所蔵: Gravitational Search Algorithm based Long Short-term Memory Deep Neural Network for Battery Capacity and Remaining Useful Life Prediction with Uncertainty

Gravitational Search Algorithm based Long Short-term Memory Deep Neural Network for Battery Capacity and Remaining Useful Life Prediction with Uncertainty

This paper presents a hybrid approach for predicting the remaining useful life (RUL) and future capacity of lithium-ion batteries (LIBs) using an improved long short-term memory (LSTM) deep neural network with a gravitational search algorithm (GSA). The proposed method address the challenges of nonl...

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主要な著者:	Reza M.S., Hannan M.A., Mansor M., Ker P.J., Tiong S.K., Hossain M.J.
その他の著者:	59055914200
フォーマット:	Conference Paper
出版事項:	Institute of Electrical and Electronics Engineers Inc. 2024
主題:	capacity prediction deep neural network gravitational search algorithm lithium-ion batteries long short-term memory remaining useful life Brain Digital storage Forecasting Learning algorithms Lithium-ion batteries Long short-term memory NASA Uncertainty analysis Battery capacity Capacity prediction Dynamic battery Gravitational search algorithm Hybrid approach Hyper-parameter Remaining useful life predictions Remaining useful lives Search Algorithms Uncertainty Deep neural networks
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Gravitational Search Algorithm based Long Short-term Memory Deep Neural Network for Battery Capacity and Remaining Useful Life Prediction with Uncertainty

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