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Lithium battery state-of-charge estimation method based on BCRLS-UKF

A state of charge, lithium battery technology, applied in the direction of measuring electricity, measuring electrical variables, measuring devices, etc., can solve the problems of filter divergence, waste of storage space, and can not reflect the dynamic characteristics of the battery well, so as to improve the identification accuracy, Effect of Improving SOC Estimation Accuracy

Pending Publication Date: 2021-12-17
SHANGHAI DIANJI UNIV
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  • Application Information

AI Technical Summary

Problems solved by technology

Doing so wastes a lot of storage space and cannot reflect the dynamic characteristics of the battery well, which will lead to a decrease in the accuracy of the traditional unscented Kalman filter, and even filter divergence.

Method used

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  • Lithium battery state-of-charge estimation method based on BCRLS-UKF
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  • Lithium battery state-of-charge estimation method based on BCRLS-UKF

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Embodiment Construction

[0018] The present invention will be described in further detail below in conjunction with the accompanying drawings.

[0019] According to one or more embodiments, a method for estimating the state of charge of a lithium battery based on BCRLS-UKF is disclosed, including the following steps:

[0020] Establish a battery model and obtain the state equation of the battery model;

[0021] Carry out OCV-SOC test and conduct online parameter identification of the battery;

[0022] The SOC value of the lithium battery is estimated by the UKF algorithm.

[0023] To establish a battery model, a second-order RC equivalent circuit model is used. The establishment of a lithium battery model is the premise of accurately estimating the SOC. It is necessary to consider both the accuracy of the model and the amount of calculation. Currently commonly used equivalent models are: internal resistance model, Thevenin model, RC model and PNGV model. The second-order RC model is more accurate ...

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Abstract

The invention discloses a lithium battery state-of-charge estimation method based on BCRLS-UKF, and solves the problems that the current parameters of a battery cannot be updated in real time in traditional offline identification, and the SOC estimation precision is easy to have a large error along with the increase of working time. The technical scheme is characterized by comprising the following steps: establishing a lithium battery model, and obtaining a state equation of the battery model; discretizing a state equation of the battery model, updating parameters of the battery model in real time through a state estimation value at a previous moment and information acquired at a current moment, performing online parameter identification by adopting a recursive least square method with deviation compensation, and calculating to obtain a parameter estimation value after deviation compensation; and estimating the SOC value of the lithium battery through a UKF algorithm. According to the lithium battery state of charge estimation method based on the BCRLS-UKF, the interference of a uncertain noise on model parameter identification can be effectively solved, the identification progress is improved, and the SOC estimation precision is improved too.

Description

technical field [0001] The invention relates to battery management technology, in particular to a method for estimating the state of charge of a lithium battery based on BCRLS-UKF. Background technique [0002] With the advancement of science and technology, the rapid development of the automobile industry has brought about the demand for non-renewable resources. People are constantly trying to use clean energy as much as possible. Hydropower, wind power, geothermal power, solar energy and other clean energy have emerged one after another. Attracting people's attention, these new energy sources are both environmentally friendly and will not be exhausted. Therefore, lithium-ion batteries are the best choice for new energy electric vehicle battery energy. Electric vehicles have become the mainstream products of new energy vehicles, and lithium-ion batteries have the advantages of high energy density, high voltage, no pollution, and small size, and have become the battery of c...

Claims

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Application Information

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IPC IPC(8): G01R31/3842
CPCG01R31/3842
Inventor 郑倩杨俊杰罗钦杨史可欣
Owner SHANGHAI DIANJI UNIV