Remaining Useful Life Prediction of Lithium-Ion Battery Based on LSTM and GRU
Authors: 
Asri Rizki Yuliani, Ade Ramdan, Vicky Zilvan, Ahmad Afif Supianto, Dikdik Krisnandi, Raden Sandra Yuwana, Dicky Prajitno, Hilman PardedeAuthors Info & Claims
Asri Rizki Yuliani, Ade Ramdan, Vicky Zilvan, Ahmad Afif Supianto, Dikdik Krisnandi, Raden Sandra Yuwana, Dicky Prajitno, Hilman PardedeAuthors Info & ClaimsPages 21 - 25
Abstract
Remaining useful life (RUL) prediction of lithium-ion battery remains a challenging problem. Battery failure can occur when there is an abnormal capacity or power degradation that would lead to system downtime and catastrophic occurrence. Thus, it is necessary to build an accurate prediction model to ensure the battery is reliable and safe. Data-driven method using machine learning has drawn much attention in this research area. This study addresses the battery RUL prediction based on long short-term memory (LSTM) and gated recurrent unit (GRU). A comparison was made between LSTM and GRU model performance and complexity. Experimental results show that GRU could achieve better performance compared to LSTM with almost 20% less than the number of parameters of LSTM.
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Published In
October 2021
204 pages
ISBN:9781450385244
DOI:10.1145/3489088
Copyright © 2021 ACM.
© 2021 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.
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Association for Computing Machinery
New York, NY, United States
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Published: 13 February 2022
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IC3INA 2021
IC3INA 2021: The 2021 International Conference on Computer, Control, Informatics and Its Applications
October 5 - 6, 2021
Virtual/online conference, Indonesia
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