The surging demand for batteries requires advanced battery management systems, where battery capacity modelling is a key functionality. In this paper, we aim to achieve accurate battery capacity prediction by learning from historical measurements of battery dynamics. We propose GiNet, a gated recurrent units enhanced Informer network, for predicting battery's capacity. The novelty and competitiveness of GiNet lies in its capability of capturing sequential and contextual information from raw battery data and reflecting the battery's complex behaviors with both temporal dynamics and long-term dependencies. We conducted an experimental study based on a publicly available dataset to showcase GiNet's strength of gaining a holistic understanding of battery behavior and predicting battery capacity accurately. GiNet achieves 0.11 mean absolute error for predicting the battery capacity in a sequence of future time slots without knowing the historical battery capacity. It also outperforms the latest algorithms significantly with 27% error reduction on average compared to Informer. The promising results highlight the importance of customized and optimized integration of algorithm and battery knowledge and shed light on other industry applications as well.

本研究解决了电池容量预测中的准确性问题，提出了一种名为GiNet的模型，能够从电池动态的历史测量中学习并实现精确预测。GiNet通过捕获原始电池数据的序列和上下文信息，反映电池的复杂行为，实验结果表明该模型在未来时间段中实现了0.11的平均绝对误差，相较于最新算法减少了27%的误差，具有重要的应用潜力。

GiNet：集成序列和上下文感知学习的电池容量预测