Generalized real-time state of health estimation for lithium-ion batteries using simulation-augmented multi-objective dual-stream fusion of multi-Bi-LSTM-attention
To maintain the safe and reliable operation of lithium-ion batteries and manage their timely replacement, accurate state of health (SOH) estimation is critically important. This paper presents a novel deep-learning framework based on multi-loss optimized dual stream fusion of attention integrated mu...
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| Format: | Article |
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Elsevier
2025-03-01
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| Series: | e-Prime: Advances in Electrical Engineering, Electronics and Energy |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772671124004479 |
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| author | Jarin Tasnim Md. Azizur Rahman Md. Shoaib Akhter Rafi Muhammad Anisuzzaman Talukder Md. Kamrul Hasan |
| author_facet | Jarin Tasnim Md. Azizur Rahman Md. Shoaib Akhter Rafi Muhammad Anisuzzaman Talukder Md. Kamrul Hasan |
| author_sort | Jarin Tasnim |
| collection | DOAJ |
| description | To maintain the safe and reliable operation of lithium-ion batteries and manage their timely replacement, accurate state of health (SOH) estimation is critically important. This paper presents a novel deep-learning framework based on multi-loss optimized dual stream fusion of attention integrated multi-Bi-LSTM networks (multi-ABi-LSTM), for generalized real-time SOH estimation of lithium-ion batteries. Battery sensor data is first preprocessed utilizing novel energy discrepancy aware variable cycle length synchronization and grid encoding schemes to achieve generalizability considering battery sets with different discharge profiles and then passed through two parallel networks: overlapped data splitting (ODS)-based attention integrated multi-Bi-LSTM network (ODS-multi-ABi-LSTM) and past cycles’ SOHs (PCSs)-based attention integrated multi-Bi-LSTM (PCS-multi-ABi-LSTM) network. The complementary features extracted from these two networks are effectively combined by a proposed fusion network to achieve high SOH estimation accuracy. Furthermore, a lithium-ion battery simulation model is employed for data augmentation during training, enhancing the generalizability of the proposed data-driven model. The suggested technique outperforms previous methods by a remarkable margin achieving 0.716% MAPE, 0.005 MAE, 0.653% RMSE, and 0.992 R2 on a combined dataset consisting of four different battery sets with varying specifications and discharge profiles, indicating its generalization capability. Appliances using lithium-ion batteries can adopt the proposed SOH prediction framework to predict battery health conditions in real-time, ensuring operational safety and reliability. |
| format | Article |
| id | doaj-art-36619672d66847d08c86a6694f614eab |
| institution | Kabale University |
| issn | 2772-6711 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | e-Prime: Advances in Electrical Engineering, Electronics and Energy |
| spelling | doaj-art-36619672d66847d08c86a6694f614eab2024-12-13T11:08:42ZengElseviere-Prime: Advances in Electrical Engineering, Electronics and Energy2772-67112025-03-0111100870Generalized real-time state of health estimation for lithium-ion batteries using simulation-augmented multi-objective dual-stream fusion of multi-Bi-LSTM-attentionJarin Tasnim0Md. Azizur Rahman1Md. Shoaib Akhter Rafi2Muhammad Anisuzzaman Talukder3Md. Kamrul Hasan4Department of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology (BUET), Dhaka, 1205, BangladeshDepartment of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology (BUET), Dhaka, 1205, BangladeshDepartment of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology (BUET), Dhaka, 1205, BangladeshDepartment of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology (BUET), Dhaka, 1205, BangladeshCorresponding author.; Department of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology (BUET), Dhaka, 1205, BangladeshTo maintain the safe and reliable operation of lithium-ion batteries and manage their timely replacement, accurate state of health (SOH) estimation is critically important. This paper presents a novel deep-learning framework based on multi-loss optimized dual stream fusion of attention integrated multi-Bi-LSTM networks (multi-ABi-LSTM), for generalized real-time SOH estimation of lithium-ion batteries. Battery sensor data is first preprocessed utilizing novel energy discrepancy aware variable cycle length synchronization and grid encoding schemes to achieve generalizability considering battery sets with different discharge profiles and then passed through two parallel networks: overlapped data splitting (ODS)-based attention integrated multi-Bi-LSTM network (ODS-multi-ABi-LSTM) and past cycles’ SOHs (PCSs)-based attention integrated multi-Bi-LSTM (PCS-multi-ABi-LSTM) network. The complementary features extracted from these two networks are effectively combined by a proposed fusion network to achieve high SOH estimation accuracy. Furthermore, a lithium-ion battery simulation model is employed for data augmentation during training, enhancing the generalizability of the proposed data-driven model. The suggested technique outperforms previous methods by a remarkable margin achieving 0.716% MAPE, 0.005 MAE, 0.653% RMSE, and 0.992 R2 on a combined dataset consisting of four different battery sets with varying specifications and discharge profiles, indicating its generalization capability. Appliances using lithium-ion batteries can adopt the proposed SOH prediction framework to predict battery health conditions in real-time, ensuring operational safety and reliability.http://www.sciencedirect.com/science/article/pii/S2772671124004479Lithium-ion batteriesState of healthEnergy discrepancy aware preprocessingOverlapped data splittingSimulation modelAttention guided multi-Bi-LSTM |
| spellingShingle | Jarin Tasnim Md. Azizur Rahman Md. Shoaib Akhter Rafi Muhammad Anisuzzaman Talukder Md. Kamrul Hasan Generalized real-time state of health estimation for lithium-ion batteries using simulation-augmented multi-objective dual-stream fusion of multi-Bi-LSTM-attention e-Prime: Advances in Electrical Engineering, Electronics and Energy Lithium-ion batteries State of health Energy discrepancy aware preprocessing Overlapped data splitting Simulation model Attention guided multi-Bi-LSTM |
| title | Generalized real-time state of health estimation for lithium-ion batteries using simulation-augmented multi-objective dual-stream fusion of multi-Bi-LSTM-attention |
| title_full | Generalized real-time state of health estimation for lithium-ion batteries using simulation-augmented multi-objective dual-stream fusion of multi-Bi-LSTM-attention |
| title_fullStr | Generalized real-time state of health estimation for lithium-ion batteries using simulation-augmented multi-objective dual-stream fusion of multi-Bi-LSTM-attention |
| title_full_unstemmed | Generalized real-time state of health estimation for lithium-ion batteries using simulation-augmented multi-objective dual-stream fusion of multi-Bi-LSTM-attention |
| title_short | Generalized real-time state of health estimation for lithium-ion batteries using simulation-augmented multi-objective dual-stream fusion of multi-Bi-LSTM-attention |
| title_sort | generalized real time state of health estimation for lithium ion batteries using simulation augmented multi objective dual stream fusion of multi bi lstm attention |
| topic | Lithium-ion batteries State of health Energy discrepancy aware preprocessing Overlapped data splitting Simulation model Attention guided multi-Bi-LSTM |
| url | http://www.sciencedirect.com/science/article/pii/S2772671124004479 |
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