Spatial-temporal Attention-Based Time Series Prediction Network for Lithium Battery Remaining Useful Life Estimation

• Published in: 2024 4th International Conference on Neural Networks, Information and Communication (NNICE) pp. 91 - 94
• Main Authors: Bao, Zhengyi, Lin, Huipin, Guan, Siwei, Zheng, Xiaorong, Li, Zhi, Gao, Mingyu
• Format: Conference Proceeding
• Language: English
• Published: IEEE 19.01.2024
• Subjects: Battery management systems; Correlation; Feature extraction; Gate Recurrent Unit; Life estimation; Lithium Battery; NASA; Predictive models; Remaining Useful Life; Spatio-temporal Attention; Time series analysis
• DOI: 10.1109/NNICE61279.2024.10498716

The accurate estimation of the remaining useful life (RUL) of lithium batteries is a pivotal aspect in battery management systems, essential for efficient battery management, optimization of battery performance, and enhanced user experience. Presently, prevailing deep learning methods for battery RU...