Long-term Prediction: Utilizes historical data, dQ/dV curves, capacity-voltage curves with temperature/charging rate parameters
Real-time Prediction: Online data assessment for remaining life estimation
Charging Feature Extrapolation: Predicts lifespan under different charging strategies
Cross-condition Offline Prediction: Adaptable to varied working conditions
Data Analysis: Peak detection, time-series prediction, anomaly detection
High accuracy: <0.5% error (single condition), <1% (cross-condition)
Ultra-fast computation: 5s for 1500 cycles, 1ms per data point
Flexible deployment: Supports CPU/GPU (RTX3090 recommended for cross-condition)
Strong generalization: AI models adapt to new data scenarios
Multi-scenario application: Compatible with automotive BMS and industrial systems