Prediction of the Remaining Useful Life of Lithium-Ion Battery Using Multilayer Perceptron

Basanta Pancha; Sushil Paudel; Basanta Thapaliya; Tomasz Siewerski; Dayasagar Niraula

doi:doi:10.11648/j.ijecec.20241001.11

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| Peer-Reviewed

Prediction of the Remaining Useful Life of Lithium-Ion Battery Using Multilayer Perceptron

Basanta Pancha

, Sushil Paudel^*

, Basanta Thapaliya

, Tomasz Siewerski

, Dayasagar Niraula

Published in International Journal of Electrical Components and Energy Conversion (Volume 10, Issue 1)

Received: 5 September 2024 Accepted: 23 September 2024 Published: 10 October 2024

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Abstract

Cogitating the reliability of the supply and ensuring continuous delivery of power to the loads, especially in the growing demand for Lithium-Ion batteries in electric vehicle applications, prediction of the remaining useful life of Lithium-Ion batteries is crucial for the timely replacement. For prediction of non-linear and chaotic relationship, experience-based approach, physics-based approach and data driven approach are used among which data driven approach is a model free, accurate and reliable approach. Therefore, a driven approach in predicting remaining useful life can be implemented in the battery management system. This research uses a multilayer perceptron to predict the remaining useful life of the battery. The NASA Ames Prognostics Center of Excellence (PCoE) battery dataset is used to test the proposed methodology. The use of multilayer perceptron for remaining life prediction seems promising despite the significant number of jump points, gaps in data and a small quantity of experimental data in the National Aeronautics and Space Administration (NASA) dataset. The predicted result was obtained with 8.52 % mean absolute error and 9.59 % root mean square error. When compared with the predicted results of different literatures, proposed multilayer perceptron with sliding window approach outperforms most of the existing approach. Incorporation of optimization techniques and hybrid algorithm in proposed approach can further enhance the accuracy of the model.

Published in	International Journal of Electrical Components and Energy Conversion (Volume 10, Issue 1)
DOI	10.11648/j.ijecec.20241001.11
Page(s)	1-17
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Lithium-Ion Battery, Multilayer Perceptron (MLP), Charge-Discharge Cycle, Remaining Useful Life (RUL), Depth of Discharge (DOD)

References

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Cite This Article

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APA Style

Pancha, B., Paudel, S., Thapaliya, B., Siewerski, T., Niraula, D. (2024). Prediction of the Remaining Useful Life of Lithium-Ion Battery Using Multilayer Perceptron. International Journal of Electrical Components and Energy Conversion, 10(1), 1-17. https://doi.org/10.11648/j.ijecec.20241001.11

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ACS Style

Pancha, B.; Paudel, S.; Thapaliya, B.; Siewerski, T.; Niraula, D. Prediction of the Remaining Useful Life of Lithium-Ion Battery Using Multilayer Perceptron. Int. J. Electr. Compon. Energy Convers. 2024, 10(1), 1-17. doi: 10.11648/j.ijecec.20241001.11

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AMA Style

Pancha B, Paudel S, Thapaliya B, Siewerski T, Niraula D. Prediction of the Remaining Useful Life of Lithium-Ion Battery Using Multilayer Perceptron. Int J Electr Compon Energy Convers. 2024;10(1):1-17. doi: 10.11648/j.ijecec.20241001.11

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@article{10.11648/j.ijecec.20241001.11,
  author = {Basanta Pancha and Sushil Paudel and Basanta Thapaliya and Tomasz Siewerski and Dayasagar Niraula},
  title = {Prediction of the Remaining Useful Life of Lithium-Ion Battery Using Multilayer Perceptron
},
  journal = {International Journal of Electrical Components and Energy Conversion},
  volume = {10},
  number = {1},
  pages = {1-17},
  doi = {10.11648/j.ijecec.20241001.11},
  url = {https://doi.org/10.11648/j.ijecec.20241001.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijecec.20241001.11},
  abstract = {Cogitating the reliability of the supply and ensuring continuous delivery of power to the loads, especially in the growing demand for Lithium-Ion batteries in electric vehicle applications, prediction of the remaining useful life of Lithium-Ion batteries is crucial for the timely replacement. For prediction of non-linear and chaotic relationship, experience-based approach, physics-based approach and data driven approach are used among which data driven approach is a model free, accurate and reliable approach. Therefore, a driven approach in predicting remaining useful life can be implemented in the battery management system. This research uses a multilayer perceptron to predict the remaining useful life of the battery. The NASA Ames Prognostics Center of Excellence (PCoE) battery dataset is used to test the proposed methodology. The use of multilayer perceptron for remaining life prediction seems promising despite the significant number of jump points, gaps in data and a small quantity of experimental data in the National Aeronautics and Space Administration (NASA) dataset. The predicted result was obtained with 8.52 % mean absolute error and 9.59 % root mean square error. When compared with the predicted results of different literatures, proposed multilayer perceptron with sliding window approach outperforms most of the existing approach. Incorporation of optimization techniques and hybrid algorithm in proposed approach can further enhance the accuracy of the model. 
},
 year = {2024}
}

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TY  - JOUR
T1  - Prediction of the Remaining Useful Life of Lithium-Ion Battery Using Multilayer Perceptron

AU  - Basanta Pancha
AU  - Sushil Paudel
AU  - Basanta Thapaliya
AU  - Tomasz Siewerski
AU  - Dayasagar Niraula
Y1  - 2024/10/10
PY  - 2024
N1  - https://doi.org/10.11648/j.ijecec.20241001.11
DO  - 10.11648/j.ijecec.20241001.11
T2  - International Journal of Electrical Components and Energy Conversion
JF  - International Journal of Electrical Components and Energy Conversion
JO  - International Journal of Electrical Components and Energy Conversion
SP  - 1
EP  - 17
PB  - Science Publishing Group
SN  - 2469-8059
UR  - https://doi.org/10.11648/j.ijecec.20241001.11
AB  - Cogitating the reliability of the supply and ensuring continuous delivery of power to the loads, especially in the growing demand for Lithium-Ion batteries in electric vehicle applications, prediction of the remaining useful life of Lithium-Ion batteries is crucial for the timely replacement. For prediction of non-linear and chaotic relationship, experience-based approach, physics-based approach and data driven approach are used among which data driven approach is a model free, accurate and reliable approach. Therefore, a driven approach in predicting remaining useful life can be implemented in the battery management system. This research uses a multilayer perceptron to predict the remaining useful life of the battery. The NASA Ames Prognostics Center of Excellence (PCoE) battery dataset is used to test the proposed methodology. The use of multilayer perceptron for remaining life prediction seems promising despite the significant number of jump points, gaps in data and a small quantity of experimental data in the National Aeronautics and Space Administration (NASA) dataset. The predicted result was obtained with 8.52 % mean absolute error and 9.59 % root mean square error. When compared with the predicted results of different literatures, proposed multilayer perceptron with sliding window approach outperforms most of the existing approach. Incorporation of optimization techniques and hybrid algorithm in proposed approach can further enhance the accuracy of the model. 

VL  - 10
IS  - 1
ER  -

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Author Information

Basanta Pancha

Department of Electrical and Electronics Engineering, Nepal Engineering College, Changunarayan, Bhaktapur, Nepal

Contact Email

http://orcid.org/0000-0002-7708-7736
Sushil Paudel

Research Training and Consultancy Division, Kantipur Engineering College, Dhapakhel, Lalitpur, Nepal

Contact Email

http://orcid.org/0009-0005-2759-9986
Basanta Thapaliya

Department of Electrical Engineering, Khwopa College of Engineering, Libali, Bhaktapur, Nepal

Contact Email

http://orcid.org/0009-0008-0973-9262
Tomasz Siewerski

Institute of Electrical Power Engineering, Lodz University of Technology, Lodz, Poland

Contact Email

http://orcid.org/0000-0002-2367-9242
Dayasagar Niraula

Department of Electrical and Computer Engineering, Iowa State University, Ames, United States

Contact Email

http://orcid.org/0009-0008-4692-5849

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Plain Text BibTeX RIS

APA Style

Pancha, B., Paudel, S., Thapaliya, B., Siewerski, T., Niraula, D. (2024). Prediction of the Remaining Useful Life of Lithium-Ion Battery Using Multilayer Perceptron. International Journal of Electrical Components and Energy Conversion, 10(1), 1-17. https://doi.org/10.11648/j.ijecec.20241001.11

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ACS Style

Pancha, B.; Paudel, S.; Thapaliya, B.; Siewerski, T.; Niraula, D. Prediction of the Remaining Useful Life of Lithium-Ion Battery Using Multilayer Perceptron. Int. J. Electr. Compon. Energy Convers. 2024, 10(1), 1-17. doi: 10.11648/j.ijecec.20241001.11

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AMA Style

Pancha B, Paudel S, Thapaliya B, Siewerski T, Niraula D. Prediction of the Remaining Useful Life of Lithium-Ion Battery Using Multilayer Perceptron. Int J Electr Compon Energy Convers. 2024;10(1):1-17. doi: 10.11648/j.ijecec.20241001.11

Copy | Download

@article{10.11648/j.ijecec.20241001.11,
  author = {Basanta Pancha and Sushil Paudel and Basanta Thapaliya and Tomasz Siewerski and Dayasagar Niraula},
  title = {Prediction of the Remaining Useful Life of Lithium-Ion Battery Using Multilayer Perceptron
},
  journal = {International Journal of Electrical Components and Energy Conversion},
  volume = {10},
  number = {1},
  pages = {1-17},
  doi = {10.11648/j.ijecec.20241001.11},
  url = {https://doi.org/10.11648/j.ijecec.20241001.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijecec.20241001.11},
  abstract = {Cogitating the reliability of the supply and ensuring continuous delivery of power to the loads, especially in the growing demand for Lithium-Ion batteries in electric vehicle applications, prediction of the remaining useful life of Lithium-Ion batteries is crucial for the timely replacement. For prediction of non-linear and chaotic relationship, experience-based approach, physics-based approach and data driven approach are used among which data driven approach is a model free, accurate and reliable approach. Therefore, a driven approach in predicting remaining useful life can be implemented in the battery management system. This research uses a multilayer perceptron to predict the remaining useful life of the battery. The NASA Ames Prognostics Center of Excellence (PCoE) battery dataset is used to test the proposed methodology. The use of multilayer perceptron for remaining life prediction seems promising despite the significant number of jump points, gaps in data and a small quantity of experimental data in the National Aeronautics and Space Administration (NASA) dataset. The predicted result was obtained with 8.52 % mean absolute error and 9.59 % root mean square error. When compared with the predicted results of different literatures, proposed multilayer perceptron with sliding window approach outperforms most of the existing approach. Incorporation of optimization techniques and hybrid algorithm in proposed approach can further enhance the accuracy of the model. 
},
 year = {2024}
}

Copy | Download

TY  - JOUR
T1  - Prediction of the Remaining Useful Life of Lithium-Ion Battery Using Multilayer Perceptron

AU  - Basanta Pancha
AU  - Sushil Paudel
AU  - Basanta Thapaliya
AU  - Tomasz Siewerski
AU  - Dayasagar Niraula
Y1  - 2024/10/10
PY  - 2024
N1  - https://doi.org/10.11648/j.ijecec.20241001.11
DO  - 10.11648/j.ijecec.20241001.11
T2  - International Journal of Electrical Components and Energy Conversion
JF  - International Journal of Electrical Components and Energy Conversion
JO  - International Journal of Electrical Components and Energy Conversion
SP  - 1
EP  - 17
PB  - Science Publishing Group
SN  - 2469-8059
UR  - https://doi.org/10.11648/j.ijecec.20241001.11
AB  - Cogitating the reliability of the supply and ensuring continuous delivery of power to the loads, especially in the growing demand for Lithium-Ion batteries in electric vehicle applications, prediction of the remaining useful life of Lithium-Ion batteries is crucial for the timely replacement. For prediction of non-linear and chaotic relationship, experience-based approach, physics-based approach and data driven approach are used among which data driven approach is a model free, accurate and reliable approach. Therefore, a driven approach in predicting remaining useful life can be implemented in the battery management system. This research uses a multilayer perceptron to predict the remaining useful life of the battery. The NASA Ames Prognostics Center of Excellence (PCoE) battery dataset is used to test the proposed methodology. The use of multilayer perceptron for remaining life prediction seems promising despite the significant number of jump points, gaps in data and a small quantity of experimental data in the National Aeronautics and Space Administration (NASA) dataset. The predicted result was obtained with 8.52 % mean absolute error and 9.59 % root mean square error. When compared with the predicted results of different literatures, proposed multilayer perceptron with sliding window approach outperforms most of the existing approach. Incorporation of optimization techniques and hybrid algorithm in proposed approach can further enhance the accuracy of the model. 

VL  - 10
IS  - 1
ER  -

Copy | Download