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A classification method for lithium-ion battery degradation based on bp neural network

A BP neural network, lithium-ion battery technology, applied in the direction of measuring electricity, measuring electrical variables, instruments, etc., can solve the problems of online battery full charge and discharge, unable to grasp the battery aging situation at any time, and achieve the effect of strong operability

Active Publication Date: 2022-04-12
广州思林杰科技股份有限公司
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Problems solved by technology

The aging condition of the battery can be obtained by detecting the effective capacity of the battery, but it is difficult to fully charge and discharge the online battery during use
The offline aging detection makes it impossible for users to know the aging situation of the battery at any time, so as to judge whether the battery should be maintained or replaced, so as to maintain the performance of the battery pack and ensure the safety of electric vehicles

Method used

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  • A classification method for lithium-ion battery degradation based on bp neural network
  • A classification method for lithium-ion battery degradation based on bp neural network
  • A classification method for lithium-ion battery degradation based on bp neural network

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Embodiment Construction

[0026] This aspect will be further described in combination with specific implementation manners.

[0027] A BP neural network-based lithium-ion battery degradation classification method provided by the present invention, the overall flow chart is as follows figure 1 shown.

[0028] First, cycle aging experiments were carried out on lithium-ion batteries to obtain the equivalent DC internal resistance spectrum and effective capacity.

[0029] When performing cycle aging experiments, temperature conditions and charge-discharge rate conditions must be controlled. The temperature conditions can be set to 0°C, 10°C, 20°C, 30°C, 40°C and 50°C; the charge and discharge rate can be set to 0.5C, 1C, 2C and 3C. The pairwise combination of temperature and charge-discharge rate constitutes 24 different experimental conditions.

[0030] During the cycle aging experiment, the battery capacity evaluation and charging internal resistance evaluation are carried out every certain number of ...

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Abstract

The present invention provides a lithium-ion battery degradation classification method based on BP neural network. By collecting the equivalent internal resistance of the lithium-ion battery online, the degradation level of the lithium-ion battery can be judged online, and the aging state of the lithium-ion battery can be monitored in real time; Through the calculation of the BP neural network model, the aging state can be monitored only through the external characteristics of the lithium-ion battery without knowing the internal characteristics of the lithium-ion battery, and the operability is strong.

Description

technical field [0001] The invention relates to a lithium ion battery degradation classification method based on BP neural network. Background technique [0002] Lithium-ion batteries are widely used in the field of electric vehicles due to their high energy density, long cycle life, and high safety. As the number of cycles of lithium-ion batteries increases during use, the external characteristics of various aspects will appear in an aging state, such as a decrease in effective capacity and an increase in the internal resistance of charge and discharge. The battery management system evaluates the battery aging degree by analyzing the state of the lithium-ion battery. The aging condition of the battery can be obtained by detecting the effective capacity of the battery, but it is difficult to fully charge and discharge the online battery during use. The offline aging detection makes it impossible for users to know the aging status of the battery at any time, so as to judge ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01R31/367G01R31/382G01R31/389
Inventor 谭晓军梁永贤范玉千
Owner 广州思林杰科技股份有限公司
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