In the field of energy storage systems, the operation data of energy storage lithium batteries serves as the foundation for data-driven state evaluation and fault prediction. However, existing battery management systems (BMS) often collect data with low quality, complex operating conditions, and fragmented charge-discharge cycles, leading to challenges in accurate analysis. This paper addresses these issues by investigating the mechanisms and methods for splicing and reconstructing operation data of energy storage lithium batteries. Using gradient descent algorithms and empirical analyses, I propose a novel approach that enhances data continuity and accuracy, facilitating improved state estimation and fault detection. The energy storage lithium battery is central to this study, as its performance directly impacts the efficiency and reliability of energy storage systems. Through extensive experimentation and real-world data validation, I demonstrate the effectiveness of the proposed method in handling multi-source heterogeneous data, ultimately contributing to the optimization of BMS functionalities.
The splicing and reconstruction of energy storage lithium battery operation data involve combining discontinuous data fragments into continuous charge-discharge sequences. This process is crucial because fragmented data, often caused by scheduling and pricing factors in practical applications, hinders the application of advanced algorithms like incremental capacity analysis. By focusing on the energy storage lithium battery, I aim to bridge this gap by developing a methodology that ensures data integrity and consistency. The core of this work lies in analyzing transient and steady-state characteristics during operational shifts, such as changes in current rates and voltage responses. For instance, when an energy storage lithium battery switches between charging, discharging, and idle states, it undergoes transient phases that must be accounted for to avoid distortions in reconstructed data. This study leverages statistical methods and empirical modeling to establish boundary conditions and mathematical equations, enabling high-precision data reconstruction.
To elucidate the mechanisms, I employ gradient descent optimization to minimize discrepancies at splicing points. The loss function is defined as follows: $$J(X) = \sum_{i=2}^{m} \max(0, |x_i – x_{i-1}| – p)^2$$ where \(X = \{x_1, x_2, \dots, x_m\}\) represents the dataset of data fragments, \(x_i\) denotes the \(i\)-th data point, and \(p\) is the boundary value at the splicing point. The gradient is computed as: $$\frac{\partial J}{\partial x_i} = 2(|x_i – x_{i-1}| – p) \cdot \text{sign}(x_i – x_{i-1})$$ and the gradient descent update is: $$x_i^{(t+1)} = x_i^{(t)} – \alpha \frac{\partial J}{\partial x_i}$$ where \(\alpha\) is the learning rate and \(t\) is the iteration number. This iterative process helps identify optimal boundary conditions for splicing, ensuring minimal error in the reconstructed data of the energy storage lithium battery.
Key boundary conditions for splicing and reconstructing energy storage lithium battery data are derived from empirical analyses. For current consistency, the difference between consecutive fragments must satisfy: $$I_d = |I_{\text{front}} – I_{\text{back}}| \leq 5 \, \text{A}$$ where \(I_{\text{front}}\) and \(I_{\text{back}}\) are the current values of the preceding and succeeding fragments, respectively. This ensures that current amplitudes do not cause abrupt changes in voltage or capacity. For cumulative charge/discharge capacity, the condition is: $$C_d = |C_{\text{front}}^{\text{end}} – C_{\text{back}}^{\text{start}}| = 0$$ where \(C_{\text{front}}^{\text{end}}\) is the final cumulative capacity of the front fragment and \(C_{\text{back}}^{\text{start}}\) is the initial cumulative capacity of the back fragment. This maintains energy conservation and data continuity. Voltage consistency requires: $$U_d = |U_{\text{front}}^{\text{end}} – U_{\text{back}}^{\text{start}}| \leq 0.005 \, \text{V}$$ and the voltage rate of change must adhere to: $$k_d = |k_{\text{front}}^{\text{end}} – k_{\text{back}}^{\text{start}}| \leq 0.0001$$ where \(k\) represents the voltage derivative over time. These conditions ensure smooth transitions and reflect the dynamic behavior of the energy storage lithium battery.
Transient duration during operational shifts is another critical factor. Based on statistical analysis of experimental data, the transient phase when an energy storage lithium battery changes states (e.g., from charging to discharging) has the following empirical values: maximum duration \(T_{\text{max}} = 96 \, \text{s}\), minimum duration \(T_{\text{min}} = 61 \, \text{s}\), and average duration \(T_{\text{mean}} = 79.82 \, \text{s}\). Therefore, for effective splicing, the time after a state change should satisfy \(T \geq T_{\text{max}}\). This avoids incorporating unstable transient data into the reconstruction. The table below summarizes these statistical values for transient duration in energy storage lithium batteries:
| Order | Duration (s) | Order | Duration (s) |
|---|---|---|---|
| 1 | 61 | 7 | 82 |
| 2 | 67 | 8 | 85 |
| 3 | 69 | 9 | 86 |
| 4 | 72 | 10 | 87 |
| 5 | 77 | 11 | 88 |
| 6 | 80 | 12 | 96 |
The methodology for splicing and reconstructing energy storage lithium battery operation data consists of several steps. First, data acquisition involves collecting key parameters such as current, voltage, and cumulative capacity from BMS sensors. Second, data fragments are divided based on time, current rate changes, or operational modes, ensuring each fragment represents a stable, single-condition sequence. Third, data cleaning and preprocessing remove anomalies and interpolate missing values. Fourth, splicing and reconstruction are performed by applying the boundary conditions and optimization algorithms. The flowchart below illustrates this process, emphasizing the iterative nature of achieving seamless data integration for energy storage lithium batteries.
After initial reconstruction, further optimization is applied to enhance the smoothness and accuracy of the charge-discharge curves. Mathematical models, such as equivalent circuit models or Kalman filters, can be used to adjust the data. For example, the resistance-capacitance (RC) model describes the electrical behavior of an energy storage lithium battery: $$V(t) = V_{\text{oc}} – I(t)R – \frac{1}{C} \int I(t) \, dt$$ where \(V(t)\) is the terminal voltage, \(V_{\text{oc}}\) is the open-circuit voltage, \(I(t)\) is the current, \(R\) is the internal resistance, and \(C\) is the capacitance. Machine learning algorithms, like support vector machines (SVM) or artificial neural networks, can also be trained on historical data to fit the curves. Incremental learning methods, such as online SVM, allow continuous model updates with new data, improving adaptability for energy storage lithium battery applications.
Validation and assessment of the reconstructed data are essential. Statistical analysis compares pre- and post-reconstruction data using metrics like mean, variance, and correlation coefficients. Error evaluation indicators, such as root mean square error (RMSE), R-squared value (R²), and mean absolute error (MAE), quantify accuracy. For instance, RMSE is defined as: $$\text{RMSE} = \sqrt{\frac{1}{n} \sum_{i=1}^{n} (y_i – \hat{y}_i)^2}$$ where \(y_i\) is the observed value and \(\hat{y}_i\) is the reconstructed value. Graphical comparisons visually demonstrate the reconstruction quality, and application-based testing in real-world scenarios verifies practicality for energy storage lithium battery systems.
Experimental validation using hybrid pulse power characterization (HPPC) tests confirms the efficacy of the proposed method. In HPPC tests, an energy storage lithium battery with a capacity of 280 Ah and current amplitude of 140 A (0.5 C) undergoes pulsed discharges. The reconstructed voltage curve seamlessly integrates fragmented data, as shown in comparative plots. Statistical analysis of transient durations from HPPC data reveals the empirical values mentioned earlier, reinforcing the reliability of the approach for energy storage lithium batteries. Similarly, reference performance test (RPT) data for charging phases, with a battery capacity of 75 Ah and current of 0.2 C, demonstrate successful reconstruction of randomly split fragments into continuous curves. This highlights the method’s robustness across different test conditions for energy storage lithium batteries.
Real-world applications in grid services, such as peak shaving and frequency regulation, further validate the method. For example, in a peak shaving scenario, an energy storage lithium battery with a capacity of 271 Ah and current of 135 A (0.5 C) produces voltage curves that are reconstructed from multiple fragments. The table below summarizes empirical ranges from actual operation data for energy storage lithium batteries, providing guidance for practical implementations:
| Parameter | Range |
|---|---|
| Voltage (V) | 3.307–3.68 |
| Current (A) | 2.5–136.4 |
| State of Energy (%) | 41–83 |
| Duration (s) | 100–1220 |
In frequency regulation cases, data from battery clusters with 240 cells each, capacities of 271 Ah, and currents of 10 A or 30 A are successfully reconstructed, demonstrating the method’s adaptability to varying operational demands for energy storage lithium batteries. The reconstructed curves maintain consistency in voltage and current, enabling more accurate state analysis.
An important engineering application involves combining data splicing and reconstruction with incremental capacity analysis (ICA) for state of health (SOH) estimation of energy storage lithium batteries. ICA relies on complete charge-discharge curves to analyze electrochemical characteristics, but real-world data often lacks continuity. By reconstructing fragmented data into full cycles, ICA can be effectively applied. The process begins with splicing and reconstructing operation data fragments of the energy storage lithium battery, followed by data cleaning. Next, the initial ICA curve is plotted to identify peak voltages, such as \(u_1\) and \(u_2\), and the initial mid-segment capacity \(Q_{\text{start}}\) is computed as the integral area between \(u_1\) and \(u_2\). For the current operational stage, the mid-segment capacity \(Q_{\text{now}}\) is calculated, and SOH is estimated using: $$S_{\text{health}} = \frac{Q_{\text{now}}}{Q_{\text{start}}} \times 100\%$$ This approach leverages the reconstructed data to provide reliable SOH estimates, enhancing the maintenance and longevity of energy storage lithium batteries.
In conclusion, the splicing and reconstruction of energy storage lithium battery operation data address critical challenges in data quality and continuity. By leveraging gradient descent algorithms and empirical boundary conditions, I have developed a method that ensures high accuracy and adaptability across experimental and real-world scenarios. The integration with techniques like ICA for SOH estimation underscores the practical value of this work. Future research could explore advanced machine learning models or real-time implementation in BMS to further optimize the performance of energy storage lithium batteries. This study contributes to the broader goal of improving energy storage system reliability and efficiency through data-driven innovations.