A Self-Adaptive Physics-Informed Gated Recurrent Unit Neural Networks Model for Estimating the Lifetime of Li-ion Batteries
IEEE Transactions on Transportation Electrification, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Basım Tarihi: 2026
- Doi Numarası: 10.1109/tte.2026.3703913
- Dergi Adı: IEEE Transactions on Transportation Electrification
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
- Anahtar Kelimeler: Attention mechanism, battery management systems, gated recurrent unit, Li-ion batteries, physics-informed neural networks
- Gazi Üniversitesi Adresli: Evet
Özet
Physics-Informed Neural Networks have recently emerged as a promising approach for applying deep neural networks to solve partial differential equations. However, accurately addressing challenging regions in the solutions of stiff partial differential equations necessitates adaptive methods. Additionally, the inherent limitations of baseline Physics-Informed Neural Networks in handling sequential or time-series data significantly constrain their applicability. In light of this, this paper introduces a Self-Adaptive Physics-Informed Attention-based Gated Recurrent Unit model, which enhances the baseline Physics-Informed Neural Networks framework to address these critical issues. The proposed model represents a significant advancement in Physics-Informed Neural Networks by integrating an attention-based Gated Recurrent Unit layer, thereby enabling more effective modeling of sequential data than existing physics-informed models. Moreover, this paper jointly minimizes the prediction error between the estimated and measured outputs (data-fit loss) and the residual associated with the governing degradation physics (physics-residual loss), while learning adaptive weights to balance both objectives during training. The efficacy of the proposed model is demonstrated through an essential case study, which is to predict the state of health of lithium-ion batteries, utilizing four battery datasets from different sources, namely the NASA, MIT, XJTU, and HUST battery datasets. The proposed model is compared with the baseline Physics-Informed Neural Networks and other benchmark models to demonstrate improvements in predictive accuracy and network initialization capabilities.