CBATE-Net: An Accurate Battery Capacity and State-of-Health (SoH) Estimation Tool for Energy Storage Systems

Fazal Ur Rehman, Concettina Buccella and Carlo Cecati ()
Additional contact information
Fazal Ur Rehman: Department of Information Engineering Computer Science and Mathematics (DISIM), University of L’Aquila, 67100 L’Aquila, Italy
Concettina Buccella: Department of Information Engineering Computer Science and Mathematics (DISIM), University of L’Aquila, 67100 L’Aquila, Italy
Carlo Cecati: Department of Information Engineering Computer Science and Mathematics (DISIM), University of L’Aquila, 67100 L’Aquila, Italy

Energies, 2025, vol. 18, issue 20, 1-29

Abstract: In battery energy storage systems, accurately estimating battery capacity and state of health is crucial to ensure satisfactory operation and system efficiency and reliability. However, these tasks present particular challenges under irregular charge–discharge conditions, such as those encountered in renewable energy integration and electric vehicles, where heterogeneous cycling accelerates degradation. This study introduces a hybrid deep learning framework to address these challenges. It combines convolutional layers for localized feature extraction, bidirectional recurrent units for sequential learning and a temporal attention mechanism. The proposed hybrid deep learning model, termed CBATE-Net, uses ensemble averaging to improve stability and emphasizes degradation-critical intervals. The framework was evaluated using voltage, current and temperature signals from four benchmark lithium-ion cells across complete life cycles, as part of the NASA dataset. The results demonstrate that the proposed method can accurately track both smooth and abrupt capacity fade while maintaining stability near the end of the life cycle, an area in which conventional models often struggle. Integrating feature learning, temporal modelling and robustness enhancements in a unified design provides the framework with the ability to make accurate and interpretable predictions, making it suitable for deployment in real-world battery energy storage applications.

Keywords: battery capacity; state of health (SoH); remaining useful life (RUL); machine learning (ML); deep learning (DL); convolutional neural networks (CNNs); bidirectional long short-term memory (BiLSTM); temporal attention; ensemble learning; CNN-BiLSTM with Temporal Attention and Ensemble (CBATE-Net); battery energy storage systems (BESSs) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/18/20/5533/pdf (application/pdf)
https://www.mdpi.com/1996-1073/18/20/5533/ (text/html)

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:gam:jeners:v:18:y:2025:i:20:p:5533-:d:1775917

Access Statistics for this article

Energies is currently edited by Ms. Cassie Shen

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().