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

Reza, MS; Hannan, MA; Mansor, M; Ker, PJ; Tiong, SK; Hossain, MJ

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

Reza, MS Hannan, MA Mansor, M Ker, PJ Tiong, SK Hossain, MJ

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Conference Proceeding
Citation:: 2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023, 2023, 00, pp. 1-7
Issue Date:: 2023-01-01

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Field	Value	Language
dc.contributor.author	Reza, MS
dc.contributor.author	Hannan, MA
dc.contributor.author	Mansor, M
dc.contributor.author	Ker, PJ
dc.contributor.author	Tiong, SK
dc.contributor.author	Hossain, MJ
dc.date	2023-12-03
dc.date.accessioned	2024-04-17T07:06:48Z
dc.date.available	2024-04-17T07:06:48Z
dc.date.issued	2023-01-01
dc.identifier.citation	2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023, 2023, 00, pp. 1-7
dc.identifier.isbn	9781665471640
dc.identifier.uri	http://hdl.handle.net/10453/178018
dc.description.abstract	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 nonlinear and dynamic battery behavior, battery aging uncertainty, the requirement for optimal hyperparameters tuning, and the importance of maintaining safe and efficient battery operation. The RUL prediction uncertainty with a 95% confidence interval (CI) is also analyzed. The GSA algorithm optimizes the hyperparameters of the LSTM network to construct an optimal model. The method proposed in this work is evaluated based on the aging data from the NASA battery dataset, and its effectiveness is compared with that of BiLSTM, baseline gated recurrent unit (GRU), and baseline LSTM using various error metrics. The results demonstrate that the LSTM-GSA model outperforms other methods in the context of prediction accuracy, achieving a minimum RMSE of 1.04% and 1.15% for both battery cases. Overall, this research provides a promising solution for predicting RUL and the future capacity of LIBs with uncertainty, which is essential for ensuring the safe and efficient operation of energy storage systems.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023
dc.relation.ispartof	2023 IEEE International Conference on Energy Technologies for Future Grids (ETFG)
dc.relation.isbasedon	10.1109/ETFG55873.2023.10407732
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Gravitational Search Algorithm based Long Short-term Memory Deep Neural Network for Battery Capacity and Remaining Useful Life Prediction with Uncertainty
dc.type	Conference Proceeding
utslib.citation.volume	00
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2024-04-17T07:06:47Z
pubs.finish-date	2023-12-06
pubs.publication-status	Published
pubs.start-date	2023-12-03
pubs.volume	00

Abstract:

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 nonlinear and dynamic battery behavior, battery aging uncertainty, the requirement for optimal hyperparameters tuning, and the importance of maintaining safe and efficient battery operation. The RUL prediction uncertainty with a 95% confidence interval (CI) is also analyzed. The GSA algorithm optimizes the hyperparameters of the LSTM network to construct an optimal model. The method proposed in this work is evaluated based on the aging data from the NASA battery dataset, and its effectiveness is compared with that of BiLSTM, baseline gated recurrent unit (GRU), and baseline LSTM using various error metrics. The results demonstrate that the LSTM-GSA model outperforms other methods in the context of prediction accuracy, achieving a minimum RMSE of 1.04% and 1.15% for both battery cases. Overall, this research provides a promising solution for predicting RUL and the future capacity of LIBs with uncertainty, which is essential for ensuring the safe and efficient operation of energy storage systems.

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http://hdl.handle.net/10453/178018