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.

翻译：健康状态（SOH）估计是确保电池安全可靠运行的关键步骤。由于不同循环中数据分布和序列长度存在差异，现有方法大多需要耗时耗力的健康特征提取技术。门控循环单元（GRU）因其结构简单、性能优越而备受关注，能够很好地解决这一问题。然而，网络中仍存在冗余信息，影响SOH估计的准确性。为解决该问题，本文提出一种基于希尔伯特-施密特独立性准则的新型GRU网络（GRU-HSIC）。首先，利用零掩码网络将所有每个循环中不同长度的电池测量数据转换为等长序列，同时保留各循环中原始数据量的信息。其次，将从信息瓶颈（IB）理论发展而来的希尔伯特-施密特独立性准则（HSIC）瓶颈扩展至GRU，以压缩隐藏层的信息。为评估所提方法，我们在马里兰大学先进生命周期工程中心（CALCE）和美国国家航空航天局艾姆斯卓越预测中心的数据集上进行了实验。实验结果表明，该模型比其他循环神经网络模型具有更高的精度。