Cross-domain battery SOH and RUL estimation via Domain-Adaptive Transformer

Bo Zhu, Li Jia, Quanke Pan and Hui Zhang

Energy, 2025, vol. 341, issue C

Abstract: Accurate estimation of the state of health (SOH) and remaining useful life (RUL) of lithium-ion batteries across heterogeneous chemistries and cycling protocols remains challenging because of significant domain shifts and complex degradation dynamics. To overcome these issues, this study proposes a Domain-Adaptive Transformer (DAT) framework for cross-domain battery health prognostics. The framework integrates a protocol-independent feature representation—constructed from voltage (V), capacity (Q), and their differential features (ΔV, ΔQ)—with a hybrid CNN–Transformer backbone enhanced by rotary positional embedding and a lightweight Extreme Learning Machine (ELM) module. This design enables efficient modeling of long-range temporal dependencies, rapid fine-tuning on new domains, and high computational efficiency. Comprehensive experiments on three transfer tasks (cross-discharge-protocol, cross-charge-protocol, and cross-chemistry) demonstrate that the proposed model consistently outperforms state-of-the-art baselines including BiLSTM, TCN, PINN, and SDE-BiLSTM. After fine-tuning, our model achieves RUL RMSE = 178 cycles (R2 = 0.853) and SOH MAE = 0.138% (R2 = 0.999), confirming its robustness and adaptability across domains. These results highlight the potential of the framework for generalizable, data-efficient, and practical battery health prognostics in real-world applications.

Keywords: Domain adaption; Transformer; Battery health prognostics; Remaining useful life; State of health (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544225049308
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:341:y:2025:i:c:s0360544225049308

DOI: 10.1016/j.energy.2025.139288

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().