A Model-Data-Fusion Pole Piece Thickness Prediction Method With Multisensor Fusion for Lithium Battery Rolling Machine

A Model-Data-Fusion Pole Piece Thickness Prediction Method With Multisensor Fusion for Lithium Battery Rolling Machine

Trend prediction based on sensor data is an important topic in the thickness control system of lithium battery electrode mills. As the number of sensors increases, we can measure and store more and more data. The characteristics of nonlinearity, uncertainty, and time-variability in the lithium batte...

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Bibliographic Details
Main Authors: Yanjun Xiao, Shuhan Deng, Furong Han, Xiaoliang Wang, Zonghua Zhang, Kai Peng
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Access
Subjects:
Multiform coupling
time series forecasting
Bayesian LSTM
deep fusion predictor
Online Access:https://ieeexplore.ieee.org/document/9779730/

Internet

https://ieeexplore.ieee.org/document/9779730/

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