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.

中文翻译：

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基于希尔伯特-施密特独立准则的门循环单元网络健康状况估计

健康状态（SOH）估计是确保电池安全可靠运行的关键步骤。由于不同周期的数据分布和序列长度不同等问题，现有方法大多需要健康特征提取技术，耗时耗力。GRU由于其简单的结构和优越的性能很好地解决了这个问题，受到了广泛的关注。然而，冗余信息仍然存在于网络中并影响SOH估计的准确性。为了解决这个问题，提出了一种基于 Hilbert-Schmidt 独立准则 (GRU-HSIC) 的新 GRU 网络。首先，使用零掩蔽网络将每个周期以不同长度测量的所有电池数据转换为相同长度的序列，同时在每个循环中仍保留有关原始数据大小的信息。其次，从信息瓶颈 (IB) 理论演变而来的 Hilbert-Schmidt 独立准则 (HSIC) 瓶颈被扩展到 GRU 以压缩来自隐藏层的信息。为了评估所提出的方法，我们对马里兰大学高级生命周期工程中心 (CALCE) 和美国宇航局艾姆斯预测卓越中心的数据集进行了实验。实验结果表明，我们的模型比其他循环模型具有更高的准确性。我们对来自马里兰大学高级生命周期工程中心 (CALCE) 和美国宇航局艾姆斯预测卓越中心的数据集进行了实验。实验结果表明，我们的模型比其他循环模型具有更高的准确性。我们对来自马里兰大学高级生命周期工程中心 (CALCE) 和美国宇航局艾姆斯预测卓越中心的数据集进行了实验。实验结果表明，我们的模型比其他循环模型具有更高的准确性。