Qian Cheng, Xu Binghui, Xia Quan, Ren Yi, Yang Dezhen, Wang Zili
School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China.
School of Aeronautic Science and Engineering, Beihang University, Beijing 100191, China.
Materials (Basel). 2022 Aug 27;15(17):5933. doi: 10.3390/ma15175933.
Online state-of-charge (SOC) estimation for lithium-ion batteries is one of the most important tasks of the battery management system in ensuring its operation safety and reliability. Due to the advantages of learning the long-term dependencies in between the sequential data, recurrent neural networks (RNNs) have been developed and have shown their superiority over SOC estimation. However, only time-series measurements (e.g., voltage and current) are taken as inputs in these RNNs. Considering that the mapping relationship between the SOC and the time-series measurements evolves along with the battery degradation, there still remains a challenge for RNNs to estimate the SOC accurately throughout the battery's lifetime. In this paper, a dual-input neural network combining gated recurring unit (GRU) layers and fully connected layers (acronymized as a DIGF network) is developed to overcome the above-mentioned challenge. Its most important characteristic is the adoption of the state of health (SOH) of the battery as the network input, in addition to time-series measurements. According to the experimental data from a batch of LiCoO batteries, it is validated that the proposed DIGF network is capable of providing more accurate SOC estimations throughout the battery's lifetime compared to the existing RNN counterparts. Moreover, it also shows greater robustness against different initial SOCs, making it more applicable for online SOC estimations in practical situations. Based on these verification results, it is concluded that the proposed DIGF network is feasible for estimating the battery's SOC accurately throughout the battery's lifetime against varying initial SOCs.
锂离子电池的在线荷电状态（SOC）估计是电池管理系统确保其运行安全性和可靠性的最重要任务之一。由于循环神经网络（RNN）在学习序列数据之间的长期依赖关系方面具有优势，因此得到了发展，并在SOC估计方面显示出其优越性。然而，这些RNN仅将时间序列测量值（例如电压和电流）作为输入。考虑到SOC与时间序列测量值之间的映射关系会随着电池退化而演变，RNN在整个电池寿命期间准确估计SOC仍然存在挑战。本文提出了一种结合门控循环单元（GRU）层和全连接层的双输入神经网络（简称为DIGF网络）来克服上述挑战。其最重要的特点是除了时间序列测量值之外，还采用电池的健康状态（SOH）作为网络输入。根据一批钴酸锂电池的实验数据，验证了所提出的DIGF网络在整个电池寿命期间能够比现有的RNN同类网络提供更准确的SOC估计。此外，它在面对不同的初始SOC时也表现出更强的鲁棒性，使其更适用于实际情况下的在线SOC估计。基于这些验证结果，可以得出结论，所提出的DIGF网络在针对不同初始SOC估计电池的SOC方面在整个电池寿命期间是可行的。
