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

Researchers propose C2L-Net, a data-driven neural network architecture that improves state-of-charge (SOC) estimation for lithium-ion batteries using only 20-second historical windows. The model achieves up to 60x faster inference than existing methods while maintaining competitive accuracy, addressing computational inefficiency and positional bias problems in battery management systems.

C2L-Net represents a meaningful advancement in battery management technology by tackling a fundamental tradeoff in machine learning: accuracy versus computational efficiency. Traditional SOC estimation models require lengthy historical sequences to capture battery dynamics accurately, creating latency and processing overhead problematic for real-time BMS applications. This research resolves that constraint through architectural innovation, separating contextual encoding from real-time measurement updates in a way that mirrors classical recursive filtering approaches.

The technical contribution combines several modern deep learning techniques—Theta Attention Pooling, Fourier-based seasonality detection, and Causal Cosine Attention—to compress temporal information efficiently. By reducing sequence length through chunk-based feature extraction, the model maintains responsiveness to dynamic operating conditions while dramatically lowering computational demands. The 60x inference speedup matters substantially for edge deployment in vehicles and energy storage systems where processing power is limited.

This advancement has indirect but significant implications for the broader electric vehicle and renewable energy sectors. Efficient, accurate SOC estimation directly impacts battery longevity, charging optimization, and vehicle range prediction—consumer-facing features that influence EV adoption rates. Battery management remains a bottleneck in widespread electrification, and algorithmic improvements that reduce both computational overhead and model complexity strengthen the economic case for sophisticated BMS implementations.

Future development trajectories should focus on temperature-varying conditions beyond the fixed-temperature testing environment and validation across different battery chemistries. Real-world deployment will reveal whether the model's efficiency gains translate to meaningful cost reductions in automotive and stationary storage applications.

• →C2L-Net achieves 60x faster inference speed than recent baselines while maintaining state-of-the-art SOC estimation accuracy
• →The architecture separates contextual encoding from measurement updating, enabling both efficiency and rapid adaptation to dynamic battery states
• →Uses only 20-second historical windows instead of long sequences, reducing computational cost and positional bias problems
• →Combines Theta Attention Pooling and Fourier-based seasonality detection for efficient temporal pattern capture
• →Results tested on public drive-cycle datasets demonstrate robust performance across unseen driving profiles