Khoa Tran, T. Nguyen-Thoi
Accurate state-of-charge (SOC) estimation is critical for the safe and efficient operation of lithium-ion batteries in battery management systems (BMS). Although data-driven approaches can effectively capture nonlinear battery dynamics, many existing methods rely on long historical input sequences, resulting in high computational cost and introducing padding-induced positional bias at the beginning of drive cycles. To address these limitations, we propose C2L-Net, a novel context-to-latest data-driven framework for realistic online SOC estimation using only a short historical window (20 s). Unlike existing short-receptive-field or long-history models, the proposed framework explicitly separates contextual encoding from latest-measurement updating, enabling both efficient temporal modeling and rapid adaptation to dynamic battery conditions. This enhances the accuracy and responsiveness of SOC estimation, making it particularly relevant for applications in real-time battery management systems.
@article{49c10a86-ae5b-46fb-8494-f6b0c0950dba,
title={C2L-Net: A Data-Driven Model for State-of-Charge Estimation of Lithium-Ion Batteries During Discharge},
author={Khoa Tran and T. Nguyen-Thoi},
year={2025},
language={English}
}TY - JOUR TI - C2L-Net: A Data-Driven Model for State-of-Charge Estimation of Lithium-Ion Batteries During Discharge AU - Khoa Tran AU - T. Nguyen-Thoi PY - 2025 LA - English ER -
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