中国邮电高校学报(英文版) ›› 2020, Vol. 27 ›› Issue (1): 1-9.doi: 10.19682/j.cnki.1005-8885.2020.0004

• Computer Applied Technology •    下一篇

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

周温丁1,鲍士兼2,许方敏1,赵成林3   

  1. 1. 北京邮电大学
    2. 中国国际工程咨询有限公司
    3. 北京邮电大学信息与通信工程学院
  • 收稿日期:2019-05-16 修回日期:2019-12-08 出版日期:2020-02-28 发布日期:2020-02-28
  • 通讯作者: 许方敏 E-mail:xufm@bupt.edu.cn

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

  • Received:2019-05-16 Revised:2019-12-08 Online:2020-02-28 Published:2020-02-28
  • Contact: Fangmin XU E-mail:xufm@bupt.edu.cn

摘要: 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.

关键词: wasserstein GAN

Abstract: 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 NASA Ames Research Center are used to prove the feasibility and accuracy of the proposed method.

Key words: wasserstein generative adversarial network (GAN)

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