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Gravitational Search Algorithm Based LSTM Deep Neural Network for Battery Capacity and Remaining Useful Life Prediction With Uncertainty

An accurate estimation of the remaining useful life (RUL) and capacity of lithium-ion batteries (LIBs) can guarantee safe and reliable operation and help to make wise replacement decisions. This paper presents an improved approach for predicting the RUL and capacity of LIB using a long short-term me...

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Main Authors: Reza M.S., Hannan M.A., Mansor M.B., Ker P.J., Tiong S.K., Hossain M.J.
Other Authors: 59055914200
Format: Article
Published: Institute of Electrical and Electronics Engineers Inc. 2025
Subjects:
Brain
Digital storage
Forecasting
Learning algorithms
Lithium-ion batteries
Long short-term memory
NASA
Particle swarm optimization (PSO)
Accuracy
Battery
Capacity prediction
Gravitational search algorithm
Optimisations
Prediction algorithms
Predictive models
Remaining useful lives
Search Algorithms
Uncertainty
Deep neural networks
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spelling my.uniten.dspace-370602025-03-03T15:47:04Z Gravitational Search Algorithm Based LSTM Deep Neural Network for Battery Capacity and Remaining Useful Life Prediction With Uncertainty Reza M.S. Hannan M.A. Mansor M.B. Ker P.J. Tiong S.K. Hossain M.J. 59055914200 7103014445 59437877200 37461740800 15128307800 57209871691 Brain Digital storage Forecasting Learning algorithms Lithium-ion batteries Long short-term memory NASA Particle swarm optimization (PSO) Accuracy Battery Capacity prediction Gravitational search algorithm Optimisations Prediction algorithms Predictive models Remaining useful lives Search Algorithms Uncertainty Deep neural networks An accurate estimation of the remaining useful life (RUL) and capacity of lithium-ion batteries (LIBs) can guarantee safe and reliable operation and help to make wise replacement decisions. This paper presents an improved approach for predicting the RUL and capacity of LIB using a long short-term memory (LSTM) deep neural network-integrated with a gravitational search algorithm (GSA) to address the challenges associated with predicting battery life. Initially, data cleaning is carried out to minimize any negative impacts that can reduce the convergence rate. Abnormal data are replaced with highly correlated data, and the data is standardized. Moreover, the LSTM model hyperparameters are optimized using the GSA optimization technique. To evaluate the robustness of the proposed method, 15 prediction samples are generated to calculate the uncertainty levels (95% CI) of the predicted RUL. The proposed method is assessed using aging data from the NASA battery dataset. Its performance is compared with baseline LSTM, baseline GRU, BiLSTM, and LSTM-based particle swarm optimization (PSO) models across various error metrics. The robustness of the proposed method is verified by benchmarking it against other existing approaches for predicting RUL and capacity. The results indicate that the LSTM-GSA model outperforms in prediction accuracy, achieving RMSE values of 1.04%, 1.15%, 1.26%, and 0.92% across different battery cases at both early and later cycle stages. 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. ? 1972-2012 IEEE. Final 2025-03-03T07:47:04Z 2025-03-03T07:47:04Z 2024 Article 10.1109/TIA.2024.3429452 2-s2.0-85199092656 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85199092656&doi=10.1109%2fTIA.2024.3429452&partnerID=40&md5=f8cad24425830fcee7dfaa26addbbd8b https://irepository.uniten.edu.my/handle/123456789/37060 60 6 9171 9183 Institute of Electrical and Electronics Engineers Inc. Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
topic Brain
Digital storage
Forecasting
Learning algorithms
Lithium-ion batteries
Long short-term memory
NASA
Particle swarm optimization (PSO)
Accuracy
Battery
Capacity prediction
Gravitational search algorithm
Optimisations
Prediction algorithms
Predictive models
Remaining useful lives
Search Algorithms
Uncertainty
Deep neural networks
spellingShingle Brain
Digital storage
Forecasting
Learning algorithms
Lithium-ion batteries
Long short-term memory
NASA
Particle swarm optimization (PSO)
Accuracy
Battery
Capacity prediction
Gravitational search algorithm
Optimisations
Prediction algorithms
Predictive models
Remaining useful lives
Search Algorithms
Uncertainty
Deep neural networks
Reza M.S.
Hannan M.A.
Mansor M.B.
Ker P.J.
Tiong S.K.
Hossain M.J.
Gravitational Search Algorithm Based LSTM Deep Neural Network for Battery Capacity and Remaining Useful Life Prediction With Uncertainty
description An accurate estimation of the remaining useful life (RUL) and capacity of lithium-ion batteries (LIBs) can guarantee safe and reliable operation and help to make wise replacement decisions. This paper presents an improved approach for predicting the RUL and capacity of LIB using a long short-term memory (LSTM) deep neural network-integrated with a gravitational search algorithm (GSA) to address the challenges associated with predicting battery life. Initially, data cleaning is carried out to minimize any negative impacts that can reduce the convergence rate. Abnormal data are replaced with highly correlated data, and the data is standardized. Moreover, the LSTM model hyperparameters are optimized using the GSA optimization technique. To evaluate the robustness of the proposed method, 15 prediction samples are generated to calculate the uncertainty levels (95% CI) of the predicted RUL. The proposed method is assessed using aging data from the NASA battery dataset. Its performance is compared with baseline LSTM, baseline GRU, BiLSTM, and LSTM-based particle swarm optimization (PSO) models across various error metrics. The robustness of the proposed method is verified by benchmarking it against other existing approaches for predicting RUL and capacity. The results indicate that the LSTM-GSA model outperforms in prediction accuracy, achieving RMSE values of 1.04%, 1.15%, 1.26%, and 0.92% across different battery cases at both early and later cycle stages. 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. ? 1972-2012 IEEE.
author2 59055914200
author_facet 59055914200
Reza M.S.
Hannan M.A.
Mansor M.B.
Ker P.J.
Tiong S.K.
Hossain M.J.
format Article
author Reza M.S.
Hannan M.A.
Mansor M.B.
Ker P.J.
Tiong S.K.
Hossain M.J.
author_sort Reza M.S.
title Gravitational Search Algorithm Based LSTM Deep Neural Network for Battery Capacity and Remaining Useful Life Prediction With Uncertainty
title_short Gravitational Search Algorithm Based LSTM Deep Neural Network for Battery Capacity and Remaining Useful Life Prediction With Uncertainty
title_full Gravitational Search Algorithm Based LSTM Deep Neural Network for Battery Capacity and Remaining Useful Life Prediction With Uncertainty
title_fullStr Gravitational Search Algorithm Based LSTM Deep Neural Network for Battery Capacity and Remaining Useful Life Prediction With Uncertainty
title_full_unstemmed Gravitational Search Algorithm Based LSTM Deep Neural Network for Battery Capacity and Remaining Useful Life Prediction With Uncertainty
title_sort gravitational search algorithm based lstm deep neural network for battery capacity and remaining useful life prediction with uncertainty
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2025
_version_ 1825816127772557312
score 13.244109

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