Deep Learning Regression with Sequences of Different Length: An Application for State of Health Trajectory Prediction and Remaining Useful Life Estimation in Lithium-Ion Batteries

This study presents methods to handle deep learning regressions with input and output sequences of different lengths. We discuss the Autoregressive one-step prediction framework and introduce an innovative one-time multi-step (OTMS) prediction approach, based on a custom loss function, that predicts...

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Principais autores:	Michele Bellomo, Spyridon Giazitzis, Susheel Badha, Filippo Rosetti, Alberto Dolara, Emanuele Ogliari
格式:	Artigo
語言:	Inglês
出版:	MDPI AG 2024-08-01
叢編:	Batteries
主題:	RUL SoH trajectory batteries time series forecasting deep learning
在線閱讀:	https://www.mdpi.com/2313-0105/10/8/292
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