Lithium battery state-of-charge prediction method based on improved generative adversarial network

A state of charge and prediction method technology, applied in biological neural network models, neural learning methods, electrical measurement, etc., can solve the problems of few training samples, insufficient model depth, insufficient nonlinear expressiveness, etc., and achieve the improvement effect Effect

Active Publication Date: 2020-04-14
ZHEJIANG UNIV
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Problems solved by technology

The bp neural network method has a good self-learning ability, but there are few training samples and the depth of the

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  • Lithium battery state-of-charge prediction method based on improved generative adversarial network
  • Lithium battery state-of-charge prediction method based on improved generative adversarial network
  • Lithium battery state-of-charge prediction method based on improved generative adversarial network

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Embodiment 1

[0056] Embodiment 1, based on the lithium battery state of charge prediction method of improved generation confrontation network, such as figure 2 shown; the steps include the following:

[0057] S1), simulate different charging and discharging environments on lithium batteries, and use BMS equipment (battery management system) to collect single-cell batteries in the battery pack to obtain modal parameters under different temperature, voltage, current, and battery internal resistance environments.

[0058] Specifically, the sample battery is a Panasonic 18650 lithium battery, 18 refers to a battery diameter of 18.0 mm, and 650 refers to a battery height of 65.0 mm. The lithium-ion battery voltage is a nominal voltage of 3.7v, and the charging cut-off voltage is 4.2v.

[0059] BMS has acquired the data of three different working conditions of the sample battery, namely NEDC, EPA, and WLTP, and simulated the usage status of the battery, traffic conditions and climate, driving ...

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Abstract

The invention provides a lithium battery state-of-charge prediction method based on an improved generative adversarial network. The lithium battery state-of-charge prediction method comprises the following steps: collecting modal parameters of a lithium battery and a real state-of-charge SOC in a lithium battery sample; estimating a lower bound value of mutual information between the generation model G output G (z, c) and the condition variable c by using a regression model R; enabling the generation model G and a discrimination model D to confront each other to achieve Nash equilibrium; generating a sample by utilizing the generation model G, and adding the sample into a training set used by the regression model R for training; and alternately training the generation model G, the discrimination model D and the regression model R to enable each model to tend to converge. According to the invention, the training set conforming to original distribution is expanded by using the generationmodel; at the same time, in the improved generative adversarial network, two activation functions of a random correction linear unit RReLU and an exponential linear unit Exponential Units (ELU) are used to obtain stronger model expressive force, and the nonlinear characteristics of the lithium battery are better learned.

Description

technical field [0001] The invention relates to the technical field of lithium batteries, in particular to a method for predicting the state of charge of a lithium battery. Background technique [0002] As an important part of electric vehicles, lithium batteries play a key role in the acceleration, climbing and battery life of the entire vehicle. The state of charge (SOC) of the battery is an important parameter to reflect the energy of the lithium battery. Accurately estimating the SOC can prolong the service life of the battery, avoid overcharging / discharging of the battery, and ensure the safe driving of electric vehicles. premise. SOC is an internal characteristic parameter of the battery that cannot be directly measured, and there is a strong nonlinear relationship between SOC and battery voltage, current, temperature and other parameters. Therefore, how to improve the precision and accuracy of SOC estimation is an urgent problem to be solved in the field of electric...

Claims

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Application Information

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IPC IPC(8): G01R31/367G01R31/388G06N3/04G06N3/08
CPCG01R31/367G01R31/388G06N3/08G06N3/044G06N3/045
Inventor 金心宇马文山林虎孙斌
Owner ZHEJIANG UNIV
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