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

TL;DR

GiNet is a novel deep learning model that combines sequential and contextual data to accurately predict battery capacity, outperforming existing methods by capturing complex battery behaviors.

Contribution

Introduces GiNet, a gated recurrent units enhanced Informer network, for improved battery capacity prediction by integrating temporal and long-term battery data.

Findings

Achieves 0.11 MAE in capacity prediction

Outperforms existing algorithms with 27% error reduction

Effectively captures complex battery behaviors

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.