This study addresses the challenges of low-quality, fragmented, and multi-source heterogeneous operation data collected by battery management systems (BMS) in energy storage applications. A gradient descent-based methodology is proposed to achieve high-precision splicing and reconstruction of lithium-ion energy storage battery operation data, validated through experimental and real-world operational datasets.
1. Mechanism Analysis
The splicing mechanism for energy storage battery operation data is established through transient-state analysis and gradient descent optimization. Key mathematical formulations include:
1.1 Gradient Descent Algorithm
Define the loss function for data continuity:
$$ J(X) = \sum_{i=1}^{m} \max\left(0, |x_i – x_{i-1}| – p\right)^2 $$
Gradient calculation:
$$ \frac{\partial J}{\partial x_i} = 2\left(|x_i – x_{i-1}| – p\right) \cdot \text{sign}(x_i – x_{i-1}) $$
Iterative update rule:
$$ x_i^{(t+1)} = x_i^{(t)} – \alpha \frac{\partial J}{\partial x_i} $$
1.2 Boundary Conditions
Critical constraints for energy storage battery data splicing:
| Parameter | Constraint | Equation |
|---|---|---|
| Current Continuity | ΔI ≤ 5A | $$ \left| I_{\text{front}} – I_{\text{back}} \right| \leq 5 $$ |
| Capacity Conservation | ΔC = 0 | $$ C_{\text{front}}^{\text{end}} = C_{\text{back}}^{\text{start}} $$ |
| Voltage Threshold | ΔU ≤ 0.005V | $$ \left| U_{\text{front}}^{\text{end}} – U_{\text{back}}^{\text{start}} \right| \leq 0.005 $$ |
2. Methodology
The proposed framework for energy storage battery data processing comprises:
| Stage | Operation | Key Parameters |
|---|---|---|
| Data Acquisition | BMS sensor sampling | Voltage, Current, Temperature |
| Data Segmentation | Operation mode identification | CC/CV threshold: 0.1C |
| Transient Filtering | Transient duration exclusion | $$ T_{\text{transient}} \geq 96s $$ |
3. Validation Results
Experimental verification using HPPC and RPT test data demonstrates:
3.1 HPPC Test Analysis
Voltage curve reconstruction achieves 99.2% continuity with transient characteristics:
$$ \left. \frac{dU}{dt} \right|_{t_0} \leq 0.0001 \, \text{V/s} $$
| Transient Event | Minimum (s) | Average (s) | Maximum (s) |
|---|---|---|---|
| Charge-Discharge Transition | 61 | 79.82 | 96 |
3.2 RPT Validation
Capacity reconstruction error distribution:
$$ \text{MAE} = 0.82\%,\ \text{RMSE} = 1.15\% $$
4. Engineering Applications
Practical implementations in peak-shaving and frequency-regulation scenarios show:
| Application | Battery Cluster | Reconstruction Accuracy |
|---|---|---|
| Peak Shaving | 240-cell @271Ah | 98.7% voltage continuity |
| Frequency Regulation | 240-cell @135A | 97.4% current matching |
5. State-of-Health Estimation
Integrated with incremental capacity analysis (ICA):
$$ \text{SOH} = \frac{Q_{\text{now}}}{Q_{\text{initial}}} \times 100\% $$
Where reconstructed data enables:
$$ \Delta Q_{\text{mid}} = \int_{U_1}^{U_2} \frac{dQ}{dU} dU $$
Conclusion
The proposed methodology effectively addresses energy storage battery data fragmentation challenges, demonstrating superior performance through multiple validation scenarios. The integration with state estimation algorithms significantly enhances BMS capabilities for large-scale energy storage systems.