Battery capacity estimation model and method based on double-tower deep learning network

A technology of deep learning network and battery capacity, which is applied in the field of battery capacity prediction model based on twin-tower deep learning network, which can solve the problems of simple feature construction and model construction

Active Publication Date: 2021-11-30
杭州宇谷科技股份有限公司
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Problems solved by technology

[0010] The present invention provides a battery capacity estimation model based on a double-tower deep learning network, ...

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  • Battery capacity estimation model and method based on double-tower deep learning network
  • Battery capacity estimation model and method based on double-tower deep learning network
  • Battery capacity estimation model and method based on double-tower deep learning network

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Embodiment 1

[0057] combine figure 1 As shown, this embodiment provides a battery capacity estimation model based on a double-tower deep learning network, which has:

[0058] The first input layer is used to input the constant current charging feature sequence;

[0059] A first fully connected network, which is used to process the constant current charging characteristic sequence and generate a first output;

[0060] The second input layer is used to input a random discharge feature sequence;

[0061] A second fully connected network for processing the random discharge signature sequence and generating a second output; and

[0062] The output layer is used to generate an estimated battery capacity after combining the first output and the second output.

[0063] In order to improve the accuracy of battery capacity prediction, a double-tower deep learning network is constructed based on the deep learning network in this embodiment, which actually belongs to the battery capacity estimation...

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Abstract

The invention relates to the technical field of battery capacity prediction, and in particular, relates to a battery capacity estimation model and method based on a double-tower deep learning network. The battery capacity estimation model comprises a first input layer used for inputting a constant current charging characteristic sequence, a first fully connected network for processing the constant current charging characteristic sequence and generating a first output, a second input layer used for inputting a random discharge characteristic sequence, a second fully connected network for processing the random discharge characteristic sequence and generating a second output, and an output layer used for combining the first output and the second output to generate an estimated battery capacity. The method is realized based on the model. The method has higher battery capacity estimation precision.

Description

technical field [0001] The invention relates to the technical field of battery capacity prediction, in particular to a battery capacity prediction model and method based on a double-tower deep learning network. Background technique [0002] Lithium batteries have the advantages of high energy storage density, long service life, low self-discharge rate, light weight, and environmental protection. They have been widely used in various daily life scenarios such as mobile phones, laptop computers, power tools, and new energy vehicles. As the battery usage time increases, the battery performance continues to degrade, resulting in a decline in battery capacity and power. [0003] The mathematical definition of battery capacity (SOH) is as follows: in, Indicates the full charge of the battery in the current state, Indicates the nominal charge of the battery when the battery leaves the factory. The battery capacity (SOH) reflects the health status of the battery during use. G...

Claims

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Application Information

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IPC IPC(8): G01R31/367
CPCG01R31/367
Inventor 肖劼胡雄毅余为才
Owner 杭州宇谷科技股份有限公司
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