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Realistic fault detection of li-ion battery via dynamical deep learning

Engineering and Technology

Realistic fault detection of li-ion battery via dynamical deep learning

J. Zhang, Y. Wang, et al.

Revolutionary research conducted by Jingzhao Zhang and colleagues introduces a cutting-edge deep-learning framework for Li-ion battery anomaly detection, significantly cutting inspection costs and enhancing safety. With over 690,000 charging data points analyzed, this groundbreaking work showcases the power of deep learning in addressing complex battery issues while considering social and financial factors.

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