Pace: Physics-Aware Attentive Temporal Convolutional Network for Battery Health Estimation
This addresses battery health management for electric vehicles and grid storage, offering a practical solution with real-time deployment, though it is incremental as it builds on existing temporal and attention methods.
The paper tackled battery health estimation by proposing Pace, a physics-aware attentive temporal convolutional network that integrates sensor data with battery physics features, achieving an average performance improvement of 6.5 and 2.0x over baseline models on a large public dataset.
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.