With the global transition in energy structures, renewable energy sources such as wind and solar power are increasingly integrated into modern power systems. While these sources offer significant environmental benefits, they are characterized by high volatility and intermittency. Energy storage lithium battery systems play a crucial role in addressing challenges like peak shaving, valley filling, and instability mitigation. They are widely applied to balance grid loads, regulate voltage and frequency, and store renewable energy. However, traditional energy storage control methods often rely on fixed strategies, which may suffice for simple scenarios but exhibit substantial limitations in complex load environments. These limitations lead to inefficient energy management, poor operational stability, accelerated battery degradation, and reduced lifespan. To overcome these issues, intelligent control technologies have emerged as a key research focus for optimizing energy storage lithium battery systems. Intelligent control enables dynamic adjustment of charging and discharging strategies based on real-time load variations and employs advanced algorithms to predict load demands and optimize regulatory processes. This paper proposes an optimization technology for energy storage lithium battery systems based on intelligent control, aiming to enhance system adaptability in complex load conditions through improved control workflows.
The core of this optimization lies in refining key performance indicators of the energy storage lithium battery system, including charging and discharging efficiency, battery lifespan, system stability, and response time. By leveraging real-time data acquisition and intelligent algorithms, the system dynamically adjusts battery operating modes to maximize efficiency. Data management and intelligent control are utilized to predict load changes and fine-tune charging and discharging strategies, ensuring battery longevity while optimizing performance. The charging and discharging efficiency is calculated as follows:
$$ \eta = \frac{E_{\text{out}}}{E_{\text{in}}} $$
where \( E_{\text{out}} \) represents the output energy and \( E_{\text{in}} \) denotes the input energy. The intelligent control strategy enhances this efficiency by balancing the state of charge across battery cells and optimizing the input-output energy ratio in real-time. Factors such as the frequency of charging and discharging cycles, depth of discharge, and temperature significantly impact the lifespan of energy storage lithium batteries. The intelligent control approach reduces the number of deep discharge cycles to extend battery life, as expressed by:
$$ L = \frac{C_{\text{max}}}{1 + \alpha \cdot \text{DoD}} $$
Here, \( C_{\text{max}} \) is the maximum battery capacity, \( \alpha \) is a constant related to battery characteristics, and DoD represents the depth of discharge. By optimizing the discharge depth and implementing advanced thermal management, the system minimizes battery stress and prevents overheating, thereby prolonging service life. System stability is critical when dealing with periodic fluctuating loads and sudden load changes. The power fluctuation amplitude \( \Delta P \) quantifies stability:
$$ \Delta P = P_{\text{max}} – P_{\text{min}} $$
Real-time monitoring of grid and load states allows for adjustments in charging and discharging power, reducing the frequency of large power adjustments and minimizing fluctuations. Response time is another vital metric, defined as:
$$ T_{\text{response}} = \frac{T_{\text{output}}}{T_{\text{input}}} $$
where \( T_{\text{output}} \) is the time taken for the system to adjust its output after a load change, and \( T_{\text{input}} \) is the time of load change input. Predictive and feedback mechanisms, combined with intelligent data management, reduce response time and enhance real-time accuracy and adaptability.
The optimization process for energy storage lithium battery systems under intelligent control involves several structured steps, as outlined below. First, data acquisition and preliminary analysis are conducted. Base data is collected, preprocessed, and fed into the control system. The state of charge (SOC) is computed in real-time to assess battery status:
$$ \text{SOC} = \frac{C_{\text{current}}}{C_{\text{max}}} \times 100 $$
where \( C_{\text{current}} \) is the current remaining capacity and \( C_{\text{max}} \) is the maximum capacity. Second, control algorithms are designed using machine learning and predictive models like Long Short-Term Memory (LSTM) networks to forecast load demand and optimize charging and discharging strategies. For load demand \( L(t) \), the predicted demand at time \( t+1 \) is given by:
$$ \hat{L}(t+1) = f(L(t), L(t-1), \ldots, L(t-n)) $$
where \( f(\cdot) \) is the prediction model function and \( n \) is the time step for historical data. The optimization objective \( J \) minimizes energy loss over a period \( T \):
$$ J = \min \sum_{t=0}^{T} E_{\text{loss}}(t) $$
with \( E_{\text{loss}}(t) \) representing energy loss at time step \( t \). Third, optimization execution and feedback are implemented. A feedback control function \( u(t) \) adjusts charging and discharging rates based on real-time sensor data of battery state \( S(t) \) and load changes \( L(t) \):
$$ u(t) = K_p (L(t) – \hat{L}(t)) + K_i \sum_{i=0}^{t} (L(i) – \hat{L}(i)) + K_d (L(t) – L(t-1)) $$
where \( K_p \), \( K_i \), and \( K_d \) are proportional, integral, and derivative coefficients, respectively. This feedback mechanism ensures stability and efficiency by minimizing the impact of load fluctuations. Fourth, performance evaluation and system adjustments are performed. Key indicators such as energy loss rate and response time are analyzed to iteratively refine control strategies, further enhancing stability and extending battery life.
To validate the practical effectiveness of the proposed intelligent control-based optimization technology for energy storage lithium battery systems, experimental studies were conducted comparing it with traditional fixed-strategy control methods. The energy storage lithium battery system comprised lithium iron phosphate battery packs, a battery management system (BMS), an energy management system (EMS), a load simulation device, and a data acquisition and analysis system. Key parameters of the system are summarized in the table below.
| Parameter Name | Typical Value / Range |
|---|---|
| Single Cell Rated Voltage | 3.2 V |
| Rated Capacity | 50–300 Ah |
| Rated Energy | 10–500 kWh |
| Charge-Discharge Rate | 0.5 C–3 C |
| Rated Output Power | 10–1000 kW |
| Response Time | ≤100 ms |
| AC Output Voltage | 220 / 380 V |
| Temperature Protection Threshold | 65 °C |
| Operating Temperature Range | -20–60 °C |
Experiments were designed under two typical operating conditions: periodic fluctuating load environments, simulating the impact of renewable energy integration on grid stability, and random load variation environments, testing system adaptability to sudden load changes. In the periodic fluctuating load scenario, the intelligent control optimization demonstrated superior stability compared to traditional methods. The traditional approach exhibited high-frequency oscillations with significant amplitude variations in current and voltage, leading to increased device wear and grid instability. In contrast, the intelligent control strategy substantially reduced fluctuation amplitudes, resulting in smoother operation and improved grid compatibility. Similarly, under random load variations, the traditional method showed pronounced oscillations in response to load changes, whereas the optimized system maintained lower fluctuation levels, indicating enhanced dynamic response and reliability. These findings highlight the ability of intelligent control to precisely adjust charging and discharging strategies, ensuring stable performance and higher power quality.
Further analysis of energy storage effectiveness under both operating conditions yielded quantitative results, as presented in the following table. The data clearly indicates that the intelligent control optimization technology outperforms traditional methods across all key metrics, including power fluctuation range, energy loss rate, battery charging and discharging stability, and system response time.
| Operating Condition | Technology Used | Average Power Fluctuation Range (W) | Energy Loss Rate (%) | Battery Charge-Discharge Stability | System Response Time (ms) |
|---|---|---|---|---|---|
| Periodic Fluctuating Load | Traditional Technology | 120–180 | 8.5 | Low | 220 |
| Periodic Fluctuating Load | Proposed Technology | 50–80 | 3.2 | High | 150 |
| Random Load Variation | Traditional Technology | 140–200 | 9.3 | Low | 250 |
| Random Load Variation | Proposed Technology | 60–90 | 4.1 | High | 170 |
The proposed intelligent control-based optimization technology for energy storage lithium battery systems significantly enhances charging and discharging management through smart control strategies, improving adaptability in complex conditions such as periodic fluctuating loads and random load variations. Experimental results confirm that this approach outperforms traditional fixed-strategy methods in critical metrics like power fluctuation, energy loss rate, battery stability, and response speed. By applying this technology, energy storage lithium battery systems can handle load fluctuations more efficiently, extend equipment lifespan, and bolster overall grid stability and reliability. Future work should focus on refining intelligent control algorithms for computational efficiency and integrating advanced machine learning techniques for more accurate load forecasting and dynamic optimization. This progress will further solidify the role of energy storage lithium battery systems in sustainable energy infrastructures.