Accurately predicting lithium-ion batteries (LIBs) lifespan is pivotal for optimizing usage and preventing accidents. Previous approaches often relied on inputs challenging to measure in real-time, and failed to capture intra- and inter-cycle data patterns simultaneously. Our study employ attention mechanisms (AM) to develop data-driven models predicting LIB lifespan using easily measurable inputs. Developed model integrates recurrent neural network and convolutional neural network, featuring two types of AMs: temporal attention (TA) and cyclic attention (CA). TA identifies important time steps within each cycle, CA strives to capture key features of inter-cycle correlations through self-attention (SA). We apply the developed model to publicly available data consisting of three batches of cycling modes. TA scores highlight the rest phase as a key characteristic to distinguish different batches. By leveraging CA scores, we decreased the input dimension from 100 cycles to 50 and 30 cycles with single- and multi-head attention.

翻译：准确预测锂离子电池的寿命对于优化使用和预防事故至关重要。以往的方法通常依赖难以实时测量的输入，且未能同时捕捉循环内和循环间的数据模式。本研究采用注意力机制，利用易于测量的输入开发数据驱动模型，以预测锂离子电池寿命。所开发的模型集成了循环神经网络和卷积神经网络，包含两种注意力机制：时间注意力和循环注意力。时间注意力识别每个循环内的重要时间步长，循环注意力则通过自注意力机制捕捉循环间相关性的关键特征。我们将该模型应用于由三批不同循环模式组成的公开数据集。时间注意力得分表明，休息阶段是区分不同批次的典型特征。通过利用循环注意力得分，我们利用单头和多头注意力将输入维度从100个循环减少至50个和30个循环。