CN109901083B - Online reconstruction method for OCV curve of power battery - Google Patents

Abstract

The invention provides an OCV curve online reconstruction method for a power battery, which is used for reconstructing an OCV curve online based on online parameter identification and SOC estimation results. Since there is inconsistency in the OCV curve between each battery cell and the OCV curve varies with the change in temperature and the aging of the battery, the OCV curve of each battery cell is unknown. The traditional open circuit voltage test experimental method can only obtain the OCV curve of a specific monomer in a specific environment state, and the method can carry out online reconstruction on the OCV curve according to online parameter identification and SOC estimation results, thereby not only saving a large amount of experimental time, but also locally correcting the OCV curve online and timely.

Description

Online reconstruction method for OCV curve of power battery

Technical Field

The invention relates to the technical field of power battery system management, in particular to a technology for reconstructing an open-circuit voltage curve of a power battery.

Background

At present, a model-based power battery state estimation algorithm generally adopts a functional relationship between Open Circuit Voltage (OCV) and state of charge (SOC) to correct an SOC estimation value obtained by an ampere-hour integration method, so that SOC estimation accuracy greatly depends on accuracy of an OCV-SOC curve. The OCV-SOC curve is generally obtained from an OCV test experiment, which is long-lasting, and only an OCV curve of a specific monomer in a specific environmental state can be obtained. The OCV curves of the battery cells are inconsistent, and change with the change of temperature and the aging of the battery, so that the OCV curves of the battery cells in different environments and aging states are unknown. Therefore, there is a need in the art for a method for obtaining an OCV-SOC function online, reconstructing an OCV curve, and performing online correction and update on the OCV curve.

Disclosure of Invention

Aiming at the technical problems in the prior art, the invention provides an online reconstruction method of an OCV curve of a power battery, which specifically comprises the following steps:

step 1, rewriting an OCV-SOC function relational expression in a polynomial form;

step 2, acquiring and storing terminal voltage and current data in the running process of the power battery in real time on line;

step 3, performing online parameter identification to obtain an OCV identification result;

step 4, SOC estimation is carried out to obtain an SOC estimation result;

step 5, according to the terminal voltage prediction error, screening and counting the data points which meet the condition by taking the terminal voltage error smaller than a set value as the condition;

step 6, judging whether the screened accumulated number of the data points reaches the condition for triggering the OCV reconstruction program, and reconstructing the OCV curve once when the accumulated number reaches the triggering condition;

and 7, performing parameter identification and SOC estimation at the next moment.

Further, the step 1 of rewriting the OCV-SOC function relational expression in the polynomial form specifically includes: for an nth-order polynomial function, n +1 coefficients are shared, and the expression can be determined by n +1 non-coincident points in a plane, that is, the nth-order polynomial function can be rewritten as:

in the formula of U_OCThe open circuit voltage OCV is shown, z is the state of charge SOC of the battery, and the coordinates of n +1 points through which the OCV curve passes are (x)₀,y₀),(x₁,y₁),…,(x_n,y_n) Defining these points as control points; z is a radical of_nThe vector of SOC from 0 power to n power, y is the vector of control point ordinate, and X is the matrix of control point abscissa from 0 power to n power; the abscissa of the control point is real number which is not equal to each other, and can be selected according to actual needs. Due to the physical meaning of SOC, the abscissa of the control point is typically between 0 and 100%; after the abscissa of the control point is selected, the ordinate of the control point can be used as the coefficient of the OCV-SOC functional relation expression, and the initial ordinate vector of the control point is obtained, so that the initial OCV curve can be obtained.

Further, the step 6 specifically includes:

step 6.1, reading the SOC value of each data point obtained by screening in the step 5, and determining the SOC interval in which the data point is respectively positioned;

step 6.2, activating corresponding control points according to the SOC interval, wherein the vertical coordinate of the activated control points is selected as a variable to be updated, and the coordinate of the control points which are not activated is kept unchanged;

and 6.3, fitting the OCV and SOC data of each screened data point, updating the coordinates of the activated control points, and obtaining an online reconstructed OCV curve. Based on the function relationship between OCV and SOC, the method can be used for subsequent SOC estimation.

The invention has the following beneficial effects: according to the method, an OCV-SOC functional relation is rewritten into a form that coordinates of control points are used as coefficients, online parameter identification and SOC estimation results are screened according to terminal voltage prediction errors, and points with large errors are removed to prevent interference on an OCV curve fitting result; updating the corresponding local control point according to the SOC interval where the data point is located so as to realize the online reconstruction of the OCV-SOC; the online reconstructed OCV curve can be further applied to SOC estimation of the battery.

Drawings

FIG. 1 is a schematic flow chart of the method of the present invention

FIG. 2 is a reference OCV curve obtained from OCV test data of the battery cells #1, #2, #3, and #4

FIG. 3 is an OCV reconstruction of monomer #5 at 25 deg.C for UDDS operating conditions

FIG. 4 shows SOC estimation results and errors thereof of monomer #5 under UDDS working condition and OCV online reconstruction process at 10 DEG C

FIG. 5 shows SOC estimation results and errors thereof of monomer #5 under 25 ℃ UDDS working condition and OCV online reconstruction process

FIG. 6 shows SOC estimation results and errors thereof of monomer #5 under the UDDS working condition at 40 ℃ and OCV online reconstruction process

Detailed Description

The method for reconstructing the OCV curve of the power battery on line provided by the invention is described in detail below with reference to the accompanying drawings.

The method for reconstructing the OCV curve of the power battery on line, disclosed by the invention, as shown in FIG. 1, specifically comprises the following steps:

step 1, rewriting an OCV-SOC function relational expression in a polynomial form;

step 2, acquiring and storing terminal voltage and current data in the running process of the power battery in real time on line;

step 3, performing online parameter identification to obtain an OCV identification result;

step 4, SOC estimation is carried out to obtain an SOC estimation result;

step 5, according to the terminal voltage prediction error, screening and counting the data points which meet the condition by taking the terminal voltage error smaller than a set value as the condition;

step 6, judging whether the screened accumulated number of the data points reaches the condition for triggering the OCV reconstruction program, and reconstructing the OCV curve once when the accumulated number reaches the triggering condition;

and 7, performing parameter identification and SOC estimation at the next moment.

In one embodiment of the invention, a nickel-cobalt-manganese ternary lithium battery is selected as a research object, the rated capacity of the nickel-cobalt-manganese ternary lithium battery is 2Ah, and the charging and discharging cut-off voltages are 4.1V and 3.0V respectively. The experimental condition is an urban road circulation condition (UDDS). OCV test experiments at 10 ℃,25 ℃, and 40 ℃ were performed on the battery cells #1, #2, #3, and #4, and the mean value of the OCV data obtained from the experiments was used as a reference OCV curve, as shown in fig. 2. Since no OCV test experiment was performed on monomer #5, the OCV curve for monomer #5 was unknown. The OCV curve online reconstruction method provided by the invention is used for reconstructing the OCV curve of #5 under the UDDS working condition and carrying out SOC estimation so as to illustrate the effectiveness of the OCV curve online reconstruction method.

FIG. 3 shows the OCV curve reconstruction for monomer #5 at 25 ℃ for UDDS operating conditions. In this example, the OCV curve reconstruction was performed every 1000 valid data points that meet the criteria, and a total of 17 reconstructions were performed. As can be seen from fig. 3, in the UDDS operating condition, as the battery discharges, the process of reconstructing the OCV curve starts from the high SOC interval, and gradually proceeds to the middle and low SOC intervals, and finally, as the discharging process is completed, the OCV curve of the full SOC interval is reconstructed. The method provided by the invention can realize online reconstruction of the OCV curve.

FIG. 4, FIG. 5, and FIG. 6 show SOC estimation results of monomer #5 with online OCV reconstruction under UDDS conditions at 10 deg.C, 25 deg.C, and 40 deg.C, respectively. As can be seen from fig. 4, 5, and 6, when the OCV curve of the battery cell #5 is unknown, SOC estimation is performed using the OCV curve obtained by online reconstruction using the method provided by the present invention, and the maximum error of SOC estimation is less than 2.5% at 10 ℃ to 40 ℃. The method provided by the invention has better OCV curve reconstruction effect under different temperatures and can realize good SOC estimation precision.

The foregoing detailed description of the invention has shown and described the basic principles and features of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are merely illustrative of the principles of the present invention, but various changes and modifications may be made without departing from the spirit and principles of the invention, and these changes and modifications are within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.

Claims (2)

1. An online reconstruction method for an OCV curve of a power battery is characterized by comprising the following steps: the method specifically comprises the following steps:

step 1, rewriting an OCV-SOC function relational expression in a polynomial form, comprising: for an nth-order polynomial function, n +1 coefficients are shared, and the expression can be determined by n +1 non-coincident points in a plane, that is, the nth-order polynomial function can be rewritten as:

in the formula of U_OCThe open circuit voltage OCV is shown, z is the state of charge SOC of the battery, and the coordinates of n +1 points through which the OCV curve passes are (x)₀,y₀),(x₁,y₁),…,(x_n,y_n) Defining these points as control points; z is a radical of_nThe vector of SOC from 0 power to n power, y is the vector of control point ordinate, and X is the matrix of control point abscissa from 0 power to n power; the abscissa of the control point is real numbers which are not equal to each other;

step 2, acquiring and storing terminal voltage and current data in the running process of the power battery in real time on line;

step 3, performing online parameter identification to obtain an OCV identification result;

step 4, SOC estimation is carried out to obtain an SOC estimation result;

step 5, according to the terminal voltage prediction error, screening and counting the data points which meet the condition by taking the terminal voltage prediction error smaller than a set value as the condition;

step 6, judging whether the screened accumulated number of the data points reaches the condition for triggering the OCV reconstruction program, and reconstructing the OCV curve once when the accumulated number reaches the triggering condition;

and 7, performing parameter identification and SOC estimation at the next moment.

2. The method of claim 1, wherein: the step 6 specifically includes:

step 6.1, reading the SOC value of each data point obtained by screening in the step 5, and determining the SOC interval in which the data point is respectively positioned;

step 6.2, activating corresponding control points according to the SOC interval, wherein the vertical coordinate of the activated control points is selected as a variable to be updated, and the coordinate of the control points which are not activated is kept unchanged;

and 6.3, fitting the OCV and SOC data of each screened data point, updating the coordinates of the activated control points to obtain an online reconstructed OCV curve, and based on the function relationship of OCV and SOC, using the online reconstructed OCV curve to perform subsequent SOC estimation.

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