research-article

A Hybrid CNN-LSTM for Battery Remaining Useful Life Prediction with Charging Profiles Data

Authors:

Huzaifi Hafizhahullah,

Asri Rizki Yuliani,

Hilman Pardede,

Dikdik Krisnandi,

Jimmy KadarAuthors Info & Claims

IC3INA '22: Proceedings of the 2022 International Conference on Computer, Control, Informatics and Its Applications

Pages 106 - 110

https://doi.org/10.1145/3575882.3575903

Published: 27 February 2023 Publication History

Abstract

The capacity degradation of battery can occur due to continuously used as primary energy source equipment. An accurate prediction of battery remaining useful life (RUL) is necessary to avoid system functionality failure. This study proposes battery RUL prediction using data-driven method based on a hybrid deep model of Convolutional Neural Networks (CNN) and Long-Short Term Memory (LSTM). CNN and LSTM are used to extract features from multiple measurable data in parallel. CNN extracts features of multi-channel charging profiles, whereas LSTM extracts features of historical capacity data of discharging profiles which related to time dependency. An error index is compared between single model LSTM and hybrid model CNN-LSTM. The result indicates that the proposed hybrid model outperforms the single model by up to 37%-61% in case of mean absolute percentage error.

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Cited By

Safavi VMohammadi Vaniar ABazmohammadi NVasquez JGuerrero J(2024)Battery Remaining Useful Life Prediction Using Machine Learning Models: A Comparative StudyInformation10.3390/info1503012415:3(124)Online publication date: 22-Feb-2024
https://doi.org/10.3390/info15030124
Chen BLiu YXiao B(2024)A novel hybrid neural network-based SOH and RUL estimation method for lithium-ion batteriesJournal of Energy Storage10.1016/j.est.2024.11307498(113074)Online publication date: Sep-2024
https://doi.org/10.1016/j.est.2024.113074
Rastegarpanah AContreras CStolkin R(2023)Hyperparameter-optimized CNN and CNN-LSTM for Predicting the Remaining Useful Life of Lithium-Ion Batteries2023 Eleventh International Conference on Intelligent Computing and Information Systems (ICICIS)10.1109/ICICIS58388.2023.10391176(110-115)Online publication date: 21-Nov-2023
https://doi.org/10.1109/ICICIS58388.2023.10391176

Index Terms

A Hybrid CNN-LSTM for Battery Remaining Useful Life Prediction with Charging Profiles Data
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks

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IC3INA '22: Proceedings of the 2022 International Conference on Computer, Control, Informatics and Its Applications

November 2022

415 pages

ISBN:9781450397902

DOI:10.1145/3575882

Copyright © 2022 ACM.

© 2022 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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Published: 27 February 2023

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IC3INA 2022

IC3INA 2022: The 2022 International Conference on Computer, Control, Informatics and Its Applications

November 22 - 23, 2022

Virtual Event, Indonesia

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Cited By

Safavi VMohammadi Vaniar ABazmohammadi NVasquez JGuerrero J(2024)Battery Remaining Useful Life Prediction Using Machine Learning Models: A Comparative StudyInformation10.3390/info1503012415:3(124)Online publication date: 22-Feb-2024
https://doi.org/10.3390/info15030124
Chen BLiu YXiao B(2024)A novel hybrid neural network-based SOH and RUL estimation method for lithium-ion batteriesJournal of Energy Storage10.1016/j.est.2024.11307498(113074)Online publication date: Sep-2024
https://doi.org/10.1016/j.est.2024.113074
Rastegarpanah AContreras CStolkin R(2023)Hyperparameter-optimized CNN and CNN-LSTM for Predicting the Remaining Useful Life of Lithium-Ion Batteries2023 Eleventh International Conference on Intelligent Computing and Information Systems (ICICIS)10.1109/ICICIS58388.2023.10391176(110-115)Online publication date: 21-Nov-2023
https://doi.org/10.1109/ICICIS58388.2023.10391176

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