  Lithium-ion batteries are widely used in various applications, including
electric vehicles and renewable energy storage. The prediction of the remaining
useful life (RUL) of batteries is crucial for ensuring reliable and efficient
operation, as well as reducing maintenance costs. However, determining the life
cycle of batteries in real-world scenarios is challenging, and existing methods
have limitations in predicting the number of cycles iteratively. In addition,
existing works often oversimplify the datasets, neglecting important features
of the batteries such as temperature, internal resistance, and material type.
To address these limitations, this paper proposes a two-stage RUL prediction
scheme for Lithium-ion batteries using a spatio-temporal multimodal attention
network (ST-MAN). The proposed ST-MAN is to capture the complex spatio-temporal
dependencies in the battery data, including the features that are often
neglected in existing works. Despite operating without prior knowledge of
end-of-life (EOL) events, our method consistently achieves lower error rates,
boasting mean absolute error (MAE) and mean square error (MSE) of 0.0275 and
0.0014, respectively, compared to existing convolutional neural networks (CNN)
and long short-term memory (LSTM)-based methods. The proposed method has the
potential to improve the reliability and efficiency of battery operations and
is applicable in various industries.
