Prediction of Lithium-Ion Battery Health Using GRU-BPP
Accurate prediction of lithium-ion batteries’ (LIBs) state-of-health (SOH) is crucial for the safety and maintenance of LIB-powered systems. This study addresses the variability in degradation trajectories by applying gated recurrent unit (GRU) networks alongside principal component analysis (PCA),...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Batteries |
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| Online Access: | https://www.mdpi.com/2313-0105/10/11/399 |
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| author | Sahar Qaadan Aiman Alshare Alexander Popp Benedikt Schmuelling |
| author_facet | Sahar Qaadan Aiman Alshare Alexander Popp Benedikt Schmuelling |
| author_sort | Sahar Qaadan |
| collection | DOAJ |
| description | Accurate prediction of lithium-ion batteries’ (LIBs) state-of-health (SOH) is crucial for the safety and maintenance of LIB-powered systems. This study addresses the variability in degradation trajectories by applying gated recurrent unit (GRU) networks alongside principal component analysis (PCA), Granger causality, and K-means clustering to analyze the relationships between operating conditions—such as temperature and load profiles—and battery performance degradation. This paper uses a publicly accessible dataset derived by aging three prismatic LIB cells under a realistic forklift operation profile. First, we identify the features that are relevant to driving variance, then we employ the winning algorithm of K-means clustering for the classification of operational states. Granger causality later investigates the inter-group relationships. Our GRU-BPP model achieves an RMSE value of 0.167 and an MAE of 0.129 for the reference performance testing (RPT) dataset and an RMSE of 0.032 with an MAE of 0.025 for the aging dataset, thus outperformed benchmark methods such as GRU, LME, and XGBoost. These results further enhance the predictiveness and robustness of this approach and yield a holistic solution to the conventional challenges in battery management and their remaining useful life (RUL) predictions. |
| format | Article |
| id | doaj-art-ab44935927504f77b72440cbb9ef2aa4 |
| institution | OA Journals |
| issn | 2313-0105 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Batteries |
| spelling | doaj-art-ab44935927504f77b72440cbb9ef2aa42025-08-20T02:28:10ZengMDPI AGBatteries2313-01052024-11-01101139910.3390/batteries10110399Prediction of Lithium-Ion Battery Health Using GRU-BPPSahar Qaadan0Aiman Alshare1Alexander Popp2Benedikt Schmuelling3Mechatronics Engineering, German Jordanian University, Madaba Street, Amman 11180, JordanMechanical and Maintenance Engineering, German Jordanian University, Madaba Street, Amman 11180, JordanInstitute of Electric Mobility and Energy Storage Systems, University of Wuppertal, Rainer-Gruenter-Str. 21, 42119 Wuppertal, GermanyInstitute of Electric Mobility and Energy Storage Systems, University of Wuppertal, Rainer-Gruenter-Str. 21, 42119 Wuppertal, GermanyAccurate prediction of lithium-ion batteries’ (LIBs) state-of-health (SOH) is crucial for the safety and maintenance of LIB-powered systems. This study addresses the variability in degradation trajectories by applying gated recurrent unit (GRU) networks alongside principal component analysis (PCA), Granger causality, and K-means clustering to analyze the relationships between operating conditions—such as temperature and load profiles—and battery performance degradation. This paper uses a publicly accessible dataset derived by aging three prismatic LIB cells under a realistic forklift operation profile. First, we identify the features that are relevant to driving variance, then we employ the winning algorithm of K-means clustering for the classification of operational states. Granger causality later investigates the inter-group relationships. Our GRU-BPP model achieves an RMSE value of 0.167 and an MAE of 0.129 for the reference performance testing (RPT) dataset and an RMSE of 0.032 with an MAE of 0.025 for the aging dataset, thus outperformed benchmark methods such as GRU, LME, and XGBoost. These results further enhance the predictiveness and robustness of this approach and yield a holistic solution to the conventional challenges in battery management and their remaining useful life (RUL) predictions.https://www.mdpi.com/2313-0105/10/11/399model performance testgated recurrent unitstate of healthGranger causality analysisclusteringprincipal component analysis |
| spellingShingle | Sahar Qaadan Aiman Alshare Alexander Popp Benedikt Schmuelling Prediction of Lithium-Ion Battery Health Using GRU-BPP Batteries model performance test gated recurrent unit state of health Granger causality analysis clustering principal component analysis |
| title | Prediction of Lithium-Ion Battery Health Using GRU-BPP |
| title_full | Prediction of Lithium-Ion Battery Health Using GRU-BPP |
| title_fullStr | Prediction of Lithium-Ion Battery Health Using GRU-BPP |
| title_full_unstemmed | Prediction of Lithium-Ion Battery Health Using GRU-BPP |
| title_short | Prediction of Lithium-Ion Battery Health Using GRU-BPP |
| title_sort | prediction of lithium ion battery health using gru bpp |
| topic | model performance test gated recurrent unit state of health Granger causality analysis clustering principal component analysis |
| url | https://www.mdpi.com/2313-0105/10/11/399 |
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