Remaining Useful Life Prediction of Lithium-ion Batteries using
  Spatio-temporal Multimodal Attention Networks
Sungho Suh Dhruv Aditya Mittal Hymalai Bello Bo Zhou Mayank Shekhar Jha Paul Lukowicz
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 remaining useful
life prediction scheme for Lithium-ion batteries using a spatio-temporal
multimodal attention network (ST-MAN). The proposed model is designed to
iteratively predict the number of cycles required for the battery to reach the
end of its useful life, based on available data. 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. Experimental results
demonstrate that the proposed ST-MAN model outperforms existing CNN and
LSTM-based methods, achieving state-of-the-art performance in predicting the
remaining useful life of Li-ion batteries. The proposed method has the
potential to improve the reliability and efficiency of battery operations and
is applicable in various industries, including automotive and renewable energy.