Cover Image

Battery remaining useful life estimation based on particle swarm optimization-neural network

Determining the Remaining Useful Life (RUL) of a battery is essential for several purposes, including proactive maintenance planning, optimizing resource allocation, preventing unforeseen failures, improving safety, extending battery lifespan, and achieving accurate cost savings. Concerning that mat...

Full description

Saved in:
Bibliographic Details
Main Authors: Zuriani, Mustaffa, Mohd Herwan, Sulaiman
Format: Article
Language:English
Published: Elsevier B.V. 2024
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://umpir.ump.edu.my/id/eprint/42916/1/Battery%20remaining%20useful%20life%20estimation%20based%20on%20particle%20swarm%20optimization-neural%20network.pdf
http://umpir.ump.edu.my/id/eprint/42916/
https://doi.org/10.1016/j.cles.2024.100151
https://doi.org/10.1016/j.cles.2024.100151
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.ump.umpir.42916
record_format eprints
spelling my.ump.umpir.429162025-01-16T03:24:08Z http://umpir.ump.edu.my/id/eprint/42916/ Battery remaining useful life estimation based on particle swarm optimization-neural network Zuriani, Mustaffa Mohd Herwan, Sulaiman TK Electrical engineering. Electronics Nuclear engineering Determining the Remaining Useful Life (RUL) of a battery is essential for several purposes, including proactive maintenance planning, optimizing resource allocation, preventing unforeseen failures, improving safety, extending battery lifespan, and achieving accurate cost savings. Concerning that matter, this study proposed hybrid Particle Swarm Optimization–Neural Network (PSO NN) for estimating battery RUL. In the evaluation of the proposed method, the effectiveness is assessed using the metrics of Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The dataset employed for this investigation comprises eight input parameters and one output variable, representing the battery RUL. In conducting an analysis, the performance of the PSO NN model is compared with hybrid NN with Cultural Algorithm (CA-NN) and Harmony Search Algorithm (HSA-NN), as well as the standalone Autoregressive Integrated Moving Average (ARIMA). Upon examination of the findings, it becomes evident that the PSO NN model outperforms the alternatives with an MAE of 2.7708 and an RMSE of 4.3468, significantly lower than HSA-NN (MAE: 22.0583, RMSE: 34.5154), CA-NN (MAE: 9.1189, RMSE: 22.4646), and ARIMA (MAE: 494.6275, RMSE: 584.3098). The PSO NN also achieves the lowest maximum error of 104.7381 compared to 490.3125 for HSA-NN, 827.0163 for CA-NN, and 1,160.0000 for ARIMA. Additionally, the low two-tail probability values (P(T ≤ t)), all below the significance level of 0.05, indicate that the differences between PSO NN and the other methods (HSA-NN, CA-NN, and ARIMA) are statistically significant. These results highlight the superior accuracy and robustness of the PSO NN model in predicting battery RUL. This study contributes to the field by presenting the PSO NN as a highly effective tool for accurate battery RUL estimation, as evidenced by its superior performance over alternative methods. Elsevier B.V. 2024-12 Article PeerReviewed pdf en cc_by_nc_4 http://umpir.ump.edu.my/id/eprint/42916/1/Battery%20remaining%20useful%20life%20estimation%20based%20on%20particle%20swarm%20optimization-neural%20network.pdf Zuriani, Mustaffa and Mohd Herwan, Sulaiman (2024) Battery remaining useful life estimation based on particle swarm optimization-neural network. Cleaner Energy Systems, 9 (100151). pp. 1-9. ISSN 2772-7831. (Published) https://doi.org/10.1016/j.cles.2024.100151 https://doi.org/10.1016/j.cles.2024.100151
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Zuriani, Mustaffa
Mohd Herwan, Sulaiman
Battery remaining useful life estimation based on particle swarm optimization-neural network
description Determining the Remaining Useful Life (RUL) of a battery is essential for several purposes, including proactive maintenance planning, optimizing resource allocation, preventing unforeseen failures, improving safety, extending battery lifespan, and achieving accurate cost savings. Concerning that matter, this study proposed hybrid Particle Swarm Optimization–Neural Network (PSO NN) for estimating battery RUL. In the evaluation of the proposed method, the effectiveness is assessed using the metrics of Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The dataset employed for this investigation comprises eight input parameters and one output variable, representing the battery RUL. In conducting an analysis, the performance of the PSO NN model is compared with hybrid NN with Cultural Algorithm (CA-NN) and Harmony Search Algorithm (HSA-NN), as well as the standalone Autoregressive Integrated Moving Average (ARIMA). Upon examination of the findings, it becomes evident that the PSO NN model outperforms the alternatives with an MAE of 2.7708 and an RMSE of 4.3468, significantly lower than HSA-NN (MAE: 22.0583, RMSE: 34.5154), CA-NN (MAE: 9.1189, RMSE: 22.4646), and ARIMA (MAE: 494.6275, RMSE: 584.3098). The PSO NN also achieves the lowest maximum error of 104.7381 compared to 490.3125 for HSA-NN, 827.0163 for CA-NN, and 1,160.0000 for ARIMA. Additionally, the low two-tail probability values (P(T ≤ t)), all below the significance level of 0.05, indicate that the differences between PSO NN and the other methods (HSA-NN, CA-NN, and ARIMA) are statistically significant. These results highlight the superior accuracy and robustness of the PSO NN model in predicting battery RUL. This study contributes to the field by presenting the PSO NN as a highly effective tool for accurate battery RUL estimation, as evidenced by its superior performance over alternative methods.
format Article
author Zuriani, Mustaffa
Mohd Herwan, Sulaiman
author_facet Zuriani, Mustaffa
Mohd Herwan, Sulaiman
author_sort Zuriani, Mustaffa
title Battery remaining useful life estimation based on particle swarm optimization-neural network
title_short Battery remaining useful life estimation based on particle swarm optimization-neural network
title_full Battery remaining useful life estimation based on particle swarm optimization-neural network
title_fullStr Battery remaining useful life estimation based on particle swarm optimization-neural network
title_full_unstemmed Battery remaining useful life estimation based on particle swarm optimization-neural network
title_sort battery remaining useful life estimation based on particle swarm optimization-neural network
publisher Elsevier B.V.
publishDate 2024
url http://umpir.ump.edu.my/id/eprint/42916/1/Battery%20remaining%20useful%20life%20estimation%20based%20on%20particle%20swarm%20optimization-neural%20network.pdf
http://umpir.ump.edu.my/id/eprint/42916/
https://doi.org/10.1016/j.cles.2024.100151
https://doi.org/10.1016/j.cles.2024.100151
_version_ 1822924943194587136
score 13.232414

Similar Items