Accurate estimation of state of charge (SOC) constitutes the basis to enable the reliable operations of lithium-ion batteries. The recent development in deep learning provides an emerging solution to SOC estimation. However, the limited training and testing profiles and the ignorance of battery working principles jeopardise the performance of deep learning-based methods. In this study, we propose to incorporate two kinds of domain knowledge into the deep learning-based methods. First, voltage and current sequences are decoupled into open circuit voltage (OCV), ohmic response and polarisation voltage to augment the input of deep neural networks (DNNs). Second, as conventional DNNs ignore the time-dependency in SOC estimation results, we propose a combination framework to adaptively fuse the SOC estimation results from the DNN and short-term Ampere-hour predictions. The proposed method is validated on a large dataset which is collected by conducting the tests on eight batteries at various real-world driving profiles and is compared with a basic long short-term memory DNN based on the input of only voltage and current. The results show that the proposed method can sharply reduce the SOC estimation root mean square error and maximum absolute error by 30.89% and 64.88%, respectively, with only slightly increased computational cost. Further validations under different temperatures and the applications of the proposed method to other DNNs also demonstrate its effectiveness. These results highlight the potential to boost the performance of DNNs by making effective use of battery domain knowledge.

充电状态 (SOC) 的准确估计是锂离子电池可靠运行的基础。深度学习的最新发展为 SOC 估计提供了一种新兴的解决方案。然而，有限的训练和测试配置文件以及对电池工作原理的无知会危及基于深度学习的方法的性能。在这项研究中，我们建议将两种领域知识纳入基于深度学习的方法中。首先，将电压和电流序列解耦为开路电压 (OCV)、欧姆响应和极化电压，以增加深度神经网络 (DNN) 的输入。其次，由于传统的 DNN 忽略了 SOC 估计结果的时间依赖性，我们提出了一个组合框架，以自适应地融合来自 DNN 和短期安时预测的 SOC 估计结果。所提出的方法在一个大型数据集上进行了验证，该数据集是通过在各种真实世界驾驶配置文件中对八个电池进行测试而收集的，并与仅基于电压和电流输入的基本长短期记忆 DNN 进行比较。结果表明，该方法可以将SOC估计均方根误差和最大绝对误差分别大幅降低30.89%和64.88%，而计算成本仅略有增加。在不同温度下的进一步验证以及所提出的方法在其他 DNN 中的应用也证明了它的有效性。