首页|Remaining useful life prediction of lithium-ion batteries using a fusion method based on Wasserstein GAN

Remaining useful life prediction of lithium-ion batteries using a fusion method based on Wasserstein GAN

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Lithium-ion batteries are the main power supply equipment in many fields due to their advantages of no memory,high energy density,long cycle life and no pollution to the environment.Accurate prediction for the remaining useful life (RUL) of lithium-ion batteries can avoid serious economic and safety problems such as spontaneous combustion.At present,most of the RUL prediction studies ignore the lithium-ion battery capacity recovery phenomenon caused by the rest time between the charge and discharge cycles.In this paper,a fusion method based on Wasserstein generative adversarial network (GAN) is proposed.This method achieves a more reliable and accurate RUL prediction of lithium-ion batteries by combining the artificial neural network (ANN) model which takes the rest time between battery charging cycles into account and the empirical degradation models which provide the correct degradation trend.The weight of each model is calculated by the discriminator in the Wasserstein GAN model.Four data sets of lithium-ion battery provided by the National Aeronautics and Space Administration (NASA) Ames Research Center are used to prove the feasibility and accuracy of the proposed method.

remaining useful lifelithium-ion batterybattery capacity recoveryfusion methodWasserstein generative adversarial network (GAN)

Zhou Wending、Bao Shijian、Xu Fangmin、Zhao Chenglin

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School of Telecommunication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China

China International Engineering Consulting Corporation, Beijing 100048, China

This work was supported by the Project of the New Touch-Integrated Display ModuleNew Intelligent Manufacturing Mode Application,Ministry of Industry and Information Technology,2017

2020

中国邮电高校学报(英文版)
北京邮电大学

中国邮电高校学报(英文版)

CSCDEI
影响因子：0.419
ISSN：1005-8885
年,卷(期)：2020.27(1)
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