Remaining useful life prediction of Lithium-ion batteries using spatio-temporal multimodal attention networks

• URL: http://arxiv.org/abs/2310.18924v2
• Date: Thu, 6 Jun 2024 12:20:48 GMT
• Title: Remaining useful life prediction of Lithium-ion batteries using spatio-temporal multimodal attention networks
• Authors: Sungho Suh, Dhruv Aditya Mittal, Hymalai Bello, Bo Zhou, Mayank Shekhar Jha, Paul Lukowicz,
• Abstract summary: 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. This paper proposes a two-stage RUL prediction scheme for Lithium-ion batteries using a-temporal attention network (ST-MAN)
• Score: 4.249657064343807
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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