In recent years, the rapid advancement of energy storage technologies has positioned mobile energy storage cells as critical components in power systems, particularly for applications such as emergency power supply, grid stabilization, and portable energy solutions. However, ensuring the safety, stability, and reliability of these energy storage cells remains a significant challenge. As a researcher focused on energy storage systems, I have observed that issues like overcharging, overcurrent, internal and external short circuits, and excessive temperatures can lead to catastrophic failures, including thermal runaway, fires, or explosions. To address these concerns, intelligent Battery Management System (BMS) technology has emerged as a pivotal solution, enabling real-time monitoring, control, and protection of energy storage cells. In this article, I propose a safety estimation model for mobile energy storage cells based on intelligent BMS technology, incorporating dual extended Kalman filtering for state estimation. The model aims to accurately monitor key parameters such as state of charge (SoC), internal resistance, capacity, and state of health (SoH), thereby enhancing the safety and performance of energy storage cells in mobile applications. Throughout this discussion, I will emphasize the importance of energy storage cell safety and how intelligent BMS can mitigate risks, supported by mathematical models, simulations, and analytical insights.
The foundation of this research lies in understanding the technical parameters and safety characteristics of mobile energy storage cells. Typically, these energy storage cells, such as lithium iron phosphate (LiFePO4) variants, are preferred due to their high energy density, long cycle life, and thermal stability. Key technical parameters include capacity (measured in kWh), which defines the total energy storage capability; energy density, indicating storage per unit volume or mass; power density, representing the rate of energy discharge; state of charge (SoC), which reflects the current energy level relative to maximum capacity; open-circuit voltage (OCV), related to SoC; internal resistance, affecting efficiency and heat generation; and charge/discharge rates, determining how quickly energy can be absorbed or released. These parameters directly influence the safety of energy storage cells. For instance, high internal resistance can lead to excessive heat buildup, while improper SoC management may cause overcharging or over-discharging, escalating the risk of thermal runaway. To illustrate, I have summarized the primary technical parameters and their safety implications in Table 1.
| Parameter | Description | Safety Impact |
|---|---|---|
| Capacity | Total energy storage (kWh) | Insufficient capacity may lead to over-discharge, causing cell damage. |
| Energy Density | Energy per unit volume or mass | High density increases risk of thermal runaway if not properly managed. |
| Power Density | Rate of energy discharge | Rapid discharge can generate heat, potentially leading to fires. |
| State of Charge (SoC) | Current energy level (%) | Overcharging or over-discharging can cause electrolyte decomposition or short circuits. |
| Open-Circuit Voltage (OCV) | Voltage at no load | Abnormal OCV may indicate internal faults, increasing safety risks. |
| Internal Resistance | Resistance to current flow | High resistance results in energy loss and heat, raising temperature-related hazards. |
| Charge/Discharge Rate | Speed of energy transfer | High rates can induce stress, leading to mechanical failures or thermal events. |
Safety in energy storage cells is influenced by multiple factors, including module structure, material selection, BMS functionality, and thermal control measures. For example, a well-designed module structure can isolate individual cells to prevent cascading failures, while materials like LiFePO4 enhance thermal stability due to their robust crystal structure. However, without an effective BMS, these energy storage cells are vulnerable to issues like overvoltage, overcurrent, and temperature excursions. In my analysis, I have identified common safety factors and their mitigation strategies, as shown in Table 2. This underscores the necessity of integrating intelligent BMS technology to monitor and control these parameters in real-time, ensuring the safe operation of mobile energy storage cells.
| Safety Factor | Description | Mitigation Strategy |
|---|---|---|
| Overcharging | Exceeding maximum voltage limits | Implement voltage protection in BMS to cut off charging. |
| Overcurrent | Current beyond safe thresholds | Use current limiting and short-circuit protection. |
| Internal Short Circuit | Fault within the cell structure | Employ cell balancing and isolation mechanisms. |
| External Short Circuit | Fault in external connections | Incorporate circuit breakers and fault detection. |
| High Temperature | Excessive heat leading to thermal runaway | Integrate temperature sensors and cooling systems. |
Intelligent BMS technology plays a crucial role in enhancing the safety of energy storage cells by providing comprehensive monitoring, estimation, and control. As a core component, BMS utilizes sensors to track parameters like voltage, current, and temperature, and employs algorithms for state estimation, such as SoC and SoH. The functional architecture of a typical intelligent BMS includes modules for parameter acquisition, state estimation and control, protection, balancing, and communication. For instance, the protection module prevents overcharging and over-discharging by continuously monitoring voltage levels, while the balancing module ensures uniform voltage across cells to extend lifespan. In terms of safety management, BMS addresses issues like temperature fluctuations through real-time monitoring and adaptive control strategies. To elaborate, I have outlined the key functions of an intelligent BMS in Table 3, highlighting how each module contributes to the overall safety of energy storage cells.
| Module | Function | Safety Contribution |
|---|---|---|
| Parameter Acquisition | Collects voltage, current, temperature data | Enables early detection of anomalies, preventing hazardous conditions. |
| State Estimation and Control | Estimates SoC, SoH, and other states | Facilitates optimal charging/discharging, reducing risk of overstress. |
| Protection Module | Monitors for overvoltage, overcurrent, etc. | Immediately shuts down operations in unsafe scenarios. |
| Balancing Module | Equalizes cell voltages | Prevents individual cell failures that could trigger chain reactions. |
| Communication Module | Transmits data to external systems | Allows remote monitoring and intervention for enhanced safety. |
To quantitatively assess the safety of energy storage cells, I developed a battery model based on an equivalent circuit approach, which is integral to the BMS framework. This model includes components such as an open-circuit voltage source (Vocv), a series resistance (R0), and RC networks representing polarization effects. The output voltage Vb of the energy storage cell can be expressed as a function of SoC and other parameters. Specifically, the model is defined by the following equations, which form the basis for state estimation using dual extended Kalman filtering (DEKF). The state-space representation captures the dynamics of the energy storage cell, allowing for accurate prediction of its behavior under various operating conditions.
The battery model equations are as follows. The output voltage is given by:
$$ V_b(t) = V_{ocv}(z(t)) – I(t) R_0 – V_1(t) – V_2(t) $$
where \( V_b(t) \) is the measured voltage, \( z(t) \) is the SoC, \( I(t) \) is the current, \( R_0 \) is the series resistance, and \( V_1(t) \) and \( V_2(t) \) are the voltages across the RC networks. The open-circuit voltage \( V_{ocv} \) is a polynomial function of SoC:
$$ V_{ocv}(z(t)) = \sum_{m=1}^{6} p_m z(t)^{6-m} $$
with \( p_m \) being polynomial coefficients. The SoC update equation considers the sampling time \( \Delta T \), battery capacity \( Q \), and efficiency \( \eta \):
$$ z(t+1) = z(t) – \frac{\Delta T}{Q} \eta(t) I(t) $$
The voltages \( V_1 \) and \( V_2 \) are governed by the discrete-time equations derived from Kirchhoff’s laws:
$$ V_1(t+1) = V_1(t) \left(1 – \frac{\Delta T}{R_1 C_1}\right) + \frac{\Delta T}{C_1} I(t) $$
$$ V_2(t+1) = V_2(t) \left(1 – \frac{\Delta T}{R_2 C_2}\right) + \frac{\Delta T}{C_2} I(t) $$
where \( R_1 \), \( R_2 \), \( C_1 \), and \( C_2 \) are the resistances and capacitances of the RC branches. Combining these, the overall state-space model for the energy storage cell is:
$$ x(t+1) = A x(t) + B u(t) + w(t) $$
with the state vector \( x(t) = [V_1(t), V_2(t), z(t)]^T \), input \( u(t) = I(t) \), and process noise \( w(t) \). The matrices A and B are defined as:
$$ A = \begin{bmatrix}
1 – \frac{\Delta T}{R_1 C_1} & 0 & 0 \\
0 & 1 – \frac{\Delta T}{R_2 C_2} & 0 \\
0 & 0 & 1
\end{bmatrix}, \quad B = \begin{bmatrix}
\frac{\Delta T}{C_1} \\
\frac{\Delta T}{C_2} \\
-\frac{\Delta T}{Q} \eta(t)
\end{bmatrix} $$
This model is optimized using a nonlinear generalized reduced gradient algorithm to minimize the error between measured and estimated voltages, ensuring it accurately represents the energy storage cell behavior. The objective function is:
$$ F = \min \sum_{i=1}^{n} (V_b – \hat{V}_b)^2 $$
subject to constraints such as \( C_1 \geq 0 \), \( C_2 \geq 0 \), \( R_1 \geq 0 \), and \( R_2 \geq 0 \), where \( \hat{V}_b \) is the estimated voltage. This formulation allows the BMS to reliably monitor the energy storage cell’s state, which is crucial for safety.
For state estimation, I employed a dual extended Kalman filter (DEKF) to handle the nonlinearities in the energy storage cell model. DEKF consists of two filters: one for estimating rapidly changing states (e.g., SoC) and another for slowly varying parameters (e.g., internal resistance and capacity). This approach enhances the accuracy of safety-related metrics like SoH. The first EKF (EKFx) estimates the state vector \( x \), while the second EKF (EKFθ) estimates the parameter vector \( \theta \), which includes internal resistance \( R_0 \) and capacity \( C_p \). The parameter update equation is:
$$ \theta_{k+1} = \theta_k + q_k $$
where \( \theta = [R_0, C_p]^T \) and \( q_k \) is the process noise. The SoH is then calculated based on capacity degradation and internal resistance changes. For capacity-based SoH:
$$ \text{SoH} = \frac{C_i}{C_0} \times 100\% $$
where \( C_i \) is the estimated capacity and \( C_0 \) is the nominal capacity. For resistance-based SoH:
$$ \text{SoH} = \frac{R_{\text{eol}} – R_b}{R_{\text{eol}} – R_n} \times 100\% $$
with \( R_{\text{eol}} = 1.6 R_b \), where \( R_b \) is the measured internal resistance, \( R_n \) is the resistance of a new energy storage cell, and \( R_{\text{eol}} \) is the end-of-life resistance. This dual estimation process enables the BMS to dynamically track the health and safety status of the energy storage cell, providing early warnings for potential failures.
To validate the proposed model, I conducted simulations using MATLAB, focusing on a mobile energy storage cell with a rated capacity of 50 kWh, typical for power grid applications. The simulation involved charge-discharge cycles to evaluate the performance of DEKF in estimating SoC, internal resistance, capacity, and SoH. The results demonstrate the effectiveness of the intelligent BMS in enhancing safety. For instance, during a charge cycle, the SoC estimation error was as low as 0.3%, with a voltage error of 0.13%, while in discharge cycles, the SoC error was 0.58% and voltage error was 1.05 × 10^{-4}. These findings indicate that the DEKF-based approach provides high accuracy in state estimation, which is vital for preventing overcharging or over-discharging in energy storage cells.
I compared the performance of the standard extended Kalman filter (EKF) and the DEKF in terms of SoC estimation accuracy across multiple cycles. The results, summarized in Table 4, show that DEKF consistently outperforms EKF with lower absolute deviations. This improvement is attributed to DEKF’s ability to simultaneously estimate internal resistance and capacity, leading to more reliable SoC calculations. Such precision is crucial for maintaining the safety of energy storage cells, as it allows the BMS to make informed decisions based on real-time data.
| Cycle | Charge EKF Error | Charge DEKF Error | Discharge EKF Error | Discharge DEKF Error |
|---|---|---|---|---|
| 1 | 0.0094 | 0.0059 | 0.0014 | 0.0019 |
| 2 | 0.0032 | 0.0019 | 0.0058 | 0.0045 |
| 3 | 0.0105 | 0.0147 | 0.0087 | 0.0078 |
Additionally, the SoH estimation results for the energy storage cell are presented in Table 5 and Table 6. Table 5 shows that the capacity-based SoH remains at 100% over three cycles, indicating no significant degradation. However, Table 6 reveals that as internal resistance increases, the SoH gradually decreases but stays above 99%, demonstrating the robustness of the energy storage cell. These metrics are essential for predictive maintenance, as they help identify potential safety hazards before they escalate. For example, a decline in SoH could signal the need for cell replacement to avoid failures. The integration of these estimates into the BMS allows for proactive safety management, ensuring the long-term reliability of mobile energy storage cells.
| Cycle | Capacity (Ah) | SoH (%) |
|---|---|---|
| 1 | 1400 | 100 |
| 2 | 1400 | 100 |
| 3 | 1400 | 100 |
| Cycle | Internal Resistance (Ω) | SoH (%) |
|---|---|---|
| 1 | 0.030073 | 99.91 |
| 2 | 0.030042 | 99.96 |
| 3 | 0.030005 | 99.98 |
In conclusion, the integration of intelligent BMS technology with advanced estimation algorithms like DEKF significantly enhances the safety of mobile energy storage cells. By accurately monitoring SoC, internal resistance, capacity, and SoH, the proposed model enables real-time detection of potential issues, such as overcharging or thermal runaway, thereby mitigating risks. The simulation results confirm that DEKF provides superior performance compared to traditional EKF, with lower estimation errors and better adaptability to dynamic changes in energy storage cell parameters. This research underscores the importance of continuous innovation in BMS design to address the evolving challenges in energy storage safety. Future work could explore the integration of machine learning techniques for even more precise state predictions, further solidifying the role of intelligent BMS in safeguarding energy storage cells across various applications.