用新模型精准预测电池容量，提升电池管理效率。
GiNet: Integrating Sequential and Context-Aware Learning for Battery Capacity Prediction
- 融合时序与上下文信息的新型网络结构
- 预测误差仅0.11，较Informer降低27%
- 适合电池寿命评估与智能管理系统研发
随着电池需求激增，先进的电池管理系统亟需精准的容量建模能力。本文提出GiNet——一种基于门控循环单元增强的Informer网络，用于从电池动态历史数据中学习并预测容量。其核心优势在于同时捕捉原始数据中的时序特征与上下文依赖，全面反映电池复杂的演化行为。在公开数据集上的实验表明，GiNet在不依赖历史容量的前提下，对后续时间序列的容量预测平均绝对误差仅为0.11，显著优于最新算法，相较Informer平均误差降低27%。结果验证了算法与电池知识深度融合的重要性，也为其他工业场景提供了借鉴。
原文摘要 · Abstract (English)
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.
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