Battery SOH Estimation Based on Dual-View Voltage Signal Features and Enhanced LSTM
Accurate assessment of the state of health (SOH) of lithium-ion batteries (LIBs) is fundamental to ensuring safe operation. However, due to the complex electrochemical processes during battery operation and the limited availability of training data, accurate estimation of the state of health remains...
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MDPI AG
2025-07-01
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| Online Access: | https://www.mdpi.com/1996-1073/18/15/4016 |
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| author | Shunchang Wang Yaolong He Hongjiu Hu |
| author_facet | Shunchang Wang Yaolong He Hongjiu Hu |
| author_sort | Shunchang Wang |
| collection | DOAJ |
| description | Accurate assessment of the state of health (SOH) of lithium-ion batteries (LIBs) is fundamental to ensuring safe operation. However, due to the complex electrochemical processes during battery operation and the limited availability of training data, accurate estimation of the state of health remains challenging. To address this, this paper proposes a prediction framework based on dual-view voltage signal features and an improved Long Short-Term Memory (LSTM) neural network. By relying solely on readily obtainable voltage signals, the data requirement is greatly reduced; dual-view features, comprising kinetic and aggregated aspects, are extracted based on the underlying reaction mechanisms. To fully leverage the extracted feature information, Scaled Dot-Product Attention (SDPA) is employed to dynamically score all hidden states of the long short-term memory network, adaptively capturing key temporal information. The experimental results based on the NASA PCoE battery dataset indicate that, under various operating conditions, the proposed method achieves an average absolute error below 0.51% and a root mean square error not exceeding 0.58% in state-of-health estimation, demonstrating high predictive accuracy. |
| format | Article |
| id | doaj-art-336a216e1183409ebec280ea3708dfd4 |
| institution | Kabale University |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-336a216e1183409ebec280ea3708dfd42025-08-20T03:36:41ZengMDPI AGEnergies1996-10732025-07-011815401610.3390/en18154016Battery SOH Estimation Based on Dual-View Voltage Signal Features and Enhanced LSTMShunchang Wang0Yaolong He1Hongjiu Hu2Shanghai Institute of Applied Mathematics and Mechanics, School of Mechanics and Engineering Science, Shanghai University, Shanghai 200072, ChinaShanghai Frontier Science Center of Mechanoinformatics, Shanghai 200072, ChinaShanghai Institute of Applied Mathematics and Mechanics, School of Mechanics and Engineering Science, Shanghai University, Shanghai 200072, ChinaAccurate assessment of the state of health (SOH) of lithium-ion batteries (LIBs) is fundamental to ensuring safe operation. However, due to the complex electrochemical processes during battery operation and the limited availability of training data, accurate estimation of the state of health remains challenging. To address this, this paper proposes a prediction framework based on dual-view voltage signal features and an improved Long Short-Term Memory (LSTM) neural network. By relying solely on readily obtainable voltage signals, the data requirement is greatly reduced; dual-view features, comprising kinetic and aggregated aspects, are extracted based on the underlying reaction mechanisms. To fully leverage the extracted feature information, Scaled Dot-Product Attention (SDPA) is employed to dynamically score all hidden states of the long short-term memory network, adaptively capturing key temporal information. The experimental results based on the NASA PCoE battery dataset indicate that, under various operating conditions, the proposed method achieves an average absolute error below 0.51% and a root mean square error not exceeding 0.58% in state-of-health estimation, demonstrating high predictive accuracy.https://www.mdpi.com/1996-1073/18/15/4016lithium-ion batterystate of healthvoltage signaldual-viewLSTM |
| spellingShingle | Shunchang Wang Yaolong He Hongjiu Hu Battery SOH Estimation Based on Dual-View Voltage Signal Features and Enhanced LSTM Energies lithium-ion battery state of health voltage signal dual-view LSTM |
| title | Battery SOH Estimation Based on Dual-View Voltage Signal Features and Enhanced LSTM |
| title_full | Battery SOH Estimation Based on Dual-View Voltage Signal Features and Enhanced LSTM |
| title_fullStr | Battery SOH Estimation Based on Dual-View Voltage Signal Features and Enhanced LSTM |
| title_full_unstemmed | Battery SOH Estimation Based on Dual-View Voltage Signal Features and Enhanced LSTM |
| title_short | Battery SOH Estimation Based on Dual-View Voltage Signal Features and Enhanced LSTM |
| title_sort | battery soh estimation based on dual view voltage signal features and enhanced lstm |
| topic | lithium-ion battery state of health voltage signal dual-view LSTM |
| url | https://www.mdpi.com/1996-1073/18/15/4016 |
| work_keys_str_mv | AT shunchangwang batterysohestimationbasedondualviewvoltagesignalfeaturesandenhancedlstm AT yaolonghe batterysohestimationbasedondualviewvoltagesignalfeaturesandenhancedlstm AT hongjiuhu batterysohestimationbasedondualviewvoltagesignalfeaturesandenhancedlstm |