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 |
Lithium-Ion Battery, Multilayer Perceptron (MLP), Charge-Discharge Cycle, Remaining Useful Life (RUL), Depth of Discharge (DOD)
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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
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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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}
}
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 -
Department of Electrical and Electronics Engineering, Nepal Engineering College, Changunarayan, Bhaktapur, Nepal
Research Training and Consultancy Division, Kantipur Engineering College, Dhapakhel, Lalitpur, Nepal
Department of Electrical Engineering, Khwopa College of Engineering, Libali, Bhaktapur, Nepal
Institute of Electrical Power Engineering, Lodz University of Technology, Lodz, Poland
Department of Electrical and Computer Engineering, Iowa State University, Ames, United States
