Abstract
To address the safety concerns associated with mobile energy storage batteries in power systems, this paper proposes a safety estimation model utilizing intelligent Battery Management System (BMS) technology. The primary technical parameters and safety impact characteristics of mobile battery energy storage are analyzed, and an in-depth examination of the application of intelligent BMS technology in battery safety scenarios is conducted. A battery model based on a battery safety management system and a battery status monitoring model employing the Dual Extended Kalman Filter (DEKF) are established to estimate the battery’s State of Charge (SoC), internal resistance, capacity, and health status. Simulation analysis demonstrates that the proposed model significantly enhances monitoring efficiency compared to conventional algorithms.
1. Introduction
With advancements in equipment technology, energy storage batteries play an increasingly vital role as critical energy storage devices. Ensuring the safety, stability, and reliability of energy storage batteries poses a significant challenge, particularly during long-term operation. Effective monitoring of the safety status of energy storage batteries is essential for detecting potential issues promptly and maintaining safe system operation.
Traditional monitoring methods often rely on physically based mathematical models, which are typically complex and susceptible to inaccuracies due to battery’s internal physicochemical characteristics, especially in complex operational environments. Consequently, Intelligent Battery Management Systems (BMS) have emerged as a promising solution, enhancing monitoring precision and real-time performance through advanced data processing, algorithms, and sensor technologies.
This paper aims to achieve precise monitoring of mobile energy storage battery safety states using BMS technology in conjunction with the Dual Extended Kalman Filter (DEKF) algorithm. DEKF, an efficient state estimation method, provides more accurate estimations of critical parameters such as battery charge state, health state, and temperature.
2. Literature Review
Previous studies have explored various aspects of battery safety and BMS applications. Several notable contributions include:
- Battery Safety Research: Several studies have focused on battery safety characteristics, particularly those based on lithium-ion batteries. For instance, the development of safety features for lithium-iron phosphate battery-based energy storage systems has been reported. Additionally, analyses on the fire safety of lithium-ion battery energy storage stations and the design of safety monitoring systems for electrochemical energy storage stations have been conducted.
- BMS Applications: While BMS has been widely applied in various fields, its primary focus has been on electric vehicles. Research on safety management systems for electric vehicle batteries highlights the importance of BMS in maintaining battery safety.
- State Estimation Methods: Kalman filtering techniques, including the Extended Kalman Filter (EKF) and its variations, have been applied to estimate battery states. Studies have demonstrated the effectiveness of EKF in joint estimation of energy and power states and State of Health (SoH). However, further research is needed, particularly regarding the dynamic changes in battery resistance and capacity.
3. Technical Parameters and Safety Characteristics of Mobile Energy Storage Batteries
3.1 Main Technical Parameters
Mobile energy storage batteries encompass various types, including lead-carbon, lithium-iron phosphate, and vanadium redox flow batteries. Lithium-iron phosphate batteries are widely used due to their high capacity and long lifespan. The primary technical parameters include:
- Capacity: Measured in units such as kilowatt-hours (kWh), indicates the total energy a battery can store.
- Energy Density: The amount of energy stored per unit volume or mass.
- Power Density: The rate at which energy can be released per unit volume or mass.
- SoC: The ratio of the current battery charge to its maximum capacity.
- Open-Circuit Voltage (OCV): The voltage of a battery when not connected to a load, providing insights into its charge state.
- Internal Resistance: Resistance to current flow within the battery, affecting energy efficiency.
- Charge/Discharge Rate: The rate at which a battery can be charged or discharged.
3.2 Safety Characteristics Analysis
Factors contributing to battery safety include overcharging, overcurrent, internal and external short circuits, and excessive temperatures. Safety is influenced by the following aspects:
- Module Structure: Effective separation of battery cells prevents the spread of issues from one cell to another. Proper heat dissipation design mitigates thermal runaway risks.
- Material Selection: Lithium-iron phosphate improves thermal stability and reduces the risk of thermal runaway.
- BMS Functionality: Comprehensive BMS capabilities, including charge/discharge control, temperature monitoring, and voltage/current protection, ensure safe operation.
- Cell Temperature Control Measures: Timely monitoring and control of battery temperatures prevent thermal runaway.
4. Intelligent BMS Technology
BMS, a vital component of battery systems, oversees monitoring, control, and protection to ensure safe, stable, and efficient operation. Its functions encompass:
- Battery Parameter Monitoring: Real-time monitoring of voltage, current, temperature, SoC, and individual cell voltages.
- State Estimation and Prediction: Mathematical models and algorithms analyze battery parameters to estimate remaining capacity, health status, and lifespan.
- Battery Protection and Balancing: Prevents overcharging, overdischarging, overcurrent, and overheating. Balances cell voltages to extend battery life.
- Communication and Control: Enables remote monitoring, control, and data transmission, ensuring overall system stability and safety.
4.1 BMS Functional Modules
- Battery Parameter Acquisition Module: Monitors battery parameters using sensors.
- State Estimation and Control Module: Analyzes acquired data for state estimation and charging/discharging strategies.
- Protection Module: Detects and mitigates potential hazards.
- Balancing Module: Ensures voltage balance among battery cells.
- Communication Module: Facilitates data exchange with external systems.
5. Battery Modeling and Safety State Analysis
5.1 Battery Model
An equivalent circuit model is used for lithium-ion batteries, comprising an open-circuit voltage (Vocv), serial resistance (R0), and RC circuits.
The battery output voltage (Vb) is a function of SoC, current (I), and internal resistances, as expressed in Equations (1) to (5). Model parameters are optimized using nonlinear generalized reduced gradient algorithms to minimize the difference between measured and estimated voltages.
5.2 Dual Extended Kalman Filter for Battery State Monitoring
The Dual Extended Kalman Filter (DEKF) combines two EKFs: one for fast-changing states (EKFx) and another for slow-changing states (EKFθ). This approach enables concurrent estimation of SoC, internal resistance, and capacity, enhancing accuracy.
- EKFx Estimates: Fast-changing states like SoC.
- EKFθ Estimates: Slow-changing parameters like internal resistance and capacity.
Health status (SoH) is estimated based on capacity (Equation 16) and internal resistance (Equations 17 and 18).
6. Simulation Analysis
6.1 Input Conditions
Simulations were conducted using MATLAB for a 50 kWh mobile energy storage battery intended for power grid maintenance tasks.
6.2 Results
SoC Estimation:
SoC estimation results during charging and discharging cycles are presented, respectively. The DEKF approach yields lower estimation errors compared to EKF, as summarized in Table 1.
Table 1: SoC Estimation Error Comparison
| Cycle | Charging Error (EKF) | Charging Error (DEKF) | Discharging Error (EKF) | Discharging Error (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 |
Health Status Estimation:
Capacity estimates and their corresponding SoH values are presented in Tables 2 and 3. The battery maintains a high SoH even after multiple cycles, indicating minimal degradation.
Table 2: Capacity and SoH Estimates
| Cycle | Capacity (Ah) | SoH (%) |
|---|---|---|
| 1 | 1400 | 100 |
| 2 | 1400 | 100 |
| 3 | 1400 | 100 |
Table 3: Internal Resistance and SoH Estimates
| Cycle | Internal Resistance (Ω) | SoH (%) |
|---|---|---|
| 1 | 0.03007 | 99.91 |
| 2 | 0.03004 | 99.96 |
| 3 | 0.030005 | 99.98 |
7. Conclusion
This paper presents a safety estimation model for mobile energy storage batteries based on intelligent BMS technology and the DEKF algorithm. Simulation results demonstrate that the proposed DEKF model significantly outperforms conventional EKF in estimating SoC, internal resistance, capacity, and SoH, particularly during dynamic changes. The model effectively enhances battery safety monitoring, ensuring reliable operation in power systems.