Aihua Tanga , Yihan Jiangb, Yuwei Nib, Quanqing Yub, Weixiang Shenc, and Michael G. Pechtd
a School of Vehicle Engineering, Chongqing University of Technology, Chongqing, 400054, China
b School of Automotive Engineering, Harbin Institute of Technology, Weihai, Shandong, 264209, China
c School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Hawthorn, Victoria, 3122, Australia
d Center for Advanced Life Cycle Engineering (CALCE), University of Maryland, College Park, MD, USA
For more information about this article and related research, please contact Michael G. Pecht.
Abstract:
With the continuous concern on the safety of battery systems, accurate and rapid assessment of battery degradation is essential for practical applications. In this paper, a transferable attention network model based on deep learning is developed to evaluate battery degradation, which can simultaneously predict state of health (SOH) and remaining useful life (RUL) for lithium-ion batteries. First, degradation patterns of the cells are identified by K-means clustering based on the difference of the cells at their early cycles. Secondly, the attention mechanisms are designed to suppress noises in extracted feature maps and trace the interaction between long- and short-term degradation modes. Thirdly, the common knowledge represented by the reference cells and the unique degradation features of the target cell are fused by transfer learning, then SOH and RUL prediction are realized through multi-task learning. Finally, a large-scale battery dataset containing different cycle conditions is used to verify the accuracy and generalization of the developed method. The results show that the developed method achieves accurate SOH and RUL prediction with the average root mean square error within 0.14% and six cycles, respectively.
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