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cloud.google.com·7h ago
AI Research
arXiv CS.AI·Aug 11·5 min read
C2L-Net: A Data-Driven Model for State-of-Charge Estimation of Lithium-Ion Batteries During Discharge
TL;DR
C2L-Net is a new data-driven model designed for accurately estimating the state-of-charge (SOC) of lithium-ion batteries during discharge. It significantly enhances computational efficiency, achieving up to 60 times faster inference while maintaining high accuracy.
✦ Why It Matters
Consider implementing C2L-Net for real-time SOC estimation in your battery management systems to enhance performance.
Key Takeaways
- Implement C2L-Net for efficient SOC estimation in battery systems.
- Utilize short historical data windows to reduce computational costs.
- Leverage chunk-based feature extraction for improved performance.
- Adopt causal context encoding to prevent information leakage.
How It Works
C2L-Net separates the process of contextual encoding from the latest measurement updates. It uses a chunk-based feature extraction mechanism that combines Theta Attention Pooling with a Fourier-based Seasonality Basis to effectively capture local temporal patterns while reducing the input sequence length.
The causal context encoder, which integrates a gated recurrent unit (GRU) with Causal Cosine Attention, models temporal dependencies without leaking information from future states.