I focus on the safety issues of mobile energy storage cells in power systems. In this study, I propose a safety estimation model for mobile energy storage cells based on intelligent battery management system (BMS) technology. The primary technical parameters and safety impact characteristics of mobile energy storage cells are first analyzed. I then examine the application of intelligent BMS technology and its main usage scenarios in battery safety. A battery model based on the BMS and a state monitoring model based on the dual extended Kalman filter (DEKF) are established to estimate the state of charge (SOC), internal resistance, capacity, and state of health (SOH) of the energy storage cell. Simulation analysis demonstrates that the proposed model significantly improves monitoring efficiency compared to the original algorithm.
1. Overview of Mobile Energy Storage Cell Parameters
Mobile energy storage cells, primarily lithium iron phosphate (LFP) types, are widely used due to high capacity and long life. Key technical parameters include:
| Parameter | Symbol | Description |
|---|---|---|
| Capacity | Q | Energy stored, measured in Ah or kWh |
| Energy Density | — | Energy per unit volume or mass |
| Power Density | — | Rate of energy release per unit volume or mass |
| State of Charge | SoC or z | Remaining charge as percentage of full capacity |
| Open-Circuit Voltage | Vocv | Voltage at no load |
| Internal Resistance | R0 | Ohmic resistance of the cell |
| Charge/Discharge Rate | C-rate | Rate relative to capacity |
Safety of an energy storage cell is influenced by module structure, material selection, BMS, and thermal management. Overcharging, overcurrent, internal short circuits, external short circuits, and excessive temperature are critical factors that can lead to thermal runaway. Table 1 summarizes the factors affecting safety in an energy storage cell.
| BMS Aspect | Effect | Safety Issue |
|---|---|---|
| No energy input | Insufficient output | Power loss |
| Low energy input | Insufficient output | Power loss |
| Overvoltage | Overcharge | Smoke, fire, electrolyte leakage |
| Overcurrent | Overcurrent in pack | Unsafe heat generation |
| High system voltage | High voltage exposure | Electric shock risk |
I emphasize that the energy storage cell must be operated within safe limits to avoid thermal runaway. Material choice (e.g., LFP) improves thermal stability due to robust crystal structure.
2. Intelligent BMS Technology
An intelligent BMS monitors, controls, and protects the energy storage cell. It includes modules for parameter acquisition, state estimation, protection, balancing, and communication. Figure 1 illustrates the typical BMS architecture.
Key functions of an intelligent BMS for an energy storage cell include:
- Overcharge/overdischarge protection: Monitors voltage and limits current to prevent damage.
- Temperature monitoring and control: Uses sensors to detect excess heat and reduce charge rate or stop charging.
- Short circuit protection: Detects abnormal current and disconnects the circuit.
- Health monitoring: Estimates SOH and predicts remaining useful life.
3. Battery Modeling and Safety State Analysis
3.1 Equivalent Circuit Model
I model the energy storage cell using a second-order RC equivalent circuit. The model includes an open-circuit voltage Vocv as a function of SOC, a series resistance R0, and two RC parallel branches (R1-C1 and R2-C2). The terminal voltage Vb is:
$$
V_b(t) = V_{ocv}(z(t)) – I(t) R_0 – V_1(t) – V_2(t)
$$
where z(t) is the SOC, I(t) is the current, and V1, V2 are the voltages across the RC branches. The open-circuit voltage is modeled as a sixth-order polynomial:
$$
V_{ocv}(z(t)) = \sum_{m=1}^{6} p_m z(t)^{6-m}
$$
The SOC update equation is:
$$
z(t+1) = z(t) – \frac{\Delta T}{Q} \eta(t) I(t)
$$
where ΔT is the sampling time, Q is the capacity, and η is the Coulombic efficiency. The RC branch voltages evolve as:
$$
V_1(t+1) = V_1(t)\left(1 – \frac{\Delta T}{R_1 C_1}\right) + \frac{\Delta T}{C_1} I(t)
$$
$$
V_2(t+1) = V_2(t)\left(1 – \frac{\Delta T}{R_2 C_2}\right) + \frac{\Delta T}{C_2} I(t)
$$
The complete state-space model for the energy storage cell is:
$$
\begin{bmatrix}
V_1(t+1) \\
V_2(t+1) \\
z(t+1)
\end{bmatrix}
=
\begin{bmatrix}
1 – \frac{\Delta T}{R_1 C_1} & 0 & 0 \\
0 & 1 – \frac{\Delta T}{R_2 C_2} & 0 \\
0 & 0 & 1
\end{bmatrix}
\begin{bmatrix}
V_1(t) \\
V_2(t) \\
z(t)
\end{bmatrix}
+
\begin{bmatrix}
-\frac{\Delta T}{C_1} \\
-\frac{\Delta T}{C_2} \\
-\frac{\Delta T}{Q} \eta(t)
\end{bmatrix}
I(t) + w(t)
$$
Parameters are identified by minimizing the sum of squared errors between measured and estimated voltage:
$$
F = \min \sum_{i=1}^{n} \left( V_b(i) – \hat{V}_b(i) \right)^2
$$
subject to constraints: C1≥0, C2≥0, R1≥0, R2≥0.
3.2 Dual Extended Kalman Filter for State Monitoring
I apply a dual extended Kalman filter (DEKF) to estimate both the fast-changing states (SOC, V1, V2) and the slow-changing parameters (internal resistance R0 and capacity Cp). The two EKFs run in parallel: EKFx estimates the state vector, and EKFθ estimates the parameter vector.
The parameter dynamics are:
$$
\theta_{k+1} = \theta_k + q_k, \quad \theta = \begin{bmatrix} R_0 \\ C_p \end{bmatrix}
$$
where qk is process noise. The state of health (SOH) of the energy storage cell is defined by capacity degradation:
$$
\text{SOH} = \frac{C_i}{C_0} \times 100\%
$$
where Ci is the estimated capacity and C0 is the nominal capacity. Alternatively, based on internal resistance:
$$
\text{SOH} = \frac{R_{eol} – R_b}{R_{eol} – R_n} \times 100\%
$$
with Reol = 1.6 Rb (end-of-life resistance).
The monitoring procedure is:
- Initialize the battery model and DEKF.
- At each time step, measure voltage and current.
- Run EKFx to estimate SOC and polarization voltages.
- Run EKFθ to estimate R0 and capacity.
- Compute SOH from capacity or resistance estimates.
4. Simulation Analysis
I simulate a 50 kWh LFP energy storage cell used in mobile power utility applications using MATLAB. The cell is subjected to charge and discharge cycles.
4.1 SOC Estimation Results
Figure 2 shows the SOC estimation during charging. The maximum SOC error is 0.3% and voltage error 0.13% for a charge cycle. For a discharge cycle, the SOC error is 0.58% and voltage error 1.05×10−4. These results confirm accurate SOC tracking.
Table 2 compares the absolute SOC error between standard EKF and DEKF for three cycles.
| Cycle | Charge EKF | Charge DEKF | Discharge EKF | Discharge DEKF |
|---|---|---|---|---|
| 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 |
Overall, DEKF yields smaller absolute errors in most cases because it simultaneously estimates internal resistance and capacity, improving SOC accuracy for the energy storage cell.
4.2 State of Health Estimation
Capacity and resistance estimates from DEKF are used to compute SOH. Table 3 shows capacity-based SOH, which remains at 100% after three cycles. Table 4 shows resistance-based SOH, slightly decreasing as internal resistance increases.
| 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 |
The simulations confirm that the DEKF-based model accurately tracks the health of the energy storage cell, detecting subtle changes in internal resistance.
5. Conclusion
In this paper, I have presented a safety state analysis and monitoring model for mobile energy storage cells based on intelligent BMS technology and a dual extended Kalman filter. The proposed DEKF model achieves higher estimation accuracy for SOC and SOH compared to the standard EKF, particularly by jointly estimating internal resistance and capacity. Simulation results on a 50 kWh LFP energy storage cell demonstrate that the DEKF reduces SOC estimation error and effectively tracks health degradation. This approach enhances the safety monitoring capability of the intelligent BMS, ensuring reliable operation of the energy storage cell in mobile power applications.