首页|Prognosability regularized generative adversarial network for battery state of health estimation with limited samples
Prognosability regularized generative adversarial network for battery state of health estimation with limited samples
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
Elsevier
State of health (SOH) estimation is essential to improve reliability of battery. However, it is a challenging issue to develop an effective health prognostics model due to the lack of run-to-failure data in the industrial scenarios. In this paper, a prognosability regularized generative adversarial network (ProGAN) is proposed to implement SOH estimation of battery. Firstly, a prognosability regulator is proposed in ProGAN to generate the samples with good trendability over time. Secondly, a multi-level wavelet decomposition long short-term memory (MwLSTM) is proposed to generate samples from low and high frequency components. Finally, an SOH estimator is constructed based on the synthetic samples. Compared to existing generative models, ProGAN introduces a novel prognosability regulator that enhances the trendability of generated samples. Additionally, a MwLSTM is developed by considering both low and high frequency components. Three battery SOH estimation cases are adopted to verify the effectiveness of ProGAN. The experimental results indicate that ProGAN can not only generate the samples with high consistency in space (i.e., data distribution), but also have good trendability over time (i.e., prognosability). Moreover, ProGAN has a better performance in both data augmentation and SOH estimation than other generative models.
BatteryData augmentationGenerative adversarial networkPrognosability regularizationState of health estimation
Zhuang Ye、Jiantao Chang、Jianbo Yu
展开 >
School of Mechanical Engineering,Tongji University,Shanghai,201804,PR China
Xidian University,State Key Laboratory of Electromechanical Integrated Manufacturing of High-performance Electronic Equipments,Xian,710071,PR China
NETL
NSTL
Elsevier
