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

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dc.citedby5
dc.contributor.authorReza M.S.en_US
dc.contributor.authorHannan M.A.en_US
dc.contributor.authorMansor M.en_US
dc.contributor.authorKer P.J.en_US
dc.contributor.authorRahman S.A.en_US
dc.contributor.authorJang G.en_US
dc.contributor.authorMahlia T.M.I.en_US
dc.contributor.authorid59055914200en_US
dc.contributor.authorid7103014445en_US
dc.contributor.authorid6701749037en_US
dc.contributor.authorid37461740800en_US
dc.contributor.authorid59409302500en_US
dc.contributor.authorid7102646117en_US
dc.contributor.authorid56997615100en_US
dc.date.accessioned2025-03-03T07:41:53Z
dc.date.available2025-03-03T07:41:53Z
dc.date.issued2024
dc.description.abstractAn 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 Ltden_US
dc.description.natureFinalen_US
dc.identifier.ArtNo113056
dc.identifier.doi10.1016/j.est.2024.113056
dc.identifier.scopus2-s2.0-85199135839
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85199135839&doi=10.1016%2fj.est.2024.113056&partnerID=40&md5=f5342d096aca953b8d7d1fda02fc349f
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36309
dc.identifier.volume98
dc.publisherElsevier Ltden_US
dc.sourceScopus
dc.sourcetitleJournal of Energy Storage
dc.subjectBattery management systems
dc.subjectDrops
dc.subjectExtraction
dc.subjectForecasting
dc.subjectIons
dc.subjectLearning algorithms
dc.subjectLearning systems
dc.subjectLong short-term memory
dc.subjectMean square error
dc.subjectNASA
dc.subjectParameter estimation
dc.subjectParticle swarm optimization (PSO)
dc.subjectUncertainty analysis
dc.subjectData feature
dc.subjectDeep learning
dc.subjectLearn+
dc.subjectMemory modeling
dc.subjectOptimization algorithms
dc.subjectRemaining useful life predictions
dc.subjectSearch Algorithms
dc.subjectSystematic sampling
dc.subjectUncertainty
dc.subjectUncertainty parameters
dc.subjectLithium-ion batteries
dc.titleTowards enhanced remaining useful life prediction of lithium-ion batteries with uncertainty using optimized deep learning algorithmen_US
dc.typeArticleen_US
dspace.entity.typePublication
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