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A method for online reconstruction of power battery ocv curve

A power battery and curve technology, applied in the direction of measuring electricity, measuring electrical variables, instruments, etc., can solve the problems of inconsistent OCV curves and long time consumption

Active Publication Date: 2020-05-22
BEIJING INSTITUTE OF TECHNOLOGYGY
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AI Technical Summary

Problems solved by technology

The OCV-SOC curve is usually obtained by the OCV test experiment. The OCV test experiment takes a long time and can only obtain the OCV curve of a specific monomer under a specific environmental state.
However, there are inconsistencies in the OCV curves between each battery cell, and as the temperature changes and the battery ages, the OCV curve will also change, so the OCV curve of each battery cell in different environments and aging states is Unknown

Method used

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  • A method for online reconstruction of power battery ocv curve
  • A method for online reconstruction of power battery ocv curve
  • A method for online reconstruction of power battery ocv curve

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Embodiment Construction

[0025] A power battery OCV curve online reconstruction method provided by the present invention will be described in detail below in conjunction with the accompanying drawings.

[0026] The power battery OCV curve online reconstruction method provided by the present invention, such as figure 1 As shown, it specifically includes the following steps:

[0027] Step 1, rewriting the OCV-SOC function relation expression in polynomial form;

[0028] Step 2. Obtain and store the terminal voltage and current data of the power battery during operation in real time online;

[0029] Step 3. Perform online parameter identification to obtain OCV identification results;

[0030] Step 4, perform SOC estimation, and obtain the SOC estimation result;

[0031] Step 5. According to the terminal voltage prediction error, the terminal voltage error is smaller than the set value as the condition, and the data points meeting the conditions are screened and counted;

[0032] Step 6, judging wheth...

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Abstract

The invention provides a power battery OCV curve online reconstruction method. According to the method, an OCV curve is reconstructed on the basis of online parameter identification and SOC estimationresults; and since the OCV curves of each battery cell are inconsistent, and the OCV curves also change with the change of temperature and the aging of a battery, the OCV curve of each battery cell is unknown. A traditional open-circuit voltage test experimental method can only obtain the OCV curve of a specific battery cell under a specific environmental state, however, the power battery OCV curve online reconstruction method of the present invention can perform online reconstruction of the OCV curve according to the online parameter identification and the SOC estimation results, can save alot of experimental time and can locally correct the OCV curve online and in time.

Description

technical field [0001] The invention relates to the technical field of power battery system management, in particular to the open-circuit voltage curve reconstruction technology of a power battery. Background technique [0002] At present, the model-based power battery state estimation algorithm usually uses the functional relationship between the open circuit voltage (OCV) and the state of charge (SOC) to correct the SOC estimated value obtained by the ampere-hour integration method, so the SOC estimation accuracy is very dependent on OCV-SOC The precision of the curve. The OCV-SOC curve is usually obtained by the OCV test experiment, which takes a long time and can only obtain the OCV curve of a specific monomer in a specific environmental state. However, there are inconsistencies in the OCV curves between each battery cell, and as the temperature changes and the battery ages, the OCV curve will also change, so the OCV curve of each battery cell in different environments ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01R31/396
Inventor 熊瑞雷浩
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
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