融合电池物理模型与注意力机制，提升健康状态预测精度
Pace: Physics-Aware Attentive Temporal Convolutional Network for Battery Health Estimation
- 结合等效电路模型提取物理特征，增强时序建模能力
- 在公开数据集上相比最佳基线提升6.5%和2.0倍性能
- 可部署于树莓派实现边缘实时推理，适合实际应用
电池是电动汽车和电网储能等现代能源系统的核心组件。有效的电池健康管理对系统安全、成本效益和可持续性至关重要。本文提出Pace，一种面向电池健康估计的物理感知注意力时序卷积网络。Pace将原始传感器数据与源自等效电路模型的电池物理特征相结合，设计了三种电池专用模块：用于高效时序编码的膨胀时序块、用于上下文建模的分块注意力块，以及用于融合短长期退化模式的双头输出块。这些模块使Pace能在多种使用条件下准确高效地预测电池健康状态。在大规模公开数据集上，Pace性能显著优于现有模型，平均较两个表现最佳的基线模型分别提升6.5%和2.0倍。我们进一步通过在树莓派上的实时边缘部署验证了其实用性。结果表明，Pace是一种兼具高性能与实用性的电池健康分析解决方案。
原文摘要 · Abstract (English)
Batteries are critical components in modern energy systems such as electric vehicles and power grid energy storage. Effective battery health management is essential for battery system safety, cost-efficiency, and sustainability. In this paper, we propose Pace, a physics-aware attentive temporal convolutional network for battery health estimation. Pace integrates raw sensor measurements with battery physics features derived from the equivalent circuit model. We develop three battery-specific modules, including dilated temporal blocks for efficient temporal encoding, chunked attention blocks for context modeling, and a dual-head output block for fusing short- and long-term battery degradation patterns. Together, the modules enable Pace to predict battery health accurately and efficiently in various battery usage conditions. In a large public dataset, Pace performs much better than existing models, achieving an average performance improvement of 6.5 and 2.0x compared to two best-performing baseline models. We further demonstrate its practical viability with a real-time edge deployment on a Raspberry Pi. These results establish Pace as a practical and high-performance solution for battery health analytics.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。