Hybrid firefly algorithm–neural network for battery remaining useful life estimation
Accurately estimating the remaining useful life (RUL) of batteries is crucial for optimizing maintenance, preventing failures, and enhancing reliability, thereby saving costs and resources. This study introduces a hybrid approach for estimating the RUL of a battery based on the firefly algorithm–neu...
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| Format: | Article |
| Language: | English |
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Oxford University Press
2024
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| Online Access: | http://umpir.ump.edu.my/id/eprint/44151/ http://umpir.ump.edu.my/id/eprint/44151/1/Hybrid%20firefly%20algorithm%E2%80%93neural%20network%20for%20battery.pdf |
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| author | Zuriani, Mustaffa Mohd Herwan, Sulaiman |
| author_facet | Zuriani, Mustaffa Mohd Herwan, Sulaiman |
| author_sort | Zuriani, Mustaffa |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Accurately estimating the remaining useful life (RUL) of batteries is crucial for optimizing maintenance, preventing failures, and enhancing reliability, thereby saving costs and resources. This study introduces a hybrid approach for estimating the RUL of a battery based on the firefly algorithm–neural network (FA–NN) model, in which the FA is employed as an optimizer to fine-tune the network weights and hidden layer biases in the NN. The performance of the FA–NN is comprehensively compared against two hybrid models, namely the harmony search algorithm (HSA)–NN and cultural algorithm (CA)–NN, as well as a single model, namely the autoregressive integrated moving average (ARIMA). The comparative analysis is based mean absolute error (MAE) and root mean squared error (RMSE). Findings reveal that the FA–NN outperforms the HSA–NN, CA–NN, and ARIMA in both employed metrics, demonstrating superior predictive capabilities for estimating the RUL of a battery. Specifically, the FA–NN achieved a MAE of 2.5371 and a RMSE of 2.9488 compared with the HSA–NN with a MAE of 22.0583 and RMSE of 34.5154, the CA–NN with a MAE of 9.1189 and RMSE of 22.4646, and the ARIMA with a MAE of 494.6275 and RMSE of 584.3098. Additionally, the FA–NN exhibits significantly smaller maximum errors at 34.3737 compared with the HSA–NN at 490.3125, the CA–NN at 827.0163, and the ARIMA at 1.16e + 03, further emphasizing its robust performance in minimizing prediction inaccuracies. This study offers important insights into battery health management, showing that the proposed method is a promising solution for precise RUL predictions |
| first_indexed | 2025-11-15T03:54:27Z |
| format | Article |
| id | ump-44151 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:54:27Z |
| publishDate | 2024 |
| publisher | Oxford University Press |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-441512025-06-24T04:30:14Z http://umpir.ump.edu.my/id/eprint/44151/ Hybrid firefly algorithm–neural network for battery remaining useful life estimation Zuriani, Mustaffa Mohd Herwan, Sulaiman QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering Accurately estimating the remaining useful life (RUL) of batteries is crucial for optimizing maintenance, preventing failures, and enhancing reliability, thereby saving costs and resources. This study introduces a hybrid approach for estimating the RUL of a battery based on the firefly algorithm–neural network (FA–NN) model, in which the FA is employed as an optimizer to fine-tune the network weights and hidden layer biases in the NN. The performance of the FA–NN is comprehensively compared against two hybrid models, namely the harmony search algorithm (HSA)–NN and cultural algorithm (CA)–NN, as well as a single model, namely the autoregressive integrated moving average (ARIMA). The comparative analysis is based mean absolute error (MAE) and root mean squared error (RMSE). Findings reveal that the FA–NN outperforms the HSA–NN, CA–NN, and ARIMA in both employed metrics, demonstrating superior predictive capabilities for estimating the RUL of a battery. Specifically, the FA–NN achieved a MAE of 2.5371 and a RMSE of 2.9488 compared with the HSA–NN with a MAE of 22.0583 and RMSE of 34.5154, the CA–NN with a MAE of 9.1189 and RMSE of 22.4646, and the ARIMA with a MAE of 494.6275 and RMSE of 584.3098. Additionally, the FA–NN exhibits significantly smaller maximum errors at 34.3737 compared with the HSA–NN at 490.3125, the CA–NN at 827.0163, and the ARIMA at 1.16e + 03, further emphasizing its robust performance in minimizing prediction inaccuracies. This study offers important insights into battery health management, showing that the proposed method is a promising solution for precise RUL predictions Oxford University Press 2024-10-01 Article PeerReviewed pdf en cc_by_nc_4 http://umpir.ump.edu.my/id/eprint/44151/1/Hybrid%20firefly%20algorithm%E2%80%93neural%20network%20for%20battery.pdf Zuriani, Mustaffa and Mohd Herwan, Sulaiman (2024) Hybrid firefly algorithm–neural network for battery remaining useful life estimation. Clean Energy, 8 (5). pp. 157-166. ISSN 2515-4230. (Published) https://doi.org/10.1093/ce/zkae060 https://doi.org/10.1093/ce/zkae060 |
| spellingShingle | QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering Zuriani, Mustaffa Mohd Herwan, Sulaiman Hybrid firefly algorithm–neural network for battery remaining useful life estimation |
| title | Hybrid firefly algorithm–neural network for battery remaining useful life estimation |
| title_full | Hybrid firefly algorithm–neural network for battery remaining useful life estimation |
| title_fullStr | Hybrid firefly algorithm–neural network for battery remaining useful life estimation |
| title_full_unstemmed | Hybrid firefly algorithm–neural network for battery remaining useful life estimation |
| title_short | Hybrid firefly algorithm–neural network for battery remaining useful life estimation |
| title_sort | hybrid firefly algorithm–neural network for battery remaining useful life estimation |
| topic | QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/44151/ http://umpir.ump.edu.my/id/eprint/44151/ http://umpir.ump.edu.my/id/eprint/44151/ http://umpir.ump.edu.my/id/eprint/44151/1/Hybrid%20firefly%20algorithm%E2%80%93neural%20network%20for%20battery.pdf |