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

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.

Dr. Sungho Suh

Senior Researcher

Human Activity Recognition, Safe and Trusted Human Centric Artificial Intelligence in Future Manufacturing Lines

Prof. Dr. rer. nat. Paul (Pawel) Lukowicz

Professor (W3) “Embedded Intelligence”

Paul Lukowicz is Full Professor of AI at the Technical University of Kaiserslautern in Germany where he is heading the Embedded Intelligence group at DFKI. From 2006 till 2011 he has been full Professor (W3) of Computer Science at the University of Passau. He has also been a senior researcher (“Oberassistent”) at the Electronics Laboratory at the Department of Information Technology and Electrical Engineering of ETH Zurich Paul Lukowicz has MSc. (Dipl. Inf.) and a Ph.D. (Dr. rer nat.) in Computer Science a MSc. in Physics (Dipl. Phys.). His research focus are context aware ubiquitous and wearable systems including sensing, pattern recognition, system architectures, models of large scale self-organized systems, and applications. Paul Lukowicz coordinates the FP7-FET SOCIONICAL projects, is Associate Editor in Chief of IEEE Pervasive Computing Magazine, and has been serving as TPC Chair of a number of international events in the area