distributed.

[0086] Specifically, due to the observation noise V kThe actual change of the battery terminal voltage is generally unknown, so the Gaussian distribution is used to describe the unknown noise V k The description is inaccurate. Using the probability distribution function of Gaussian scale mixture, any probability distribution function form can be approximated. Therefore, this paper adopts Gaussian scale mixture distribution to approximate the unknown observation noise V k The distribution is described as:

[0087] p(v k ,ω k |λ k ,∑ k )=N(v k |0,ω k ∑ k )p(ω k |λ k ) (21)Where:∑ k is the scale matrix, ω k is an auxiliary random variable defined on R+ that characterizes the scale coefficient of the Gaussian distribution, λ k is a positive scale parameter, which satisfies:

[0088]

[0089] Wavelet transform can separate signal and noise, and then estimate the standard deviation of noise in noisy signal. w (k), can be approximated by an M-order polynomial:

[0090] m w (k) = a0 + a1k + ... + a M k M +v k (twenty three)

[0091] where v k is the observation noise.

[0092] Given a wavelet function φ(k) with scale s and time translation τ, it can be explicitly defined as:

[0093] Therefore, for the noisy observation signal m w The wavelet transform of (k) can be written as:

[0094]

[0095] Where x is the convolution operation.

[0096] If the vanishing moment of the wavelet function is ∝, when ∝ is greater than or equal to the order of M(9), the standard deviation σ of the noise can be estimated by the median of the absolute value of the most detailed part of the wavelet coefficients, that is:

[0097]

[0098] Where: scale s is 0.5, t h is the discrete representation of τ at the most detailed scale, 0≤h≤K / 2, W mw (s,t h ) is {m w (k)|k=0,1,…,K}, K / 2 wavelet coefficients, Med represents the sequence {W mw (s,t h )}.

[0099] In practical applications, the steps of wavelet transform noise estimation are as follows:

[0100] Step 1: Select a signal of length L, which is equivalent to an observation interval, and add a sliding window of length L to the sequence of this observation interval.

[0101] Step 2: Perform wavelet transform on the observation sequence within the window, and then estimate the standard deviation of the noise according to formula (24).

[0102] Combined with the Gaussian scale mixture model filter, the size of the unknown observation noise in the battery model can be estimated, and the state quantity can be estimated by the variational Bayes method. Compared with the traditional Kalman filter algorithm, this algorithm has higher accuracy and is more suitable for actual situations. That is, the unknown observation noise distribution is described by using wavelet transform and Gaussian scale mixture distribution.

[0103] So far, the state transfer equation (16) and measurement equation (17) of the Thevenin equivalent second-order RC model of the battery are obtained, wherein the state transfer equation and the measurement equation are related to the first polarization capacitor C1, the first polarization resistor R1, the second polarization capacitor C2, the second polarization resistor R2, the ohmic internal resistance R0 of the battery, and the observation noise V k , and state noise W k related, and the state noise W k is a constant, and the observation noise V k Wavelet transform is used to describe the unknown observation noise distribution using Gaussian scale mixture distribution.

[0104] The applicant found that the state of charge SOC of the battery is related to the first polarization capacitor C1, the first polarization resistor R1, the second polarization capacitor C2, the second polarization resistor R2, and the ohmic internal resistance R0. And it can be measured and obtained at different SOCs Figure 2The Thevenin equivalent second-order RC model shown is a first polarization capacitor C1, a first polarization resistor R1, a second polarization capacitor C2, a second polarization resistor R2, and an ohmic internal resistance R0 of the battery. At the same time, the polarization voltage U1, the polarization voltage U2, the battery current I, and the battery terminal voltage U can also be measured.

[0105] Then, under different SOCs, the first polarization capacitor C1, the first polarization resistor R1, the second polarization capacitor C2, the second polarization resistor R2, and the ohmic internal resistance R0 of the Thevenin equivalent second-order RC model are obtained to form a SOC-{R0, R1, R2, C1, C2} table. Specifically, the SOC of the battery can be estimated by the ampere-hour integration method. Of course, the SOC of the battery can also be estimated by other methods, which is not limited in this application. That is, step S3 includes: estimating the SOC of the battery by the ampere-hour integration method, and measuring the first polarization capacitor C1, the first polarization resistor R1, the second polarization capacitor C2, the second polarization resistor R2, and the ohmic internal resistance R0 at each SOC to form the SOC-{R0, R1, R2, C1, C2} table.

[0106] Then, the first polarization capacitor C1, the first polarization resistor R1, the second polarization capacitor C2, the second polarization resistor R2, and the ohmic internal resistance R0 of the battery under each SOC are obtained by looking up the table, and the first polarization capacitor C1, the first polarization resistor R1, the second polarization capacitor C2, the second polarization resistor R2, and the ohmic internal resistance R0 of the battery obtained by looking up the table are substituted into the state transfer equation and the measurement equation to calculate the open circuit voltage OCV of the battery corresponding to each SOC, thereby obtaining the SOC-OCV curve of the battery.

[0107] As described above, by establishing the Thevenin equivalent second-order RC model of the battery based on polynomial prediction, the battery's SOC can be estimated by the ampere-hour integration method within the time interval within which the cumulative error of the ampere-hour integration is allowed while the OCV state of the battery is estimated. The SOC-{R0, R1, R2, C1, C2} table is checked based on the real-time SOC, thus constructing a method for estimating the SOC-OCV curve of the lithium battery. The model can be applied to any working condition. When the time interval of the charge and discharge time is less than the cumulative error range of the estimated SOC value, the SOC-OCV curve can be obtained in real time and online using the above model and method.

[0108] In actual implementation, the SOC of the battery is also related to various operating conditions such as temperature. Based on the above method, the SOC-{R0, R1, R2, C1, C2} table under various operating conditions can be obtained, and then the SOC-OCV curve under various operating conditions can be obtained.

[0109] In practical applications, the above-mentioned battery SOC-OCV curve online estimation method can be applied to any battery. Preferably, the above-mentioned battery is a lithium battery.

[0110] Although the embodiments of the present disclosure and its advantages have been described in detail, it should be understood that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the disclosure as defined by the appended claims.

[0111] In addition, the scope of the present application is not intended to be limited to the specific embodiments of the processes, machines, manufactures, material compositions, devices, methods, and steps described in the specification. As one of ordinary skill in the art will readily appreciate from the disclosure of this disclosure, processes, machines, manufactures, material compositions, means, methods, or steps that perform substantially the same functions currently exist or will later be developed or achieve substantially the same results as the corresponding embodiments described herein that can be used according to this disclosure. Therefore, the appended claims are intended to include such processes, machines, manufactures, material compositions, devices, methods, or steps within their scope.

Claims

1. A method for online estimation of a battery SOC-OCV curve, characterized in that: include: S1: Establish the Thevenin equivalent second-order RC model of the battery; S2: a state transfer equation and a measurement equation characterizing the Thevenin equivalent second-order RC model, wherein the state transfer equation and the measurement equation are related to a first polarization capacitor C1, a first polarization resistor R1, a second polarization capacitor C2, a second polarization resistor R2, and an ohmic internal resistance R0 of the Thevenin equivalent second-order RC model; S3: Obtain the first polarization capacitor C1, the first polarization resistor R1, the second polarization capacitor C2, the second polarization resistor R2, and the ohmic internal resistance R0 of the Thevenin equivalent second-order RC model of the battery at different SOCs to form a SOC-{R0, R1, R2, C1, C2} table; S4: According to the SOC-{R0, R1, R2, C1, C2} table, the first polarization capacitor C1, the first polarization resistor R1, the second polarization capacitor C2, the second polarization resistor R2, and the ohmic internal resistance R0 are obtained by looking up the table, and the first polarization capacitor C1, the first polarization resistor R1, the second polarization capacitor C2, the second polarization resistor R2, and the ohmic internal resistance R0 obtained by looking up the table are substituted into the state transfer equation and the measurement equation to calculate the open circuit voltage U of the battery corresponding to each SOC oc , thereby obtaining the SOC-OCV curve of the battery.

2. The method for online estimation of the SOC-OCV curve of a battery according to claim 1, characterized in that: Step S2 includes: S21: Obtain the open circuit voltage state transition equation of the battery based on Kirchhoff’s circuit law; S22: Approximate the ohmic internal resistance R0 of the battery and obtain an expression of the ohmic internal resistance R0; S23: Based on Kirchhoff's circuit law, the open circuit voltage state transfer equation, and the ohmic internal resistance R0 expression, the state transfer equation and the measurement equation characterizing the Thevenin equivalent second-order RC model are obtained, wherein the state transfer equation and the measurement equation are also related to the observation noise.

3. The method for online estimation of the SOC-OCV curve of a battery according to claim 2, characterized in that: The open circuit voltage state transfer equation is oc,k-1 and U oc,k-2 Related, where U oc,k-1 is the open circuit voltage of the battery at the k-1th moment, U oc,k-2 is the open circuit voltage of the battery at the k-2th moment.

4. The method for online estimation of the SOC-OCV curve of a battery according to claim 3, characterized in that: The open circuit voltage state transfer equation is: Among them U oc,k is the open circuit voltage of the battery at the kth moment.

5. The method for online estimation of the SOC-OCV curve of a battery according to claim 4, characterized in that: The ohmic internal resistance R0 is expressed as: R 0,k =R 0,k-1 +r k-1 (15) where r k-1 is the error, R 0,k-1 is the ohmic internal resistance at the k-1th moment.

6. The method for online estimation of the SOC-OCV curve of a battery according to claim 5, characterized in that: The state transfer equation is: The measurement equation is: y k =U k =C k x k +v k (17) in: C k =[1 0 1 1 I k ] (20) Among them U 1,k is the polarization voltage across the first polarization capacitor C1 and the first polarization resistor R1 connected in parallel in the Thevenin equivalent second-order RC model at the kth moment, U 2,k is the polarization voltage across the second polarization capacitor C2 and the second polarization resistor R2 connected in parallel in the Thevenin equivalent second-order RC model at the kth moment, U 1,k-1 is the polarization voltage across the first polarization capacitor C1 and the first polarization resistor R1 connected in parallel in the Thevenin equivalent second-order RC model at the k-1th moment, U 2,k-2 is the polarization voltage across the second polarization capacitor C2 and the second polarization resistor R2 connected in parallel in the Thevenin equivalent second-order RC model at the k-2th moment, R 0,k is the ohmic internal resistance at the kth moment, I k-1 is the battery current at the k-1th moment, U k is the battery terminal voltage of the Thevenin equivalent second-order RC model at the kth moment, W k is the state noise, V k is the observation noise, and Δt is the algorithm calculation time, which can be set to 1s.

7. The method for online estimation of the SOC-OCV curve of a battery according to claim 6, characterized in that: The state noise W k Is a constant.

8. The method for online estimation of the SOC-OCV curve of a battery according to claim 2 or 6, characterized in that: Step S23 also includes: using wavelet transform and Gaussian scale mixture distribution to describe the unknown observation noise distribution.

9. The method for online estimation of the SOC-OCV curve of a battery according to claim 8, characterized in that: Step S3 includes: estimating the SOC of the battery by the ampere-hour integration method, and measuring the first polarization capacitor C1, the first polarization resistor R1, the second polarization capacitor C2, the second polarization resistor R2, and the ohmic internal resistance R0 at each SOC to form the SOC- {R0,R1,R2,C1,C2} table.

10. The method for online estimation of the SOC-OCV curve of a battery according to claim 1, characterized in that: The battery is a lithium battery.

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