Accurate SOC estimation is crucial for effective battery management in LiFePO4 batteries, valued for safety and high power capabilities. This guide presents a novel SOC estimation model using CNN, GRU, and TCN to improve accuracy and reliability.
Key insights include:
1. Traditional SOC methods like open-circuit voltage and ampere-hour integration have limitations in handling complex battery behaviors.
2. SOC measurement is challenging due to the non-linear relationship between SOC and voltage in LiFePO4 batteries.
3. The proposed model uses CNN for feature extraction, GRU for time-based modeling, and TCN for long-term dependencies.
4. The CNN-GRU-TCN model achieves lower error rates than other models, demonstrating high accuracy and robustness.
This model supports safer, more efficient battery management for energy storage applications.
