1. Introduction
In our research on energy storage battery systems for critical infrastructure, we have observed that the energy storage battery pack serves as the core component of power supply for seismic monitoring stations. Ensuring the green, economic, safe, and stable operation of these stations requires sophisticated monitoring of the energy storage battery state. The national earthquake intensity rapid reporting and early warning project, which will be completed by the end of 2023, has established over 15,000 stations across China, with more than 30,000 energy storage battery units installed. These energy storage battery systems must operate continuously and reliably, as seismic events are random and unpredictable, demanding uninterrupted power supply for all monitoring instruments.
The performance of the energy storage battery directly impacts the entire earthquake early warning system’s functionality. Most seismic stations in China adopt AC power supply with UPS backup systems, but the energy storage battery remains indispensable for uninterrupted power supply. Even in remote, unmanned stations with solar or wind power generation, the energy storage battery is essential. Through our comprehensive literature review, we have analyzed various online monitoring systems, relatively mature energy storage battery modeling and analysis methods, and the current application status of battery management systems in seismic stations.
2. Development of Lithium-ion Batteries and SOC Estimation for Energy Storage Battery Systems
2.1 Overview of Lithium-ion Batteries for Energy Storage Battery Applications
2.1.1 Advantages of Lithium-ion Batteries as Energy Storage Battery Solutions
Through our extensive investigation of energy storage battery technologies, we have identified several key advantages of lithium-ion batteries that make them particularly suitable for energy storage battery applications in seismic stations:
| Parameter | Lithium-ion Battery | Lead-acid Battery | Nickel-cadmium Battery |
|---|---|---|---|
| Energy Density (mAh/g) | 100-270 | 30-50 | 40-60 |
| Nominal Voltage (V) | 3.0-4.2 | 2.0 | 1.2 |
| Self-discharge Rate (%/month) | <1 | 3-5 | 10-20 |
| Cycle Life (cycles) | >10,000 (LiFePO4) | 300-500 | 500-1000 |
| Operating Temperature (°C) | -20 to 60 | -20 to 50 | -20 to 60 |
| Memory Effect | None | None | Present |
| Environmental Impact | Low | High (lead) | High (cadmium) |
The energy storage battery based on lithium-ion technology offers exceptional performance characteristics. The high energy density means that for the same weight, the energy storage battery can store significantly more energy, which is crucial for remote seismic stations where transportation and space are limited. The high nominal voltage of 3.0-4.2 V per cell reduces the number of cells needed in series for the energy storage battery pack, simplifying the overall system design.
2.1.2 Disadvantages of Lithium-ion Batteries for Energy Storage Battery Systems
Despite the numerous advantages, we must acknowledge several challenges associated with lithium-ion batteries in energy storage battery applications:
| Disadvantage | Impact on Energy Storage Battery Performance | Mitigation Strategy |
|---|---|---|
| High Production Cost | Increases overall system cost | Advanced manufacturing techniques |
| Safety Concerns | Risk of explosion under extreme conditions | BMS with overcharge/discharge protection |
| Overcharge/Overdischarge Sensitivity | Permanent capacity loss | Precise voltage monitoring and control |
2.2 SOC Estimation for Energy Storage Battery Systems
The State of Charge (SOC) estimation is one of the most critical functions in any energy storage battery management system. Accurate SOC estimation ensures optimal utilization of the energy storage battery, prevents overcharging and overdischarging, and extends the service life of the energy storage battery pack.
2.2.1 Equivalent Circuit Models for Energy Storage Battery SOC Estimation
Through our analysis of various modeling approaches, we have found that equivalent circuit models (ECMs) are widely used for energy storage battery SOC estimation due to their balance of accuracy and computational efficiency. The PNGV (Partnership for a New Generation of Vehicles) model and the Thevenin model are particularly popular for energy storage battery applications.
The Thevenin equivalent circuit model for an energy storage battery can be expressed as:
$$
V_t = V_{OC}(SOC) – I \cdot R_0 – V_1
$$
$$
\frac{dV_1}{dt} = -\frac{V_1}{R_1 C_1} + \frac{I}{C_1}
$$
where Vt is the terminal voltage of the energy storage battery, VOC is the open-circuit voltage (a function of SOC), I is the current, R0 is the ohmic internal resistance, and V1 is the voltage across the RC network representing polarization effects.
For the PNGV model, which provides improved accuracy for energy storage battery modeling, the state-space representation is:
$$
\begin{bmatrix}
\dot{V}_1 \\
\dot{V}_p
\end{bmatrix} =
\begin{bmatrix}
-\frac{1}{R_1 C_1} & 0 \\
0 & 0
\end{bmatrix}
\begin{bmatrix}
V_1 \\
V_p
\end{bmatrix} +
\begin{bmatrix}
\frac{1}{C_1} \\
\frac{1}{C_b}
\end{bmatrix} I
$$
$$
V_t = V_{OC}(SOC) – V_1 – V_p – I \cdot R_0
$$
where Cb represents the capacitance of the energy storage battery, and Vp accounts for the open-circuit voltage variation due to charge accumulation.
2.2.2 Kalman Filter-based SOC Estimation for Energy Storage Battery
The Extended Kalman Filter (EKF) has become a standard technique for SOC estimation in energy storage battery systems. The discrete-time state-space model for SOC estimation using EKF can be formulated as:
$$
x_{k+1} = f(x_k, u_k) + w_k
$$
$$
y_k = h(x_k, u_k) + v_k
$$
where the state vector typically includes SOC and polarization voltages:
$$
x_k = \begin{bmatrix}
SOC_k \\
V_{1,k} \\
V_{2,k}
\end{bmatrix}
$$
The SOC update equation for the energy storage battery is:
$$
SOC_{k+1} = SOC_k – \frac{\eta \cdot I_k \cdot \Delta t}{C_n}
$$
where η is the Coulombic efficiency, Ik is the current at time step k, Δt is the sampling interval, and Cn is the nominal capacity of the energy storage battery.
The measurement equation relates the terminal voltage of the energy storage battery to the states:
$$
V_{t,k} = V_{OC}(SOC_k) – V_{1,k} – V_{2,k} – I_k \cdot R_0
$$
2.2.3 Comparison of SOC Estimation Methods for Energy Storage Battery Systems
| Method | Accuracy | Computational Complexity | Robustness to Noise | Applicability to Energy Storage Battery |
|---|---|---|---|---|
| Coulomb Counting | Low (error accumulates) | Very Low | Poor | Limited |
| Open Circuit Voltage | Medium | Low | Good | Requires rest period |
| Extended Kalman Filter | High (error < 4%) | Medium | Excellent | Widely used |
| Unscented Kalman Filter | Very High | High | Excellent | Improved nonlinear handling |
| Neural Network | High (with training) | High (training) | Good | Requires extensive data |
| Fuzzy Logic | Medium-High | Medium | Good | Rule-based approach |
| Random Forest + ELM | Very High | Medium-High | Excellent | Emerging technique |
Through our comparative analysis, we have determined that the EKF approach, when combined with accurate energy storage battery models, provides an excellent balance of accuracy and computational efficiency for real-time energy storage battery monitoring applications. The dual Kalman filter approach further enhances estimation accuracy by simultaneously estimating both states and parameters of the energy storage battery model.
The measurement error covariance adaptation technique improves the robustness of SOC estimation for energy storage battery systems operating under varying current conditions:
$$
R_k = \alpha \cdot R_{k-1} + (1-\alpha) \cdot (e_k e_k^T – H_k P_k^- H_k^T)
$$
where Rk is the measurement noise covariance, α is a forgetting factor, ek is the innovation sequence, Hk is the measurement Jacobian, and Pk– is the prior error covariance.
3. Balanced Control Strategies for Energy Storage Battery Systems
3.1 The Importance of Balancing in Energy Storage Battery Packs
In our study of energy storage battery systems, we have identified that cell imbalance is one of the most critical issues affecting the performance and lifespan of series-connected energy storage battery packs. When individual cells in an energy storage battery pack have different capacities, internal resistances, or SOC levels, the overall pack performance is limited by the weakest cell. This imbalance becomes more pronounced as the energy storage battery ages, leading to accelerated degradation and reduced usable capacity.
3.1.1 Causes of Imbalance in Energy Storage Battery Packs
| Cause | Effect on Energy Storage Battery | Severity |
|---|---|---|
| Manufacturing Variations | Initial capacity and impedance differences | Moderate |
| Temperature Gradients | Uneven aging and capacity fade | High |
| Different Self-discharge Rates | SOC divergence during storage | Moderate |
| Cycling History | Uneven degradation patterns | High |
3.2 Active Balancing Methods for Energy Storage Battery Systems
Through our review of current research, we have identified several effective active balancing methods for energy storage battery systems:
3.2.1 Switched Capacitor Balancing for Energy Storage Battery
The switched capacitor method transfers energy between adjacent cells in the energy storage battery pack through a flying capacitor. The equivalent energy transfer efficiency can be expressed as:
$$
\eta_{cap} = \frac{C_{fly} \cdot \Delta V^2 \cdot f_{sw}}{2 \cdot P_{loss}}
$$
where Cfly is the flying capacitance, ΔV is the voltage difference between cells, fsw is the switching frequency, and Ploss represents the power losses in the energy storage battery balancing circuit.
3.2.2 Inductor-based Balancing for Energy Storage Battery
Inductor-based balancing methods achieve higher energy transfer efficiency by using magnetic energy storage. The energy transfer in an inductor-based energy storage battery balancing system follows:
$$
E_{transfer} = \frac{1}{2} \cdot L \cdot I_{peak}^2
$$
$$
I_{peak} = \frac{V_{cell} \cdot t_{on}}{L}
$$
where L is the inductance, Ipeak is the peak current, Vcell is the cell voltage, and ton is the switch on-time for the energy storage battery balancing circuit.
3.2.3 Transformer-based Balancing for Energy Storage Battery
Multi-winding transformer approaches can balance multiple cells simultaneously in the energy storage battery pack. The balancing current for each energy storage battery cell is given by:
$$
I_{bal,i} = \frac{V_{cell,i} – V_{ref}}{R_{winding,i}}
$$
where Vref is the reference voltage and Rwinding,i is the winding resistance for the i-th energy storage battery cell.
3.2.4 Comparison of Balancing Methods for Energy Storage Battery Systems
| Method | Efficiency | Speed | Complexity | Cost | Application for Energy Storage Battery |
|---|---|---|---|---|---|
| Passive Balancing | Low (wastes energy as heat) | Slow | Low | Low | Small systems |
| Switched Capacitor | Medium (70-85%) | Medium | Low | Low | Modular designs |
| Inductor-based | High (85-95%) | Fast | Medium | Medium | Medium to large packs |
| Transformer-based | High (85-92%) | Very Fast | High | High | Large-scale systems |
| Hybrid Active Balancing | Very High (>95%) | Fast | High | High | Advanced BMS |
3.3 Distributed Control Strategies for Energy Storage Battery Balancing
We have investigated distributed control approaches for energy storage battery balancing, where each module communicates with its neighbors through a sparse communication network. The SOC balancing objective can be formulated as a consensus problem:
$$
SOC_i(k+1) = SOC_i(k) + \sum_{j \in N_i} a_{ij} \cdot (SOC_j(k) – SOC_i(k))
$$
$$
\lim_{k \to \infty} SOC_i(k) = SOC_{avg} \quad \forall i \in \{1, 2, …, N\}
$$
where aij are the consensus gains, Ni is the set of neighbors of module i, and N is the total number of energy storage battery modules in the system.
The convergence rate of the distributed balancing algorithm for the energy storage battery system depends on the algebraic connectivity of the communication graph, λ2 (the second smallest eigenvalue of the Laplacian matrix):
$$
\|SOC(k) – SOC_{avg} \cdot \mathbf{1}\| \leq (1 – c \cdot \lambda_2)^k \cdot \|SOC(0) – SOC_{avg} \cdot \mathbf{1}\|
$$
where c is a constant related to the control gain, and 1 is the vector of ones.
4. Energy Storage Battery Monitoring Systems: State of the Art
4.1 Evolution of Energy Storage Battery Monitoring Technology
Through our historical analysis, we have traced the development of energy storage battery monitoring systems from simple voltage measurement to sophisticated cloud-connected platforms. The evolution can be categorized into several generations:
| Generation | Period | Key Features | Limitations |
|---|---|---|---|
| First Generation | 1980s-1990s | Basic voltage and current monitoring | No intelligence, manual inspection |
| Second Generation | 1990s-2000s | Dedicated BMS chips, basic SOC estimation | Limited communication, proprietary protocols |
| Third Generation | 2000s-2010s | Distributed architecture, advanced algorithms | Computational constraints |
| Fourth Generation | 2010s-2020s | Cloud-connected, IoT-enabled, digital twin | Data security, latency concerns |
| Fifth Generation | 2020s-Future | AI-driven, predictive analytics, self-healing | High complexity, validation required |
4.2 Key Components of Energy Storage Battery Monitoring Systems
Our analysis of modern energy storage battery monitoring systems reveals several essential components that work together to ensure safe and efficient operation:
4.2.1 Voltage Sensing for Energy Storage Battery Cells
Accurate voltage measurement of each energy storage battery cell is fundamental to the monitoring system. The voltage measurement circuit for series-connected energy storage battery cells typically uses multiplexing and differential amplification:
$$
V_{cell,i} = G \cdot (V_{i+} – V_{i-}) + V_{offset}
$$
where G is the amplifier gain, Vi+ and Vi- are the positive and negative terminal voltages of the i-th energy storage battery cell, and Voffset is the offset voltage that must be calibrated out.
4.2.2 Current Sensing for Energy Storage Battery Systems
Current measurement in energy storage battery systems is typically performed using either shunt resistors or Hall effect sensors. The relationship between the measured voltage across the shunt and the current in the energy storage battery is:
$$
I_{battery} = \frac{V_{shunt}}{R_{shunt}}
$$
For Hall effect sensors, the output voltage is proportional to the magnetic field generated by the current in the energy storage battery circuit:
$$
V_{Hall} = K_H \cdot I_{battery} \cdot B_{bias}
$$
where KH is the Hall coefficient and Bbias is the bias magnetic field.
4.2.3 Temperature Monitoring for Energy Storage Battery Packs
Temperature monitoring is critical for energy storage battery safety and performance. The temperature distribution within an energy storage battery pack can be estimated using thermal models:
$$
\rho c_p \frac{\partial T}{\partial t} = \nabla \cdot (k \nabla T) + \dot{q}_{gen}
$$
$$
\dot{q}_{gen} = I^2 R_{ohm} + I T \frac{\partial V_{OC}}{\partial T} + \dot{q}_{reaction}
$$
where ρ is the density, cp is the specific heat capacity, k is the thermal conductivity, T is the temperature, and q̇gen is the heat generation rate within the energy storage battery.
4.3 Cloud-Connected Energy Storage Battery Monitoring Platforms
Our review of recent developments shows that cloud-connected battery management systems (BMS) represent the cutting edge of energy storage battery monitoring technology. These platforms leverage the computational and storage capabilities of cloud infrastructure to enhance the monitoring and prediction of energy storage battery behavior.
The architecture of a cloud-connected energy storage battery monitoring system typically includes:
| Layer | Components | Functions for Energy Storage Battery |
|---|---|---|
| Sensing Layer | Voltage, current, temperature sensors | Real-time data acquisition from energy storage battery |
| Communication Layer | IoT modules, wireless transceivers | Data transmission from energy storage battery to cloud |
| Cloud Infrastructure | Storage, computing, analytics engines | Advanced processing of energy storage battery data |
| Application Layer | Dashboards, alerts, visualization tools | User interface for energy storage battery monitoring |
The digital twin concept for energy storage battery systems involves creating a virtual replica that mirrors the real-time state of the physical energy storage battery. The state estimation in the digital twin can be expressed as:
$$
\hat{x}_{t+1|t} = f(\hat{x}_{t|t}, u_t, \theta_t)
$$
$$
\hat{y}_{t|t} = h(\hat{x}_{t|t}, u_t, \theta_t)
$$
where θt represents the model parameters that are continuously updated based on the energy storage battery’s actual behavior. The parameter adaptation follows:
$$
\theta_{t+1} = \theta_t + K_\theta \cdot (y_{measured} – \hat{y}_{t|t})
$$
5. Applications of Energy Storage Battery Monitoring in Seismic Stations
5.1 Specific Requirements for Energy Storage Battery Systems in Seismic Stations
Through our investigation of seismic station applications, we have identified unique requirements for energy storage battery systems in this critical infrastructure:
| Requirement | Description | Impact on Energy Storage Battery Design |
|---|---|---|
| Uninterrupted Operation | 24/7 continuous power supply | Redundant energy storage battery configuration |
| Remote Monitoring | Limited on-site personnel for unmanned stations | Comprehensive remote monitoring of energy storage battery |
| Harsh Environment | Extreme temperatures, humidity variations | Robust energy storage battery packaging and thermal management |
| Long Service Life | Minimize replacement frequency | Advanced energy storage battery chemistry and management |
| Low Maintenance | Reduced operational costs | Self-diagnostic energy storage battery management system |
5.2 Smart Power Controllers for Seismic Station Energy Storage Battery Systems
We have studied the implementation of smart power controllers designed specifically for seismic station energy storage battery management. These controllers integrate multiple functions including voltage regulation, battery monitoring, and remote communication.
The power management algorithm for the energy storage battery in a seismic station can be formulated as an optimization problem:
$$
\min_{P_{batt}} \sum_{t=0}^{T} w_1 \cdot (SOC_t – SOC_{target})^2 + w_2 \cdot P_{batt,t}^2
$$
$$
\text{subject to: } SOC_{min} \leq SOC_t \leq SOC_{max}
$$
$$
P_{batt,min} \leq P_{batt,t} \leq P_{batt,max}
$$
where Pbatt is the power drawn from or supplied to the energy storage battery, w1 and w2 are weighting factors, and T is the optimization horizon.
5.3 Remote Power Monitoring and Control for Seismic Station Energy Storage Battery
Our analysis of remote monitoring systems for seismic station energy storage battery systems reveals several key features:
The remote monitoring architecture typically includes:
$$
\text{Data Flow: } \text{Energy Storage Battery} \rightarrow \text{Data Acquisition} \rightarrow \text{Serial Server} \rightarrow \text{TCP/IP} \rightarrow \text{Remote Control Center}
$$
The control command for remote power cycling of equipment connected to the energy storage battery is:
$$
\text{Command}_{reset} = f(\text{Status}_{eq}, \text{SOC}_{batt}, \text{Priority}_{eq})
$$
where the reset decision for equipment connected to the energy storage battery is a function of equipment status, battery SOC, and equipment priority.
6. Challenges and Future Directions for Energy Storage Battery Online Monitoring
6.1 Current Challenges in Energy Storage Battery Monitoring
Through our comprehensive review, we have identified several persistent challenges in the field of energy storage battery online monitoring:
| Challenge | Description | Current Research Approaches for Energy Storage Battery |
|---|---|---|
| SOC Estimation Accuracy | Maintaining accuracy over battery lifetime | Adaptive algorithms, machine learning for energy storage battery |
| SOH Estimation | Quantifying degradation in real-time | Impedance spectroscopy, data-driven models for energy storage battery |
| Cell Balancing | Efficient balancing under varying conditions | Hybrid active balancing for energy storage battery packs |
| Thermal Management | Preventing thermal runaway | Advanced cooling, predictive thermal models for energy storage battery |
| Data Security | Protecting cloud-transmitted data | Encryption, blockchain for energy storage battery data |
| Standardization | Lack of unified protocols | Industry collaboration for energy storage battery standards |
6.2 Future Directions for Energy Storage Battery Monitoring Technology
Based on our analysis of current trends and emerging technologies, we project several key directions for the future of energy storage battery online monitoring:
6.2.1 High-Precision Intelligent Monitoring for Energy Storage Battery Systems
The integration of advanced sensors and artificial intelligence will enable unprecedented precision in energy storage battery monitoring. Future systems will incorporate:
$$
\text{Precision} \rightarrow \text{Cell-level: } \pm 0.1 \text{ mV}, \pm 0.01^\circ \text{C}, \pm 0.1 \text{ m}\Omega
$$
The AI-driven diagnostic framework for energy storage battery systems can be expressed as:
$$
P(\text{Failure}_i | \mathbf{D}_t) = \frac{P(\mathbf{D}_t | \text{Failure}_i) \cdot P(\text{Failure}_i)}{\sum_j P(\mathbf{D}_t | \text{Failure}_j) \cdot P(\text{Failure}_j)}
$$
where P(Failurei | Dt) is the posterior probability of failure mode i given the observed data Dt from the energy storage battery monitoring system.
6.2.2 Integration and Miniaturization of Energy Storage Battery Monitoring Systems
We anticipate that future energy storage battery monitoring systems will achieve higher levels of integration, reducing size and power consumption while improving functionality. The system-on-chip (SoC) approach for energy storage battery monitoring will integrate:
$$
\text{SoC}_{monitor} = \{\text{Sensing}, \text{ADC}, \text{Processing}, \text{Communication}, \text{Power Management}\}
$$
The power budget for an integrated energy storage battery monitoring chip can be expressed as:
$$
P_{total} = P_{sensing} + P_{ADC} + P_{processing} + P_{communication} + P_{quiescent}
$$
$$
P_{total} \rightarrow \text{Target: } < 1 \text{ mW per energy storage battery cell}
$$
6.2.3 Wireless and Networked Energy Storage Battery Monitoring
The transition from wired to wireless monitoring will significantly enhance the flexibility and scalability of energy storage battery systems. The wireless sensor network for energy storage battery monitoring can be modeled as:
$$
\text{Network}_{\text{Energy Storage Battery}} = \{N_1, N_2, …, N_n\}
$$
$$
\text{Data Rate} = \sum_{i=1}^{n} R_i \geq R_{critical}
$$
$$
\text{Latency} = \max_{i,j} (t_{transmit,i} + t_{propagate,ij} + t_{process,j}) < t_{critical}
$$
where Ni represents the i-th wireless node monitoring the energy storage battery, Rcritical is the minimum data rate required for reliable energy storage battery monitoring, and tcritical is the maximum allowable latency for real-time control of the energy storage battery system.
6.2.4 Deep Integration with Energy Management Systems for Energy Storage Battery
We foresee that future energy storage battery monitoring systems will be deeply integrated with overall energy management systems. The hierarchical control architecture can be represented as:
$$
\text{EMS}_{\text{Integrated}} = f(\text{SOC}_{\text{Energy Storage Battery}}, \text{Load}_{\text{Station}}, \text{Generation}_{\text{Renewable}}, \text{Grid}_{\text{Status}})
$$
The optimal dispatch strategy considering the energy storage battery state is:
$$
\min_{P_{grid}, P_{batt}} C_{total} = C_{grid}(P_{grid}) + C_{degradation}(\Delta SOC_{batt})
$$
$$
\text{subject to: } P_{load} = P_{grid} + P_{batt} + P_{renewable}
$$
$$
SOC_{min} \leq SOC_{batt} \leq SOC_{max}
$$
where Ctotal is the total operational cost, Cgrid is the cost of grid power, and Cdegradation is the degradation cost of the energy storage battery.
7. Conclusion
Through our comprehensive review of energy storage battery status online monitoring technology, we have drawn several important conclusions. The energy storage battery remains the core power supply component for seismic monitoring stations, and ensuring its safe, reliable, and efficient operation is paramount for the continuous operation of earthquake early warning systems.
We have systematically analyzed various SOC estimation methods for energy storage battery systems, including equivalent circuit models, Kalman filter-based approaches, and machine learning techniques. Our comparison shows that the Extended Kalman Filter combined with accurate battery models provides an excellent balance of accuracy and computational efficiency for real-time energy storage battery monitoring. Emerging techniques such as random forest optimized extreme learning machines show promise for even higher accuracy in energy storage battery SOC estimation.
In the domain of balanced control for energy storage battery packs, we have reviewed passive and active balancing methods, with hybrid active balancing approaches demonstrating superior performance in terms of both efficiency and speed for energy storage battery applications. The distributed control strategy based on consensus algorithms offers a scalable solution for large-scale energy storage battery systems.
The evolution of energy storage battery monitoring systems from basic voltage measurement to cloud-connected intelligent platforms represents a significant advancement. Cloud-based energy storage battery management systems leverage the computational power and storage capacity of cloud infrastructure to enable more sophisticated state estimation, fault diagnosis, and predictive maintenance for energy storage battery systems.
For seismic station applications specifically, the energy storage battery monitoring system must meet stringent requirements for reliability, remote operability, and robustness to harsh environmental conditions. Smart power controllers and remote monitoring systems have been successfully deployed to enhance the management of energy storage battery systems in seismic stations.
Looking forward, we anticipate that energy storage battery monitoring technology will evolve toward higher precision, greater integration, wireless connectivity, and deeper integration with energy management systems. The development of advanced sensors, AI-driven diagnostics, and digital twin technology will further enhance our ability to monitor and manage energy storage battery systems effectively. The continued advancement of energy storage battery online monitoring technology will play a crucial role in ensuring the safety, reliability, and efficiency of critical infrastructure systems, particularly seismic monitoring networks that depend on uninterrupted power supply from energy storage battery systems.
From the perspectives of safety, reliability, green economy, and operational efficiency, the development of effective energy storage battery status online monitoring systems is essential. These systems enable real-time monitoring of energy storage battery performance while dynamically displaying operational parameters, thereby improving energy utilization efficiency and extending the service life of energy storage battery packs. The integration of advanced energy storage battery monitoring technology into the ongoing expansion and upgrading of national seismic monitoring networks will contribute significantly to the formation of scientifically sound, technologically advanced, functionally complete, and operationally efficient earthquake monitoring systems.