Author
Listed:
- Xiankun Wei
(School of New Energy Vehicles, Chongqing Technology and Business Institute, Chongqing 401520, China)
- Silun Peng
(State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China)
- Mingli Mo
(School of New Energy Vehicles, Chongqing Technology and Business Institute, Chongqing 401520, China)
AbstractAccurate SOH prediction provides a reliable reference for lithium-ion battery maintenance. However, novel algorithms are still needed because few studies have considered the correlations between monitored parameters in Euclidean space and non-Euclidean space at different time points. To address this challenge, a novel gated-temporal network assisted by improved grasshopper optimization (IGOA-GGNN-TCN) is developed. In this model, features obtained from lithium-ion batteries are used to construct graph data based on cosine similarity. On this basis, the GGNN-TCN is employed to obtain the potential correlations between monitored parameters in Euclidean and non-Euclidean spaces. Furthermore, IGOA is introduced to overcome the issue of hyperparameter optimization for GGNN-TCN, improving the convergence speed and the local optimal problem. Competitive results on the Oxford dataset indicate that the SOH prediction performance of proposed IGOA-GGNN-TCN surpasses conventional methods, such as convolutional neural networks (CNNs) and gate recurrent unit (GRUs), achieving an R 2 value greater than 0.99. The experimental results demonstrate that the proposed IGOA-GGNN-TCN framework offers a novel and effective approach for state-of-health (SOH) estimation in lithium-ion batteries. By integrating improved grasshopper optimization (IGOA) with hybrid graph-temporal modeling, the method achieves superior prediction accuracy compared to conventional techniques, providing a promising tool for battery management systems in real-world applications.
Suggested Citation
Xiankun Wei & Silun Peng & Mingli Mo, 2025.
"State of Health Prediction for Lithium-Ion Batteries Based on Gated Temporal Network Assisted by Improved Grasshopper Optimization,"
Energies, MDPI, vol. 18(14), pages 1-17, July.
Handle:
RePEc:gam:jeners:v:18:y:2025:i:14:p:3856-:d:1705706
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