A Novel Sequence-to-Sequence Prediction Model for Lithium-Ion Battery Capacity Degradation Based on Improved Particle Swarm Optimization
The state of health (SOH) evaluation and remaining useful life (RUL) prediction for lithium-ion batteries (LIBs) are crucial for health management. This paper proposes a novel sequence-to-sequence (Seq2Seq) prediction method for LIB capacity degradation based on the gated recurrent unit (GRU) neural...
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| Format: | Article |
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MDPI AG
2023-08-01
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| Series: | Batteries |
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| Online Access: | https://www.mdpi.com/2313-0105/9/8/414 |
| _version_ | 1797585488949805056 |
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| author | Dinghong Chen Weige Zhang Caiping Zhang Bingxiang Sun Haoze Chen Sijia Yang Xinwei Cong |
| author_facet | Dinghong Chen Weige Zhang Caiping Zhang Bingxiang Sun Haoze Chen Sijia Yang Xinwei Cong |
| author_sort | Dinghong Chen |
| collection | DOAJ |
| description | The state of health (SOH) evaluation and remaining useful life (RUL) prediction for lithium-ion batteries (LIBs) are crucial for health management. This paper proposes a novel sequence-to-sequence (Seq2Seq) prediction method for LIB capacity degradation based on the gated recurrent unit (GRU) neural network with the attention mechanism. An improved particle swarm optimization (IPSO) algorithm is developed for automatic hyperparameter search of the Seq2Seq model, which speeds up parameter convergence and avoids getting stuck in local optima. Before model training, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm decomposes the capacity degradation sequences. And the intrinsic mode function (IMF) components with the highest correlation are employed to reconstruct the sequences, reducing the influence of noise in the original data. A real-cycle-life data set under fixed operating conditions is employed to validate the superiority and effectiveness of the method. The comparison results demonstrate that the proposed model outperforms traditional GRU and RNN models. The predicted mean absolute percent error (MAPE) in SOH evaluation and RUL prediction can be as low as 0.76% and 0.24%, respectively. |
| first_indexed | 2024-03-11T00:07:48Z |
| format | Article |
| id | doaj.art-bb7a0595850f4ba7a94cbf54301e1c84 |
| institution | Directory Open Access Journal |
| issn | 2313-0105 |
| language | English |
| last_indexed | 2024-03-11T00:07:48Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Batteries |
| spelling | doaj.art-bb7a0595850f4ba7a94cbf54301e1c842023-11-19T00:15:53ZengMDPI AGBatteries2313-01052023-08-019841410.3390/batteries9080414A Novel Sequence-to-Sequence Prediction Model for Lithium-Ion Battery Capacity Degradation Based on Improved Particle Swarm OptimizationDinghong Chen0Weige Zhang1Caiping Zhang2Bingxiang Sun3Haoze Chen4Sijia Yang5Xinwei Cong6National Active Distribution Network Technology Research Center (NANTEC), Beijing Jiaotong University, Beijing 100044, ChinaNational Active Distribution Network Technology Research Center (NANTEC), Beijing Jiaotong University, Beijing 100044, ChinaNational Active Distribution Network Technology Research Center (NANTEC), Beijing Jiaotong University, Beijing 100044, ChinaNational Active Distribution Network Technology Research Center (NANTEC), Beijing Jiaotong University, Beijing 100044, ChinaNational Active Distribution Network Technology Research Center (NANTEC), Beijing Jiaotong University, Beijing 100044, ChinaNational Active Distribution Network Technology Research Center (NANTEC), Beijing Jiaotong University, Beijing 100044, ChinaPower Distribution Technology Center, China Electric Power Research Institute, Beijing 100192, ChinaThe state of health (SOH) evaluation and remaining useful life (RUL) prediction for lithium-ion batteries (LIBs) are crucial for health management. This paper proposes a novel sequence-to-sequence (Seq2Seq) prediction method for LIB capacity degradation based on the gated recurrent unit (GRU) neural network with the attention mechanism. An improved particle swarm optimization (IPSO) algorithm is developed for automatic hyperparameter search of the Seq2Seq model, which speeds up parameter convergence and avoids getting stuck in local optima. Before model training, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm decomposes the capacity degradation sequences. And the intrinsic mode function (IMF) components with the highest correlation are employed to reconstruct the sequences, reducing the influence of noise in the original data. A real-cycle-life data set under fixed operating conditions is employed to validate the superiority and effectiveness of the method. The comparison results demonstrate that the proposed model outperforms traditional GRU and RNN models. The predicted mean absolute percent error (MAPE) in SOH evaluation and RUL prediction can be as low as 0.76% and 0.24%, respectively.https://www.mdpi.com/2313-0105/9/8/414lithium-ion batterylifetime predictiondeep learningsequence to sequence (Seq2Seq)gated recurrent unit (GRU)improved particle swarm optimization (IPSO) |
| spellingShingle | Dinghong Chen Weige Zhang Caiping Zhang Bingxiang Sun Haoze Chen Sijia Yang Xinwei Cong A Novel Sequence-to-Sequence Prediction Model for Lithium-Ion Battery Capacity Degradation Based on Improved Particle Swarm Optimization Batteries lithium-ion battery lifetime prediction deep learning sequence to sequence (Seq2Seq) gated recurrent unit (GRU) improved particle swarm optimization (IPSO) |
| title | A Novel Sequence-to-Sequence Prediction Model for Lithium-Ion Battery Capacity Degradation Based on Improved Particle Swarm Optimization |
| title_full | A Novel Sequence-to-Sequence Prediction Model for Lithium-Ion Battery Capacity Degradation Based on Improved Particle Swarm Optimization |
| title_fullStr | A Novel Sequence-to-Sequence Prediction Model for Lithium-Ion Battery Capacity Degradation Based on Improved Particle Swarm Optimization |
| title_full_unstemmed | A Novel Sequence-to-Sequence Prediction Model for Lithium-Ion Battery Capacity Degradation Based on Improved Particle Swarm Optimization |
| title_short | A Novel Sequence-to-Sequence Prediction Model for Lithium-Ion Battery Capacity Degradation Based on Improved Particle Swarm Optimization |
| title_sort | novel sequence to sequence prediction model for lithium ion battery capacity degradation based on improved particle swarm optimization |
| topic | lithium-ion battery lifetime prediction deep learning sequence to sequence (Seq2Seq) gated recurrent unit (GRU) improved particle swarm optimization (IPSO) |
| url | https://www.mdpi.com/2313-0105/9/8/414 |
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