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

Simple item page
dc.citedby1
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.authorTiong S.K.en_US
dc.contributor.authorHossain M.J.en_US
dc.contributor.authorid59055914200en_US
dc.contributor.authorid7103014445en_US
dc.contributor.authorid6701749037en_US
dc.contributor.authorid37461740800en_US
dc.contributor.authorid15128307800en_US
dc.contributor.authorid57209871691en_US
dc.date.accessioned2024-10-14T03:19:44Z
dc.date.available2024-10-14T03:19:44Z
dc.date.issued2023
dc.description.abstractThis 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. � 2023 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1109/ETFG55873.2023.10407732
dc.identifier.scopus2-s2.0-85185784877
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85185784877&doi=10.1109%2fETFG55873.2023.10407732&partnerID=40&md5=1358008c9b46f5df44c2ee65a675b4c2
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34430
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitle2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023
dc.subjectcapacity prediction
dc.subjectdeep neural network
dc.subjectgravitational search algorithm
dc.subjectlithium-ion batteries
dc.subjectlong short-term memory
dc.subjectremaining useful life
dc.subjectBrain
dc.subjectDigital storage
dc.subjectForecasting
dc.subjectLearning algorithms
dc.subjectLithium-ion batteries
dc.subjectLong short-term memory
dc.subjectNASA
dc.subjectUncertainty analysis
dc.subjectBattery capacity
dc.subjectCapacity prediction
dc.subjectDynamic battery
dc.subjectGravitational search algorithm
dc.subjectHybrid approach
dc.subjectHyper-parameter
dc.subjectRemaining useful life predictions
dc.subjectRemaining useful lives
dc.subjectSearch Algorithms
dc.subjectUncertainty
dc.subjectDeep neural networks
dc.titleGravitational Search Algorithm based Long Short-term Memory Deep Neural Network for Battery Capacity and Remaining Useful Life Prediction with Uncertaintyen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
Files
Collections
SCOPUS