# GiNet: Integrating Sequential and Context-Aware Learning for Battery Capacity Prediction

arXiv: https://arxiv.org/abs/2501.04997 · HTML: https://mlsanity.com/paper/2501.04997/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 14 references that are in the map.
- First posted on arXiv 9 Jan 2025.

## Builds on (most cited first, 5 of 5)

- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://mlsanity.com/paper/2012.07436/) (2020)
- [A Survey of Transformers](https://mlsanity.com/paper/2106.04554/) (2021)
- [Nutrition Estimation for Dietary Management: A Transformer Approach with Depth Sensing](https://mlsanity.com/paper/2406.01938/) (2024)
- [Practical Battery Health Monitoring using Uncertainty-Aware Bayesian Neural Network](https://mlsanity.com/paper/2404.14444/) (2024)

## Built on by (most cited first, 3 of 3)

- [Pace: Physics-Aware Attentive Temporal Convolutional Network for Battery Health Estimation](https://mlsanity.com/paper/2512.11332/) (2025)
- [Optimal Signal Decomposition-based Multi-Stage Learning for Battery Health Estimation](https://mlsanity.com/paper/2501.16377/) (2025)
- [Interpretable Battery Aging without Extra Tests via Neural-Assisted Physics-based Modelling](https://mlsanity.com/paper/2604.01229/) (2026)

## Abstract (arXiv)

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.