# Pace: Physics-Aware Attentive Temporal Convolutional Network for Battery Health Estimation

arXiv: https://arxiv.org/abs/2512.11332 · HTML: https://mlsanity.com/paper/2512.11332/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 21 references that are in the map.
- First posted on arXiv 12 Dec 2025.

## Builds on (most cited first, 6 of 6)

- [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://mlsanity.com/paper/2012.07436/) (2020)
- [What Makes Convolutional Models Great on Long Sequence Modeling?](https://mlsanity.com/paper/2210.09298/) (2022)
- [LightCTS: A Lightweight Framework for Correlated Time Series Forecasting](https://mlsanity.com/paper/2302.11974/) (2023)
- [Chunked Attention-based Encoder-Decoder Model for Streaming Speech Recognition](https://mlsanity.com/paper/2309.08436/) (2023)
- [GiNet: Integrating Sequential and Context-Aware Learning for Battery Capacity Prediction](https://mlsanity.com/paper/2501.04997/) (2025)
- [Optimizing Cycle Life Prediction of Lithium-ion Batteries via a Physics-Informed Model](https://mlsanity.com/paper/2404.17174/) (2024)

## Built on by (most cited first, 2 of 2)

- [Interpretable Battery Aging without Extra Tests via Neural-Assisted Physics-based Modelling](https://mlsanity.com/paper/2604.01229/) (2026)
- [PhyMamba: Physics-Modulated Mamba for Robust Battery Health Prognostics](https://mlsanity.com/paper/2608.27978/) (2026)

## Abstract (arXiv)

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.