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| State-of-health estimation method for lithium-ion battery based on a coupled degradation model and CNN-GRU-AE fusion network |
| DOI：10.19783/j.cnki.pspc.251156 |
| Key Words:lithium-ion battery state-of-health estimation coupled degradation model hybrid neural network architecture |
| Author Name | Affiliation | | WANG Ziyi | 1. Engineering Research Center of Renewable Energy Power Generation and Grid-connected Control, Ministry of Education, Xinjiang University, Urumqi 830047, China
2. Xinjiang Branch of CGN New Energy Investment (Shenzhen) Co., Ltd., Urumqi 841100, China | | WU Jiahui | 1. Engineering Research Center of Renewable Energy Power Generation and Grid-connected Control, Ministry of Education, Xinjiang University, Urumqi 830047, China
2. Xinjiang Branch of CGN New Energy Investment (Shenzhen) Co., Ltd., Urumqi 841100, China | | WANG Weiqing | 1. Engineering Research Center of Renewable Energy Power Generation and Grid-connected Control, Ministry of Education, Xinjiang University, Urumqi 830047, China
2. Xinjiang Branch of CGN New Energy Investment (Shenzhen) Co., Ltd., Urumqi 841100, China | | DING Hongshuai | 1. Engineering Research Center of Renewable Energy Power Generation and Grid-connected Control, Ministry of Education, Xinjiang University, Urumqi 830047, China
2. Xinjiang Branch of CGN New Energy Investment (Shenzhen) Co., Ltd., Urumqi 841100, China | | ZHANG Hua | 1. Engineering Research Center of Renewable Energy Power Generation and Grid-connected Control, Ministry of Education, Xinjiang University, Urumqi 830047, China
2. Xinjiang Branch of CGN New Energy Investment (Shenzhen) Co., Ltd., Urumqi 841100, China | | YANG Jian | 1. Engineering Research Center of Renewable Energy Power Generation and Grid-connected Control, Ministry of Education, Xinjiang University, Urumqi 830047, China
2. Xinjiang Branch of CGN New Energy Investment (Shenzhen) Co., Ltd., Urumqi 841100, China |
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| Abstract:To address the limitation of existing data-driven models for battery state-of-health (SOH) estimation that neglect electrochemical mechanism constraints, a deep learning framework integrated with electrochemical mechanism constraints is proposed. First, a hybrid neural network architecture based on convolutional neural network-gated recurrent unit-autoencoder (CNN-GRU-AE) is designed to collaboratively extract temporal features from battery data. The CNN unit captures local degradation features to obtain feature vectors, while the GRU-AE unit models temporal dependencies and computes data reconstruction loss. To ensure consistency with electrochemical mechanisms during SOH estimation, a coupled degradation model is embedded into the framework. This model integrates a linear capacity degradation component, a nonlinear active lithium decay model, and a solid electrolyte interphase (SEI) growth mechanism. The entire framework is optimized via differentiable programming, enabling simultaneous learning of neural network weights and mechanistic parameters. Coupled with a dual-task learning architecture, it simultaneously realizes data reconstruction and lithium-ion battery SOH estimation. Finally, experimental results demonstrate that, compared to other models, the proposed model enhances both the accuracy and robustness of lithium-ion battery SOH estimation, achieving deep coupling between electrochemical mechanisms and data-driven modelling. |
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