Zhao Feng-Ming, Gao De-Xin, Cheng Yuan-Ming, Yang Qing
Department of Automation and Electronic Engineering, Qingdao University of Science and Technology, Qingdao, 266061, China.
Department of Computer Science and Technology, Qingdao University of Science and Technology, Qingdao, 266061, China.
Sci Rep. 2024 Aug 8;14(1):18391. doi: 10.1038/s41598-024-69424-1.
Accurately predicting the state of health (SOH) of lithium-ion batteries is fundamental in estimating their remaining lifespan. Various parameters such as voltage, current, and temperature significantly influence the battery's SOH. However, existing data-driven methods necessitate substantial data from the target domain for training, which hampers the assessment of lithium-ion battery health at the initial stage. To address these challenges, this paper introduces the multi-head attention-time convolution network (MHAT-TCN), amalgamating multi-head attention learning with random block dropout techniques. Additionally, it employs grey relational analysis (GRA) to select health indicators (HIs) highly correlated with battery capacity, thereby enhancing the accuracy of the model training. Employing leave-one-out crossvalidation (LOOCV), the MHAT-TCN network is pre-trained using data from batteries of the same model to facilitate comprehensive prediction of the target battery throughout its operational period. Results demonstrate that the MHAT-TCN network trained on HIs outperforms other models, enabling precise predictions across the entire operational period.
准确预测锂离子电池的健康状态（SOH）对于估计其剩余寿命至关重要。诸如电压、电流和温度等各种参数会显著影响电池的SOH。然而，现有的数据驱动方法需要来自目标领域的大量数据进行训练，这在初始阶段阻碍了对锂离子电池健康状况的评估。为应对这些挑战，本文引入了多头注意力-时间卷积网络（MHAT-TCN），将多头注意力学习与随机块丢弃技术相结合。此外，它采用灰色关联分析（GRA）来选择与电池容量高度相关的健康指标（HI），从而提高模型训练的准确性。采用留一法交叉验证（LOOCV），使用来自同一型号电池的数据对MHAT-TCN网络进行预训练，以便在目标电池的整个运行期间进行全面预测。结果表明，在HI上训练的MHAT-TCN网络优于其他模型，能够在整个运行期间进行精确预测。
