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

2025-01-09

AI Summary

To address the challenge of remaining useful life (RUL) prediction in battery health management—where capacity is unobservable and historical capacity labels are unavailable—this paper proposes a label-free end-to-end time-series regression method. The approach introduces a GRU-enhanced Informer hybrid architecture: gated recurrent units model short-term dynamic evolution, an improved self-attention mechanism captures long-term degradation dependencies, and gated feature selection enables joint perception of sequential patterns and contextual information. Crucially, the method directly predicts battery capacity from raw time-series measurements (e.g., voltage and current) without requiring ground-truth capacity labels. Evaluated on public benchmark datasets, it achieves a mean absolute error of 0.11, representing a 27% reduction compared to the standard Informer. This yields significantly improved accuracy and practicality for multi-step RUL forecasting.

Technology Category

• Machine Learning: Life-Long and Continual Learning
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.
• Intelligent Robots: State Estimation

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

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.

Problem

• Battery Life Prediction
• Remaining Useful Life
• Battery Management System

Innovation

• GiNet
• Battery Life Prediction
• Sequential Learning