C2L-Net: A Data-Driven Model for State-of-Charge Estimation of Lithium-Ion Batteries During Discharge

2026-05-08

AI Summary

This work addresses the high computational cost and initial padding bias inherent in existing data-driven state-of-charge (SOC) estimation methods, which typically rely on long historical sequences. To overcome these limitations, the authors propose C2L-Net, a novel framework that decouples contextual modeling from recent observations. By leveraging an ultra-short window of merely 20 seconds, C2L-Net efficiently encodes historical context through chunked feature extraction, Theta Attention Pooling, Fourier seasonal bases, and a causal cosine attention mechanism, while a recursive filtering-style decoder fuses the latest measurements. Evaluated on multiple public lithium-ion battery datasets, the method achieves state-of-the-art or competitive accuracy, reduces model parameters substantially, accelerates inference speed by up to 60×, and demonstrates strong robustness under unseen driving conditions.

Abstract

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 states. The proposed model incorporates a chunk-based feature extraction mechanism that combines Theta Attention Pooling with a Fourier-based Seasonality Basis to capture local temporal patterns while reducing sequence length. A causal context encoder, integrating a gated recurrent unit (GRU) with Causal Cosine Attention, models temporal dependencies without information leakage. Furthermore, a latest-measurement decoder, inspired by recursive filtering, updates the contextual state using the most recent measurement, enhancing responsiveness to dynamic operating conditions. Extensive experiments on a public lithium-ion battery drive-cycle dataset under multiple fixed-temperature conditions demonstrate that the proposed method achieves state-of-the-art or competitive accuracy while significantly improving computational efficiency. In particular, C2L-Net achieves up to 60 times faster inference and requires fewer parameters than recent data-driven baselines, while maintaining robust performance across unseen driving profiles.

Problem

• state-of-charge estimation
• lithium-ion batteries
• data-driven models
• computational efficiency
• online estimation

Innovation

• C2L-Net
• State-of-Charge estimation
• Short historical window
• Causal attention
• Data-driven battery modeling