Remaining useful life prediction of a lithium-ion battery based on a temporal convolutional network with data extension - Zhao, Jing; Liu, Dayong; Meng, Lingshuai (Zielona Góra: Uniwersytet Zielonogórski)
Unmanned underwater vehicles are typically deployed in deep sea environments, which present unique working conditions. Lithium-ion power batteries are crucial for powering underwater vehicles, and it is vital to accurately predict their remaining useful life (RUL) to maintain system reliability and safety.
Keywords: lithium-ion battery; remaining useful life; complete EEMD with adaptive noise; temporal convolutional net