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

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dc.citedby2
dc.contributor.authorReza M.S.en_US
dc.contributor.authorHannan M.A.en_US
dc.contributor.authorMansor M.B.en_US
dc.contributor.authorKer P.J.en_US
dc.contributor.authorTiong S.K.en_US
dc.contributor.authorHossain M.J.en_US
dc.contributor.authorid59055914200en_US
dc.contributor.authorid7103014445en_US
dc.contributor.authorid59437877200en_US
dc.contributor.authorid37461740800en_US
dc.contributor.authorid15128307800en_US
dc.contributor.authorid57209871691en_US
dc.date.accessioned2025-03-03T07:47:04Z
dc.date.available2025-03-03T07:47:04Z
dc.date.issued2024
dc.description.abstractAn 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.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1109/TIA.2024.3429452
dc.identifier.epage9183
dc.identifier.issue6
dc.identifier.scopus2-s2.0-85199092656
dc.identifier.spage9171
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85199092656&doi=10.1109%2fTIA.2024.3429452&partnerID=40&md5=f8cad24425830fcee7dfaa26addbbd8b
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/37060
dc.identifier.volume60
dc.pagecount12
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitleIEEE Transactions on Industry Applications
dc.subjectBrain
dc.subjectDigital storage
dc.subjectForecasting
dc.subjectLearning algorithms
dc.subjectLithium-ion batteries
dc.subjectLong short-term memory
dc.subjectNASA
dc.subjectParticle swarm optimization (PSO)
dc.subjectAccuracy
dc.subjectBattery
dc.subjectCapacity prediction
dc.subjectGravitational search algorithm
dc.subjectOptimisations
dc.subjectPrediction algorithms
dc.subjectPredictive models
dc.subjectRemaining useful lives
dc.subjectSearch Algorithms
dc.subjectUncertainty
dc.subjectDeep neural networks
dc.titleGravitational Search Algorithm Based LSTM Deep Neural Network for Battery Capacity and Remaining Useful Life Prediction With Uncertaintyen_US
dc.typeArticleen_US
dspace.entity.typePublication
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