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Towards enhanced remaining useful life prediction of lithium-ion batteries with uncertainty using optimized deep learning algorithm

An essential component of assessing lithium-ion battery (LIB) performance, reliability, and administration in the application of battery health monitoring and management is determining the battery's Remaining Useful Life (RUL). However, existing RUL prediction approaches have difficulties with...

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Main Authors: Reza M.S., Hannan M.A., Mansor M., Ker P.J., Rahman S.A., Jang G., Mahlia T.M.I.
Other Authors: 59055914200
Format: Article
Published: Elsevier Ltd 2025
Subjects:
Battery management systems
Drops
Extraction
Forecasting
Ions
Learning algorithms
Learning systems
Long short-term memory
Mean square error
NASA
Parameter estimation
Particle swarm optimization (PSO)
Uncertainty analysis
Data feature
Deep learning
Learn+
Memory modeling
Optimization algorithms
Remaining useful life predictions
Search Algorithms
Systematic sampling
Uncertainty
Uncertainty parameters
Lithium-ion batteries
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spelling my.uniten.dspace-363092025-03-03T15:41:53Z Towards enhanced remaining useful life prediction of lithium-ion batteries with uncertainty using optimized deep learning algorithm Reza M.S. Hannan M.A. Mansor M. Ker P.J. Rahman S.A. Jang G. Mahlia T.M.I. 59055914200 7103014445 6701749037 37461740800 59409302500 7102646117 56997615100 Battery management systems Drops Extraction Forecasting Ions Learning algorithms Learning systems Long short-term memory Mean square error NASA Parameter estimation Particle swarm optimization (PSO) Uncertainty analysis Data feature Deep learning Learn+ Memory modeling Optimization algorithms Remaining useful life predictions Search Algorithms Systematic sampling Uncertainty Uncertainty parameters Lithium-ion batteries An essential component of assessing lithium-ion battery (LIB) performance, reliability, and administration in the application of battery health monitoring and management is determining the battery's Remaining Useful Life (RUL). However, existing RUL prediction approaches have difficulties with variability and nonlinearity that occur during battery degradation, data extraction, feature extraction, hyperparameters optimization, and prediction model uncertainty. To address these problems, this paper introduces a novel hybrid approach for RUL prediction, combining a Lightning Search Algorithm (LSA) with a Long-Short Term Memory (LSTM) deep learning model. At first, the hybrid LSA + LSTM model is trained using a comprehensive framework comprising 31 data features, utilizing a mathematical systematic sampling (SS) approach. This sampling technique enables the identification of 10 related data features including temperature, voltage, and current, recorded during each charging cycle from the LIB parameters. Moreover, the LSA optimization technique is introduced to optimally determine the LSTM deep neural model hyperparameters including the number of hidden neurons, learn rate, epoch, learn rate drop factor, learn rate drop period, and gradient decay factor. The effectiveness of the proposed LSA + LSTM model is assessed using battery aging data from the NASA dataset. In addition, to validate the prediction performance of the proposed LSA + LSTM model, extensive comparisons are performed with other popular optimization-based deep learning methods including artificial bee colony (ABC) based LSTM (ABC + LSTM), gravitational search algorithm (GSA) based LSTM (GSA + LSTM), and particle swarm optimization (PSO) based LSTM (PSO + LSTM) model using different error matrices. The robustness of the proposed method is further validated with existing literature as well as with another battery dataset obtained from the MIT Stanford dataset. The RUL prediction results with uncertainty quantification at a 95 % confidence interval (CI) are also analyzed. The findings indicate that the proposed LSA + LSTM model, outperforms other optimization-based LSTM models in predictive accuracy, attaining a minimum Root Mean Square Error (RMSE) of 0.402 %, 0.526 %, 0.263 %, and 0.309 % for B5, B6, B7, and B18 batteries, respectively. In summary, this study offers a promising opportunity for RUL prediction of LIBs with uncertainty, thereby contributing to the harmless and effective operation of battery storage systems. ? 2024 Elsevier Ltd Final 2025-03-03T07:41:53Z 2025-03-03T07:41:53Z 2024 Article 10.1016/j.est.2024.113056 2-s2.0-85199135839 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85199135839&doi=10.1016%2fj.est.2024.113056&partnerID=40&md5=f5342d096aca953b8d7d1fda02fc349f https://irepository.uniten.edu.my/handle/123456789/36309 98 113056 Elsevier Ltd 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 Battery management systems
Drops
Extraction
Forecasting
Ions
Learning algorithms
Learning systems
Long short-term memory
Mean square error
NASA
Parameter estimation
Particle swarm optimization (PSO)
Uncertainty analysis
Data feature
Deep learning
Learn+
Memory modeling
Optimization algorithms
Remaining useful life predictions
Search Algorithms
Systematic sampling
Uncertainty
Uncertainty parameters
Lithium-ion batteries
spellingShingle Battery management systems
Drops
Extraction
Forecasting
Ions
Learning algorithms
Learning systems
Long short-term memory
Mean square error
NASA
Parameter estimation
Particle swarm optimization (PSO)
Uncertainty analysis
Data feature
Deep learning
Learn+
Memory modeling
Optimization algorithms
Remaining useful life predictions
Search Algorithms
Systematic sampling
Uncertainty
Uncertainty parameters
Lithium-ion batteries
Reza M.S.
Hannan M.A.
Mansor M.
Ker P.J.
Rahman S.A.
Jang G.
Mahlia T.M.I.
Towards enhanced remaining useful life prediction of lithium-ion batteries with uncertainty using optimized deep learning algorithm
description An essential component of assessing lithium-ion battery (LIB) performance, reliability, and administration in the application of battery health monitoring and management is determining the battery's Remaining Useful Life (RUL). However, existing RUL prediction approaches have difficulties with variability and nonlinearity that occur during battery degradation, data extraction, feature extraction, hyperparameters optimization, and prediction model uncertainty. To address these problems, this paper introduces a novel hybrid approach for RUL prediction, combining a Lightning Search Algorithm (LSA) with a Long-Short Term Memory (LSTM) deep learning model. At first, the hybrid LSA + LSTM model is trained using a comprehensive framework comprising 31 data features, utilizing a mathematical systematic sampling (SS) approach. This sampling technique enables the identification of 10 related data features including temperature, voltage, and current, recorded during each charging cycle from the LIB parameters. Moreover, the LSA optimization technique is introduced to optimally determine the LSTM deep neural model hyperparameters including the number of hidden neurons, learn rate, epoch, learn rate drop factor, learn rate drop period, and gradient decay factor. The effectiveness of the proposed LSA + LSTM model is assessed using battery aging data from the NASA dataset. In addition, to validate the prediction performance of the proposed LSA + LSTM model, extensive comparisons are performed with other popular optimization-based deep learning methods including artificial bee colony (ABC) based LSTM (ABC + LSTM), gravitational search algorithm (GSA) based LSTM (GSA + LSTM), and particle swarm optimization (PSO) based LSTM (PSO + LSTM) model using different error matrices. The robustness of the proposed method is further validated with existing literature as well as with another battery dataset obtained from the MIT Stanford dataset. The RUL prediction results with uncertainty quantification at a 95 % confidence interval (CI) are also analyzed. The findings indicate that the proposed LSA + LSTM model, outperforms other optimization-based LSTM models in predictive accuracy, attaining a minimum Root Mean Square Error (RMSE) of 0.402 %, 0.526 %, 0.263 %, and 0.309 % for B5, B6, B7, and B18 batteries, respectively. In summary, this study offers a promising opportunity for RUL prediction of LIBs with uncertainty, thereby contributing to the harmless and effective operation of battery storage systems. ? 2024 Elsevier Ltd
author2 59055914200
author_facet 59055914200
Reza M.S.
Hannan M.A.
Mansor M.
Ker P.J.
Rahman S.A.
Jang G.
Mahlia T.M.I.
format Article
author Reza M.S.
Hannan M.A.
Mansor M.
Ker P.J.
Rahman S.A.
Jang G.
Mahlia T.M.I.
author_sort Reza M.S.
title Towards enhanced remaining useful life prediction of lithium-ion batteries with uncertainty using optimized deep learning algorithm
title_short Towards enhanced remaining useful life prediction of lithium-ion batteries with uncertainty using optimized deep learning algorithm
title_full Towards enhanced remaining useful life prediction of lithium-ion batteries with uncertainty using optimized deep learning algorithm
title_fullStr Towards enhanced remaining useful life prediction of lithium-ion batteries with uncertainty using optimized deep learning algorithm
title_full_unstemmed Towards enhanced remaining useful life prediction of lithium-ion batteries with uncertainty using optimized deep learning algorithm
title_sort towards enhanced remaining useful life prediction of lithium-ion batteries with uncertainty using optimized deep learning algorithm
publisher Elsevier Ltd
publishDate 2025
_version_ 1825816223666929664
score 13.244109

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