Summary:In response to various issues encountered during the operation of battery storage systems in substation applications, such as frequent malfunctions and reduced lifespan, we introduce an on-line monitoring solution for battery packs and individual battery cells. The scheme adheres to a master-slave modular design, with the master control module responsible for monitoring the state of battery packs, querying individual cell states, executing commands, and communicating with the host computer. It employs an I2C serial bus for link management of each individual battery measurement unit.
KeywordsTransformer Substation; Battery Bank; Online Monitoring Solution
1. The Significance of Online Monitoring for Lead-Acid Batteries
Battery packs for substation backup power are crucial for protection, control, signaling, and communication devices, with their stability, durability, and health status being of utmost importance. Currently, the inspection of battery packs still relies on manual rounds, which are prone to human error. Therefore, a rapid, accurate, reliable, and safe online monitoring technology for battery packs is particularly important. With the rapid development of microprocessors and the widespread use of network communication, battery pack monitoring technology can obtain the status of battery packs and individual cells in real-time through monitoring modules and send the information to the back-end monitoring center via the network. The corresponding maintenance personnel can then promptly obtain the status information of the battery packs and develop maintenance plans, which helps to enhance the reliability of the entire backup power system, reduce maintenance costs, and improve work efficiency.
2. Online Battery Monitoring Solution
2.1 Overall Framework
The solution is primarily designed for online monitoring of battery packs and individual battery cells, enabling real-time monitoring of various status parameters of individual batteries and battery packs. The solution consists of a master control module, node module, and sensors.
The master control module is used for online monitoring of the total voltage, total current, and environmental temperature of the battery pack. The serial node detection module is an electrical isolation bus for distributing multiple individual batteries via the I2C bus. Due to the I2C bus's addressing capability of 8 bits, or 2^8 = 128, it can connect up to 128 individual batteries. Since the total number of batteries in the battery pack is ≤120, using the I2C bus for addressing and bus isolation is the optimal solution. The node module online monitors the individual voltage, current, and internal resistance of each battery. The sensor module collects voltage, current, and temperature data, converting it into data recognizable and processable by the processor through the AD converter of the master control module and node module. This solution also provides a standard RJ45 communication interface, based on the IEEE802.3 protocol, and together with the computer at the management center, forms a remote distributed online battery monitoring system. This system plays a crucial role in ensuring the safe operation of the user's backup battery pack. The overall framework of the solution is shown in Figure 1.
Figure 1: Overall Framework of Online Battery Monitoring Solution
2.2 Key Features
b. Utilizing a master-slave network structure, node modules are addressed and isolated using I2C serial buses, facilitating monitoring and management by the master control end. Due to the characteristics of the I2C bus, it can support the measurement and management of up to 128 battery cells with just one module.
b. The master module utilizes an RJ45/IEEE802.3 communication interface to communicate with the computer at the management center, offering advantages over traditional RS232/RS485 interfaces, such as more reliable transmission and faster speeds.
c. By using the I2C addressing method, the master control module can monitor the status of any individual battery at will, avoiding the outdated serial bus from having to access and collect all battery status information each time. This allows for precise identification of faulty individual batteries, reducing replacement and repair costs.
d. Node modules feature on-chip flash, allowing temporary storage of individual battery status data. If the main control module requires access to the data, it can directly read from the flash, enhancing data interaction time.
The main control module communicates with the battery pack via two communication lines: one transmits data signals and the other is used for clock synchronization, ensuring the entire system's safety and synchronization.
3. Modular Design
3.1 Master Control Module
The main control module of the battery online monitoring solution is composed of an NXPLPC1700 Cortex-M3 CPU, status indicator lights, communication interfaces, signal analog-to-digital/digital-to-analog conversion module, and power module. The LPC1700 features a 120MHz CPU with strong internal processing power, low power consumption, and a rich array of peripheral interfaces. It converts the current signals output by the sensors into voltage signals, which then enter the 12-bit ADC within the LPC1700 to complete the analog-to-digital conversion. Interactions with the upper computer for data and control commands are achieved through the built-in MAC and PHY layers. The module is connected to debugging equipment via an integrated RS232 interface, allowing for debugging and fault location when the system encounters issues. The main control module communicates with node modules via I2C interfaces, enabling addressing and data transmission/reception. After the node modules complete measurements of individual battery voltage, temperature, and internal resistance, the main control module can poll all node modules for reading or extract data from 1 to 128 nodes individually, storing the data in the main control module's data buffer for upper computer access. Node modules are connected using an I2C interface, electrically isolating the communication port from the nodes to ensure the safety of the battery bank and featuring a time bus for triggered data sampling, enhancing the accuracy of collected data. The main control module framework is illustrated in Figure 2.
Figure 2: Master Control Module Framework
3.2 Node Module
The node module of the online monitoring solution for battery packs includes a microprocessor, I2C controller, battery cell voltage measurement circuit, battery cell internal resistance measurement circuit, and communication expansion circuit. The microprocessor uses LPC1100 Cortex-M0 with a 50MHz bus speed, supporting 30kB of on-chip Flash storage for temporarily storing collected data, ensuring data security. The serial peripheral supports high-speed I2C bus, allowing seamless connection with the main control module, and incorporates a temperature sensor that monitors the surface temperature of individual battery cells in real-time through an analog-to-digital conversion unit. The battery cell voltage measurement circuit is responsible for measuring the battery voltage signal, while the battery cell internal resistance measurement circuit is responsible for measuring the battery internal resistance signal. The node module framework is shown in Figure 3.
Figure 3 Node Module Framework
3.3Ω Resistance Measurement Module
Methods for measuring internal resistance include the density method, open-circuit voltage method, direct current discharge method, and alternating current injection method. The first three measurement methods are not suitable for measuring the internal resistance of sealed lead-acid batteries, as they have poor accuracy and can affect the battery's lifespan. The alternating current injection method does not require discharging the battery and does not impact the battery's lifespan, thus allowing for safe online measurement of the battery's internal resistance. When using the alternating current injection method, a low-frequency AC signal is injected into the battery to ensure the battery's performance, while simultaneously measuring the low-frequency AC voltage V0 between the battery's positive and negative terminals, the low-frequency AC current Is, and the phase difference α between current and voltage. Using the impedance calculation formula, the impedance Z is then calculated by the ratio of V0 to Vs.
Z=V0/Is
The internal resistance of a battery can be calculated using impedance and phase difference as R = Zcosα. It is important to note that since the internal resistance of a battery is in the milliohm range, any errors in components or irregularities in the sine wave of the AC voltage at the test terminals during the testing process can cause a significant change in the internal resistance value, leading to a decrease in measurement accuracy. Therefore, an error correction algorithm is required for correction. The correction of the battery's internal resistance is achieved by adding equivalent polarization resistance Rc and equivalent polarization capacitance C. The improved internal resistance model is shown in Figure 4.
Figure 4: Improved Internal Resistance Model
Low-frequency test current x1 was injected at both ends of the battery, resulting in a voltage drop x2 in the battery.
x1=cos(ωt)+n1(t)
x2=cos(ωt+θ)+n2(t)
θ represents the phase difference between the injected current and the output voltage.
ω — Input frequency of test current
t——time;
n1(t) —— Low-frequency Current Noise
n2(t) — Voltage noise.
Due to the white noise signal being zero within the sine wave cycle, the equation can be simplified to ∫T0|x1−x2|^2dωt = 2T - Tcosθ / 2T - Tcosθ = Aθ = arccos(2T - AT), where A represents the voltage waveform fluctuation value and T is the injected current cycle. Since an initial reference value is required for resistance measurement, data is recorded during the first measurement to serve as a basis for future detection results and to assess the health status of the battery. The noise control method eliminates the impact of fluctuations in components and AC voltage waveforms, thereby enhancing the accuracy of resistance measurement.
4. AcrelEMS-IDC Comprehensive Energy Management System for Data Centers
4.1 Platform Composition
Ankorree Electrical stays abreast of data center energy efficiency, resource utilization, and availability, enhancing operational efficiency and reducing maintenance costs.
The AcrelEMS data center's energy management offers monitoring and control, primarily categorized into power monitoring, environmental and physical monitoring, energy consumption statistics analysis (energy management), battery monitoring, distribution monitoring, intelligent busbar monitoring, intelligent lighting, and fire-related subsystems.
4.2 Platform Topology Diagram
4.3 Battery Monitoring System
Battery Pack
Battery packs are commonly used as a supplement to UPS power, providing extended backup power to support data centers during power outages when diesel generators are unable to supply electricity.
4.3.2 Battery Pack Classification
Data centers are increasingly being replaced by lithium batteries for their applications. When selecting battery banks, it's crucial to choose the appropriate battery type based on the requirements of the application scenario and budget.
4.3.3 Battery Pack Single Connection Diagram
Battery packs in data centers are typically composed of a certain number of batteries connected in series and wired to the UPS power system. Wiring should adhere to safe and reliable principles to ensure the normal operation and longevity of the battery pack. In the event of a main power failure or blackout, the UPS power system will automatically switch to the battery backup power, ensuring continuous system operation.
4.3.4 Battery Pack Monitoring Requirements and Key Equipment Selection
Battery packs play a critical role in data center UPS power systems, and thus require monitoring to ensure proper operation and extend their lifespan. Here are some common requirements for battery pack monitoring:
Battery Pack Status Monitoring: Includes monitoring parameters such as voltage, current, temperature, and capacity to gain real-time insights into the operational status of the battery pack.
Battery Pack Remaining Life Prediction: By monitoring the operational state and life indicators of the battery pack, predict its remaining life, and proactively perform maintenance or replacement to prevent the battery pack from failing, which could lead to the failure of the UPS power system.
Automated Testing and Inspections: Regularly conduct automated testing and inspections of battery packs to identify potential faults and anomalies, ensuring timely resolution.
Alarm and Warning Function: In the event of abnormality or failure in the battery pack, notify operations personnel through alarms and warnings to address the issue promptly, preventing accidents from occurring.
Data Analysis and Recording: Analyzing and recording battery pack data allows for understanding the historical operational status of the battery pack, providing data support for optimized management and maintenance.
The battery monitoring system is primarily composed of the S module, C module, and the HS collector.
6. Summary
The online battery monitoring solution collects real-time voltage, charging/discharging current, internal resistance, temperature, and battery capacity through the master control module and various node collection modules, automatically submitting the results to the upper computer and data center, thereby enabling online monitoring of substation battery status.
References
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