Gate Recurrent Unit Network based on Hilbert-Schmidt Independence Criterion for State-of-Health Estimation
This addresses battery safety and reliability for applications like electric vehicles, but it is incremental as it builds on existing GRU methods with a specific bottleneck technique.
The paper tackles the problem of state-of-health (SOH) estimation for batteries by proposing a GRU network based on Hilbert-Schmidt Independence Criterion (GRU-HSIC) to compress redundant information, achieving higher accuracy than other recurrent models in experiments on CALCE and NASA datasets.
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.