发表机构
Ton Duc Thang University; The University of Danang—University of Science and Technology; AIWARE Limited Company（孙德胜大学; 岘港大学—科技大学; AIWARE有限公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出交叉专家框架，利用部分充电数据联合预测锂离子电池剩余使用寿命与容量，在公开数据集上取得最低RUL误差。
AI 中文摘要
剩余使用寿命（RUL）的联合预测与容量估计需要同时表征电池的逐渐退化过程和近期行为。本文提出了一种交叉专家框架，使用部分充电测量数据，且无需将历史满循环容量作为输入。RUL专家利用预训练的门控循环单元（GRU）编码器、二维卷积神经网络（2D-CNN）和时序GRU，对30个循环历史中采样的10个循环的标称10分钟片段进行编码。容量专家利用二维卷积神经网络和Transformer处理连续10个循环的标称40分钟片段的统计描述符。特征级线性调制模块利用短期表示来调节长期表示，以实现联合预测。训练过程包括监督自编码器预训练、独立专家预训练以及冻结专家的融合训练。在两个公开的电池老化数据集上，参考配置实现了平均RUL均方根误差分别为143.69和161.10个循环，容量误差分别为12.36和7.28mAh。在数据集I上，融合相对于任一独立专家均降低了平均误差。结果表明RUL与容量精度之间存在权衡：所提出的方法在两个数据集上均取得了所比较方法中最低的RUL RMSE，而若干基线方法则获得了更低的容量误差。
英文摘要
Joint remaining useful life (RUL) prediction and capacity estimation require representations of both gradual degradation and recent battery behavior. This paper presents a cross-expert framework using partial-charging measurements without requiring measured historical full-cycle capacity as an input. The RUL Expert captures long-term degradation from nominal 10-min segments sampled across a 30-cycle history, while the Capacity Expert characterizes recent battery behavior from statistical descriptors of nominal 40-min segments over ten consecutive cycles. Their complementary representations are integrated through feature-wise linear modulation for joint RUL and capacity prediction. A key contribution is a three-stage training strategy that progressively controls frozen and trainable components: supervised representation pretraining, independent expert pretraining, and final fusion training with both experts frozen. This staged optimization preserves expert-specific degradation knowledge while improving the balance between the two prediction tasks, with RUL treated as the primary prognostic objective. On two public battery-aging datasets, the reference configuration achieves mean RUL root-mean-square errors of 143.69 and 161.10 cycles and capacity errors of 12.36 and 7.28 mAh, respectively. On Dataset I, cross-expert fusion reduces both mean errors relative to either standalone expert. The proposed framework achieves the lowest reported RUL RMSE among the compared methods on both datasets while maintaining competitive capacity-estimation accuracy.
