ArXiv Preprint
State-of-health (SOH) estimation is a key step in ensuring the safe and
reliable operation of batteries. Due to issues such as varying data
distribution and sequence length in different cycles, most existing methods
require health feature extraction technique, which can be time-consuming and
labor-intensive. GRU can well solve this problem due to the simple structure
and superior performance, receiving widespread attentions. However, redundant
information still exists within the network and impacts the accuracy of SOH
estimation. To address this issue, a new GRU network based on Hilbert-Schmidt
Independence Criterion (GRU-HSIC) is proposed. First, a zero masking network is
used to transform all battery data measured with varying lengths every cycle
into sequences of the same length, while still retaining information about the
original data size in each cycle. Second, the Hilbert-Schmidt Independence
Criterion (HSIC) bottleneck, which evolved from Information Bottleneck (IB)
theory, is extended to GRU to compress the information from hidden layers. To
evaluate the proposed method, we conducted experiments on datasets from the
Center for Advanced Life Cycle Engineering (CALCE) of the University of
Maryland and NASA Ames Prognostics Center of Excellence. Experimental results
demonstrate that our model achieves higher accuracy than other recurrent
models.
Ziyue Huang, Lujuan Dang, Yuqing Xie, Wentao Ma, Badong Chen
2023-03-16
