State of Health Estimation of Lithium-ion Batteries Based on Stacked-LSTM Transfer Learning with Bayesian Optimization and Multiple Features

• Published in: IEEE sensors journal p. 1
• Main Authors: Wei, Liangliang, Sun, Yiwen, Diao, Qi, Xu, Hongzhang, Tan, Xiaojun, Fan, Yuqian
• Format: Journal Article
• Language: English
• Published: IEEE 08.10.2024
• Subjects: Accuracy; Adaptation models; Batteries; Bayesian optimization; Data models; Estimation; Feature extraction; Lithium-ion batteries; Lithium-ion battery; Long short term memory; long short-term memory (LSTM); Modeling; multi-feature; state-of-health; Transfer learning
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2024.3472648

It is critical to accurately estimate the state of health (SOH) to ensure the safe and efficient operation of lithium-ion batteries. To reduce the training amounts of existing data-driven methods, transfer learning method has attracted more attention. However, most previous studies lack validation with different battery types and working conditions. Furthermore, the shared knowledge just relies on raw current and voltage data, resulting in insufficient accuracy. This paper proposes a stacked-LSTM transfer learning (TL) method based on Bayesian optimization (BO-Stacked-LSTM), which integrates multiple features to estimate SOH. By improving the structure of the BO-Stacked-LSTM networks and the fine-tuning strategy of transfer learning, as well as employing a Bayesian optimization algorithm to optimize hyperparameters, the proposed method can achieve accurate SOH estimation. Experimental results demonstrate that it just requires a small quantity of target dataset to accurately estimate SOH on the target dataset. Furthermore, experiments were performed on three different lithium-ion battery datasets, to validate the effectiveness.