A Hybrid CNN-LSTM for Battery Remaining Useful Life Prediction with Charging Profiles Data-Reference-Cited by-同舟云学术

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

Published:2022-11-22 Issue: Volume: Page:
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Container-title:Proceedings of the 2022 International Conference on Computer, Control, Informatics and Its Applications
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Author:

Hafizhahullah Huzaifi¹^ORCID,Yuliani Asri Rizki²^ORCID,Pardede Hilman²^ORCID,Ramdan Ade²^ORCID,Zilvan Vicky²^ORCID,Krisnandi Dikdik²^ORCID,Kadar Jimmy²^ORCID

Affiliation:

1. Universitas Negeri Jakarta, Indonesia

2. National Research and Innovation Agency (BRIN), Indonesia

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3575882.3575903

Reference26 articles.

1. Machine Learning-Based Lithium-Ion Battery Capacity Estimation Exploiting Multi-Channel Charging Profiles

2. Ian Goodfellow , Yoshua Bengio , and Aaron Courville . 2016. Deep Learning . MIT Press . http://www.deeplearningbook.org. Ian Goodfellow, Yoshua Bengio, and Aaron Courville. 2016. Deep Learning. MIT Press. http://www.deeplearningbook.org.

3. A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: Challenges and recommendations

4. Long Short-Term Memory

5. Learning spatial-temporal features for video copy detection by the combination of CNN and RNN

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