Accurately predicting the lifespan of lithium-ion batteries (LIBs) is pivotal for optimizing usage and preventing accidents. Previous studies in constructing prediction models often relied on inputs challenging to measure in real-time operations and failed to capture intra-cycle and inter-cycle data patterns, essential features for accurate predictions, comprehensively. In this study, we employ attention mechanisms (AM) to develop data-driven models for predicting LIB lifespan using easily measurable inputs such as voltage, current, temperature, and capacity data. The developed model integrates recurrent neural network (RNN) and convolutional neural network (CNN) components, featuring two types of attention mechanisms: temporal attention (TA) and cyclic attention (CA). The inclusion of TA aims to identify important time steps within each cycle by scoring the hidden states of the RNN, whereas CA strives to capture key features of inter-cycle correlations through self-attention (SA). This enhances model accuracy and elucidates critical features in the input data. To validate our method, we apply it to publicly available cycling data consisting of three batches of cycling modes. The calculated TA scores highlight the rest phase as a key characteristic distinguishing LIB data among different batches. Additionally, CA scores reveal variations in the importance of cycles across batches. By leveraging CA scores, we explore the potential to reduce the number of cycles in the input data. The single-head and multi-head attentions enable us to decrease the input dimension from 100 to 50 and 30 cycles, respectively.

翻译：准确预测锂离子电池（LIB）寿命对于优化使用和预防事故至关重要。以往构建预测模型的研究往往依赖实时运行中难以测量的输入数据，且未能全面捕捉周期内和周期间的数据模式——而这些是实现精确预测的关键特征。本研究采用注意力机制（AM）构建数据驱动模型，利用电压、电流、温度和容量等易测量的输入数据预测LIB寿命。所开发模型融合了循环神经网络（RNN）与卷积神经网络（CNN）组件，并引入两种注意力机制：时间注意力（TA）和循环注意力（CA）。TA通过对RNN隐藏状态进行评分以识别每个周期内的重要时间步长，而CA则通过自注意力（SA）捕捉周期间相关性的关键特征。这既提升了模型精度，也揭示了输入数据中的关键特征。为验证方法有效性，我们将其应用于包含三批循环模式的公开循环数据集。计算得到的TA评分表明，休息阶段是区分不同批次LIB数据的关键特征。此外，CA评分揭示了不同批次间周期重要性的差异。利用CA评分，我们探索了减少输入数据中周期数量的可能性。单头注意力与多头注意力机制分别使输入维度从100个周期降至50个和30个周期。