Enhanced early prediction of Li-ion battery degradation using multicycle features and an ensemble deep learning model
Achieving high accuracy in the early prediction of Li-ion battery degradation is challenging owing to the nonlinear and dynamic nature of battery aging. This study introduces a GRU-LSTM ensemble model that combines Gated Recurrent Units (GRU) and Long Short-Term Memory (LSTM) networks to forecast th...
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| Language: | English |
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Elsevier
2025-03-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025003214 |
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| author | Meilia Safitri Teguh Bharata Adji Adha Imam Cahyadi |
| author_facet | Meilia Safitri Teguh Bharata Adji Adha Imam Cahyadi |
| author_sort | Meilia Safitri |
| collection | DOAJ |
| description | Achieving high accuracy in the early prediction of Li-ion battery degradation is challenging owing to the nonlinear and dynamic nature of battery aging. This study introduces a GRU-LSTM ensemble model that combines Gated Recurrent Units (GRU) and Long Short-Term Memory (LSTM) networks to forecast the end-of-life (EoL) of LIBs. The model utilizes features extracted from the voltage and current data during the initial 100 battery cycles and employs a multicycle feature extraction method to enhance computational efficiency without sacrificing predictive accuracy. Hyperparameter tuning via random search and k-fold cross-validation were applied to ensure model robustness. The GRU-LSTM model outperformed the standalone LSTM, GRU, and BiLSTM models across the three input scenarios, achieving the lowest mean absolute percentage error (MAPE) of 5.12 % and root mean squared error (RMSE) of 64.98 cycles using the combined voltage and current features. These results demonstrate the potential of ensemble models to enhance battery health monitoring and predictive maintenance systems. |
| format | Article |
| id | doaj-art-2b72ef57b05c4a9d966ecd2ddbac1053 |
| institution | Kabale University |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-2b72ef57b05c4a9d966ecd2ddbac10532025-02-06T05:12:43ZengElsevierResults in Engineering2590-12302025-03-0125104235Enhanced early prediction of Li-ion battery degradation using multicycle features and an ensemble deep learning modelMeilia Safitri0Teguh Bharata Adji1Adha Imam Cahyadi2Department of Electrical and Information Engineering, Universitas Gadjah Mada, Yogyakarta, 55281, Indonesia; Department of Medical Electronics Technology, Universitas Muhammadiyah Yogyakarta, Yogyakarta, 55183, IndonesiaDepartment of Electrical and Information Engineering, Universitas Gadjah Mada, Yogyakarta, 55281, Indonesia; Corresponding author.Department of Electrical and Information Engineering, Universitas Gadjah Mada, Yogyakarta, 55281, IndonesiaAchieving high accuracy in the early prediction of Li-ion battery degradation is challenging owing to the nonlinear and dynamic nature of battery aging. This study introduces a GRU-LSTM ensemble model that combines Gated Recurrent Units (GRU) and Long Short-Term Memory (LSTM) networks to forecast the end-of-life (EoL) of LIBs. The model utilizes features extracted from the voltage and current data during the initial 100 battery cycles and employs a multicycle feature extraction method to enhance computational efficiency without sacrificing predictive accuracy. Hyperparameter tuning via random search and k-fold cross-validation were applied to ensure model robustness. The GRU-LSTM model outperformed the standalone LSTM, GRU, and BiLSTM models across the three input scenarios, achieving the lowest mean absolute percentage error (MAPE) of 5.12 % and root mean squared error (RMSE) of 64.98 cycles using the combined voltage and current features. These results demonstrate the potential of ensemble models to enhance battery health monitoring and predictive maintenance systems.http://www.sciencedirect.com/science/article/pii/S2590123025003214GRU-LSTM ensembleLithium-ion battery degradationEarly predictionFeature-based machine learningBattery management system (BMS) |
| spellingShingle | Meilia Safitri Teguh Bharata Adji Adha Imam Cahyadi Enhanced early prediction of Li-ion battery degradation using multicycle features and an ensemble deep learning model Results in Engineering GRU-LSTM ensemble Lithium-ion battery degradation Early prediction Feature-based machine learning Battery management system (BMS) |
| title | Enhanced early prediction of Li-ion battery degradation using multicycle features and an ensemble deep learning model |
| title_full | Enhanced early prediction of Li-ion battery degradation using multicycle features and an ensemble deep learning model |
| title_fullStr | Enhanced early prediction of Li-ion battery degradation using multicycle features and an ensemble deep learning model |
| title_full_unstemmed | Enhanced early prediction of Li-ion battery degradation using multicycle features and an ensemble deep learning model |
| title_short | Enhanced early prediction of Li-ion battery degradation using multicycle features and an ensemble deep learning model |
| title_sort | enhanced early prediction of li ion battery degradation using multicycle features and an ensemble deep learning model |
| topic | GRU-LSTM ensemble Lithium-ion battery degradation Early prediction Feature-based machine learning Battery management system (BMS) |
| url | http://www.sciencedirect.com/science/article/pii/S2590123025003214 |
| work_keys_str_mv | AT meiliasafitri enhancedearlypredictionofliionbatterydegradationusingmulticyclefeaturesandanensembledeeplearningmodel AT teguhbharataadji enhancedearlypredictionofliionbatterydegradationusingmulticyclefeaturesandanensembledeeplearningmodel AT adhaimamcahyadi enhancedearlypredictionofliionbatterydegradationusingmulticyclefeaturesandanensembledeeplearningmodel |