In modern energy applications, such as microgrids, renewable energy integration, and electric vehicles, the battery energy storage system plays a pivotal role due to its high energy density, long cycle life, and low self-discharge rate. However, when multiple cells are connected in series to form a battery pack, inevitable inconsistencies among individual cells—arising from manufacturing variations, aging, or operational conditions—can severely impact the overall energy utilization, cycle life, and safety of the battery energy storage system. To address this, effective equalization techniques are essential. In this work, we propose a novel multi-threshold adaptive clustering group equalization control method, designed to enhance the balancing speed and efficiency for large-scale battery energy storage systems. Our approach leverages the idea of clustering adjacent cells with minor consistency differences into groups, allowing energy transfer between groups of varying sizes, rather than merely between individual cells. This innovation aims to improve the consistency among cells, thereby boosting the performance and longevity of battery energy storage systems.
The core of our method lies in combining a simple yet functional equalization topology with an intelligent control strategy. We first introduce a single-inductor energy storage equalization circuit, which offers a straightforward structure and control while supporting versatile energy transfer modes—between individual cells, between cells and modules, and between modules. The working principle is based on inductive energy storage and transfer, where the inductor charges from higher-energy cells or groups and discharges to lower-energy ones. The control signal duty cycle is designed to ensure safe operation in discontinuous conduction mode, preventing inductor saturation. For instance, if the number of cells in the discharging group is denoted as \(N_d\) and in the charging group as \(N_c\), the duty cycle \(D\) for the charging phase must satisfy:
$$D \leq 1 – \frac{N_d}{N_d + N_c} \times 100\%$$
This ensures that the inductor current resets within each switching cycle. Our topology, as illustrated below, incorporates MOSFETs, diodes, and a single inductor, enabling flexible energy redistribution across the battery energy storage system.
Building upon this hardware foundation, we develop the multi-threshold adaptive clustering group equalization control strategy. The key idea is to dynamically cluster adjacent cells with similar state-of-charge (SOC) values into groups, based on adaptive thresholds, and then perform energy transfer between these groups. This approach capitalizes on the fact that in large battery energy storage systems, the probability of having adjacent cells with small consistency differences is high, allowing for simultaneous balancing of multiple cells. The control flow involves several steps: first, we set an equalization activation threshold \(\phi\); if the SOC difference between the maximum and minimum cells exceeds \(\phi\), equalization is triggered. Second, we define clustering thresholds \(\phi_H\) and \(\phi_L\) for high-SOC and low-SOC cells, respectively, which adapt based on the current SOC range:
$$\phi_H = \text{SOC}_{\text{max}} – x \Delta, \quad \phi_L = \text{SOC}_{\text{min}} + y \Delta$$
where \(\Delta = \text{SOC}_{\text{max}} – \text{SOC}_{\text{min}}\), and \(x, y < 0.3\) to ensure small differences within groups. Third, we cluster adjacent cells around the highest-SOC cell \(B_m\) and lowest-SOC cell \(B_n\) that satisfy \(\text{SOC} > \phi_H\) or \(\text{SOC} < \phi_L\), respectively. This clustering process is adaptive, as the thresholds update with changing SOC values. Finally, we execute group-based energy transfer by adjusting the duty cycle according to the group sizes, as per the equation above. This method not only speeds up equalization but also reduces the discrete degree of SOC distribution after balancing.
To validate our approach, we conducted extensive simulations using MATLAB/Simulink, modeling a battery energy storage system with 12 series-connected cells. The parameters for our simulation are summarized in Table 1, which includes key details such as battery capacity, inductance, and switching frequency. These settings ensure a realistic representation of typical battery energy storage system operations.
| Parameter | Value |
|---|---|
| Number of Cells | 12 |
| Battery Rated Capacity | 2.6 Ah |
| Battery Rated Voltage | 3.7 V |
| Inductance (L) | 47 μH |
| Diode Forward Voltage | 0.4 V |
| Equalization Start Threshold (\(\phi\)) | 3% |
| Switching Frequency | 10 kHz |
We tested our method under various initial SOC distributions to mimic real-world scenarios in battery energy storage systems: (i) SOC high in the middle and low on both ends, (ii) SOC high on both ends and low in the middle, and (iii) uniform SOC distribution. For comparison, we implemented a traditional “single-to-single” equalization control based on the extreme SOC difference, where energy is transferred directly from the highest-SOC cell to the lowest-SOC cell with a fixed 50% duty cycle. The results, as shown in Table 2, demonstrate the superiority of our clustering group method in terms of equalization speed and consistency improvement.
| Initial SOC Distribution | Equalization Method | Equalization Time (s) | Speed Improvement | Post-Equalization SOC Standard Deviation |
|---|---|---|---|---|
| Middle High, Ends Low | Single-to-Single | 230 | — | 1.1135 |
| Clustering Group (Proposed) | 137 | 40.4% faster | 0.9640 | |
| Ends High, Middle Low | Single-to-Single | 232 | — | 1.1136 |
| Clustering Group (Proposed) | 175 | 24.6% faster | 0.9773 | |
| Uniform Distribution | Single-to-Single | 229 | — | 1.1150 |
| Clustering Group (Proposed) | 189 | 17.5% faster | 1.0395 |
The equalization efficiency \(\eta\) is defined as the ratio of the total SOC after equalization to the initial total SOC, calculated as:
$$\eta = \frac{\sum_{i=1}^{n} \text{SOC}_{i,\text{end}}}{\sum_{i=1}^{n} \text{SOC}_{i,\text{start}}} \times 100\%$$
where \(n\) is the number of cells. In all cases, our method maintained comparable efficiency (around 99.8% to 99.9%) to the traditional approach, while significantly reducing the equalization time. For instance, in the middle-high ends-low distribution, the clustering group method completed balancing in 137 seconds, compared to 230 seconds for the single-to-single method—a 40.4% speed enhancement. This acceleration stems from the ability to transfer energy between groups of cells, leveraging higher currents when multiple cells are involved. The duty cycles for different group sizes are predefined, as shown in Table 3, to optimize inductor usage and prevent saturation.
| Number of Cells in High-SOC Group | Charging Phase Duty Cycle | Discharging Phase Duty Cycle |
|---|---|---|
| 5 | 16% | 84% |
| 4 | 20% | 80% |
| 3 | 25% | 75% |
| 2 | 34% | 66% |
| 1 | 50% | 50% |
Furthermore, we analyzed the dynamic behavior of the battery energy storage system under the Urban Dynamometer Driving Schedule (UDDS) to simulate practical operating conditions. The SOC curves and range variations, as depicted in our simulations, confirm that the proposed control effectively reduces SOC differences to within 3% after equalization, and maintains this consistency during cycling. The equalization current waveforms, illustrated for multiple switching cycles, show that our method adapts the current magnitude based on group clustering—for example, when five cells are clustered, the current peaks higher than in single-cell transfers, thereby accelerating energy redistribution. This adaptability is crucial for large-scale battery energy storage systems, where efficiency and speed are paramount.
In addition to speed, our method enhances the uniformity of SOC distribution post-equalization. The standard deviation of SOC values across the battery pack is consistently lower with clustering group control, indicating a more homogeneous state. This is vital for prolonging the lifespan of the battery energy storage system, as reduced inconsistencies mitigate overcharging or over-discharging of individual cells. The adaptive clustering thresholds, \(\phi_H\) and \(\phi_L\), ensure that grouping is always relevant to the current SOC spread, avoiding unnecessary energy transfers and minimizing losses. Mathematically, the clustering process can be described as: for the highest-SOC cell \(B_m\), we include adjacent cells \(B_{m-1}, B_{m+1}, \dots\) if their SOC satisfies \(\text{SOC} > \phi_H\), until the condition fails. Similarly, for the lowest-SOC cell \(B_n\), we cluster adjacent cells with \(\text{SOC} < \phi_L\). This iterative check ensures that only cells with minor differences are grouped, optimizing the energy transfer path.
From an implementation perspective, our equalization topology requires \(2n+2\) MOSFETs and diodes for an \(n\)-cell battery energy storage system, along with a single inductor. This simplicity reduces cost and control complexity compared to more elaborate circuits. The control signals are generated based on real-time SOC monitoring, which can be achieved through estimators or sensors. We emphasize that our method is particularly suited for battery energy storage systems with many cells, as the probability of adjacent cell clustering increases, unlocking greater balancing potential. Future work could focus on integrating this control with advanced SOC estimation algorithms or developing specialized hardware to further streamline the process.
In conclusion, we have presented a multi-threshold adaptive clustering group equalization control method for battery energy storage systems, which significantly improves balancing speed by enabling energy transfer between cell groups of varying sizes. Through simulations, we demonstrated its effectiveness across different SOC distributions, with speed enhancements of up to 40.4% compared to traditional single-to-single methods, while maintaining high efficiency and reducing SOC离散程度. This innovation offers a promising direction for managing consistency in large-scale battery energy storage systems, ultimately enhancing their energy utilization, cycle life, and reliability in diverse applications.