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

• Main Authors: Reza M.S., Hannan M.A., Mansor M., Ker P.J., Tiong S.K., Hossain M.J.
• Other Authors: 59055914200
• Format: Conference Paper
• Published: Institute of Electrical and Electronics Engineers Inc. 2024
• Subjects: 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

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...