A flow battery precision analog measurement system
By employing multi-channel signal acquisition and intelligent data processing technologies, combined with adaptive precision adjustment and high-speed communication, the problems of low accuracy, poor real-time performance, and data delay in flow battery monitoring systems have been solved, resulting in a high-precision, real-time, and predictive maintenance flow battery measurement system.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Utility models(China)
- Current Assignee / Owner
- THREE GORGES NEW ENERGY JIMUSAR POWER GENERATION CO LTD
- Filing Date
- 2025-05-20
- Publication Date
- 2026-05-29
AI Technical Summary
Existing flow battery monitoring systems cannot perform accurate measurements under varying operating conditions. Their sensors have low accuracy and are susceptible to noise interference. They also suffer from severe data transmission delays and lack adaptive accuracy adjustment and intelligent analysis functions, making it difficult to achieve high-precision, real-time, and predictive maintenance.
It employs a multi-channel high-precision acquisition module, signal conditioning module, data processing unit, communication module, and cloud platform monitoring module. Combining high-precision analog-to-digital conversion, filtering, low-noise amplification, Kalman filtering algorithm, dynamic filtering depth algorithm, and intelligent data fusion technology, it achieves parallel processing and adaptive precision adjustment of multi-channel signals, and ensures real-time performance through high-speed data bus and wireless communication.
The system achieves high precision, low error, real-time performance, and adaptability in flow battery measurement, enabling accurate acquisition of key parameters under dynamic conditions, reducing noise interference, minimizing data latency, providing predictive maintenance, and improving system stability and reliability.
Smart Images
Figure CN224303814U_ABST
Abstract
Description
Technical Field
[0001] This utility model belongs to the field of energy storage measurement technology, specifically relating to a precise analog quantity measurement system for flow batteries. Background Technology
[0002] Existing flow battery monitoring systems typically measure analog quantities such as battery voltage, current, and temperature using single-channel sensors. Each sensor acquires data via an independent analog-to-digital converter (ADC) and transmits it to an embedded control unit. Single-channel systems struggle to process multiple sensor signals simultaneously and cannot effectively perform accurate measurements and data fusion under varying operating conditions.
[0003] Moreover, the sensors have low accuracy and are easily affected by noise, especially during battery operation when the current changes drastically, traditional sensors often cannot provide sufficient accuracy.
[0004] On the other hand, when using serial communication such as RS485 or Modbus to transmit data to a host computer or monitoring platform, this method may cause data delays when the data volume is large and frequent transmissions are required, and the system has high requirements for real-time performance and bandwidth.
[0005] Existing flow battery measurement systems generally lack adaptive accuracy adjustment capabilities. In varying operating environments, these systems cannot automatically adjust sensor accuracy to adapt to different battery states. Furthermore, existing systems typically lack intelligent algorithms, failing to perform in-depth analysis or optimization of real-time data, thus hindering predictive maintenance or condition identification.
[0006] For example, in the CN202523800U multifunctional zinc-bromine flow battery stack test device, multiple signals are acquired, but each signal uses a single channel, lacking reference and comparison, which leads to a loss of accuracy.
[0007] For example, in CN218975489U, a full-condition control system for vanadium redox flow batteries, when the load changes significantly, the electromagnetic interference caused by the rapid change in current and its impact on the precise control of the frequency converter are not considered, thus resulting in errors.
[0008] Therefore, developing a high-precision, high-real-time, and low-noise analog measurement system for flow batteries has significant engineering value and application prospects. Utility Model Content
[0009] To address the shortcomings of existing technologies, a precise analog quantity measurement system for flow batteries is provided, comprising a multi-channel high-precision acquisition module, a signal conditioning module, a data processing unit, a communication module, and a cloud platform monitoring module. The pressure sensor, temperature sensor, flow sensor, current sensor, voltage sensor, and reference battery in the flow battery circuit are connected to the signal conditioning module through the multi-channel high-precision acquisition module. The signal conditioning module, data processing unit, communication module, and cloud platform monitoring module are sequentially connected for communication.
[0010] Furthermore, the multi-channel high-precision acquisition module uses the ADS8344 chip, which processes multiple sensor signals simultaneously through multi-channel parallel acquisition and outputs SPI format data.
[0011] Furthermore, the output SPI format data is connected to the data processing unit through an isolation module circuit.
[0012] Furthermore, the signal conditioning module includes a high-precision analog-to-digital converter (ADC), a filter circuit, and a low-noise amplifier. The ADC, filter circuit, and low-noise amplifier are connected in sequence. The analog signal is converted into a voltage signal by a parallel resistor in the ADC. The signal is filtered out by a low-pass filter in the filter circuit to remove high-frequency interference signals. The signal is then amplified by the operational amplifier circuit of the low-noise amplifier, and finally further amplified by a second-stage operational amplifier circuit.
[0013] Furthermore, the data processing unit employs an embedded microprocessor MCU (STM32F429).
[0014] Furthermore, the communication module adopts two communication methods: high-speed data bus CAN FD and Ethernet. It uses CAN FD for internal system communication and Ethernet for external communication, uploading data to the cloud platform monitoring module.
[0015] Furthermore, the cloud platform monitoring module includes a data storage module, a status assessment module, and a fault early warning module.
[0016] The beneficial effects of this utility model are:
[0017] High precision and low error: This invention utilizes multi-channel high-precision sensors and advanced intelligent signal conditioning to accurately acquire key parameters of flow batteries under dynamic operating conditions. Furthermore, it employs an intelligent data fusion algorithm to eliminate crosstalk and errors from multi-channel signals, thereby ensuring high measurement accuracy. For example, in the CN202523800U multifunctional zinc-bromine flow battery stack testing device, multiple signals are acquired, but each signal uses a single channel, lacking reference and comparison, resulting in a loss of accuracy. This invention, however, uses a multi-channel acquisition chip combined with multi-channel data fusion and weighted averaging algorithms, significantly improving data reliability and accuracy.
[0018] High real-time performance: By adopting a high-speed data bus and wireless communication module, this invention can meet the real-time monitoring requirements of flow batteries, minimize the delay in data transmission, and ensure strong real-time and efficient data processing capabilities.
[0019] Adaptive Precision and Intelligent Adjustment: The adaptive precision adjustment module can automatically adjust the sampling precision according to the battery's operating state, ensuring the system's high dynamic response capability while effectively reducing power consumption and improving the system's economy and reliability. For example, in CN218975489U, a vanadium redox flow battery full-condition control system, when the load changes significantly, the electromagnetic interference caused by the rapid change in current and its impact on the precise control of the frequency converter are not considered, thus resulting in errors. This invention, however, fully considers the interference caused by external factors and effectively suppresses errors by adjusting the dynamic filtering depth.
[0020] Intelligent Analysis and Predictive Maintenance: By combining AI algorithms for anomaly detection and historical data storage, the system can monitor the operating status of the flow battery in real time, detect potential faults early, and issue alerts, thus enabling predictive maintenance. This function not only reduces the occurrence of sudden failures but also significantly extends the lifespan of the flow battery.
[0021] High system integration and modular design: This utility model adopts a modular design, which not only ensures the independence of each functional module, but also allows for flexible adjustment of the system composition according to actual needs. At the same time, by integrating sensors, conditioning circuits, data processing units, and other technologies, the system improves the overall performance of the flow battery monitoring system, simplifies hardware design, and reduces development costs. Attached Figure Description
[0022] Figure 1 This is a general framework diagram of the flow battery precision analog quantity measurement system of this utility model;
[0023] Figure 2 This is a schematic diagram of the signal conditioning circuit of this utility model;
[0024] Figure 3This is a schematic diagram of the isolation module circuit of this utility model;
[0025] Figure 4 This is a circuit diagram of the multi-channel acquisition module of this utility model;
[0026] Figure 5 This is a schematic diagram of the Ethernet communication module circuit of this utility model;
[0027] Figure 6 This is a schematic diagram of the CAN_FD communication module circuit of this utility model.
[0028] 1. Multi-channel high-precision acquisition module; 2. Signal conditioning module; 21. High-precision analog-to-digital converter (ADC); 22. Filtering circuit; 23. Low-noise amplifier; 3. Data processing unit; 4. Communication module; 5. Cloud platform monitoring module. Detailed Implementation
[0029] A precise analog measurement system for flow batteries, such as Figure 1 As shown, the system includes a multi-channel high-precision acquisition module 1, a signal conditioning module 2, a data processing unit 3, a communication module 4, and a cloud platform monitoring module 5. The pressure sensor, temperature sensor, flow sensor, current sensor, voltage sensor, and reference battery in the flow battery circuit are connected to the signal conditioning module 2 through the multi-channel high-precision acquisition module 1. The signal conditioning module 2, data processing unit 3, communication module 4, and cloud platform monitoring module 5 are connected in sequence for communication.
[0030] Among them, such as Figure 4 As shown, the multi-channel high-precision acquisition module 1 uses the ADS8344 chip, which processes multiple sensor signals simultaneously through multi-channel parallel acquisition and outputs SPI format data.
[0031] Among them, such as Figure 3 As shown, the output SPI format data is connected to the data processing unit 3 through an isolation module circuit, and the isolator of the isolation module circuit is an ADuM1401BRWZ.
[0032] Among them, such as Figure 2 As shown, the signal conditioning module 2 includes a high-precision analog-to-digital converter (ADC) 21, a filter circuit 22, and a low-noise amplifier 23. The ADC 21, filter circuit 22, and low-noise amplifier 23 are connected in sequence. The analog signal is converted into a voltage signal by a parallel resistor in the ADC 21. The signal is filtered by a low-pass filter in the filter circuit 22 to remove high-frequency interference signals. The signal is then amplified by the operational amplifier circuit of the low-noise amplifier 23. Finally, the signal is further amplified by the operational amplifier circuit of the second stage.
[0033] The data processing unit 3 uses an embedded microprocessor MCU (STM32F429).
[0034] Among them, such as Figures 5-6 As shown, the communication module 4 adopts two communication methods: high-speed data bus CANFD and Ethernet. It uses CANFD for internal system communication and Ethernet for external communication, uploading data to the cloud platform monitoring module.
[0035] The cloud platform monitoring module 5 includes a data storage module, a status assessment module, and a fault early warning module.
[0036] Multi-channel high-precision acquisition module 1: This module is responsible for acquiring key parameters of the flow battery, such as voltage, current, temperature, flow rate, and pressure. It employs a multi-channel parallel acquisition method, enabling simultaneous processing of signals from multiple sensors. The high-precision, low-drift acquisition module ensures accurate measurements under the battery's dynamic operating conditions. This module also possesses strong anti-interference capabilities, effectively preventing the influence of external noise on the measurement data and improving the overall stability of the system.
[0037] Signal Conditioning Module 2: This module includes a low-noise amplifier, a filter circuit, and a high-precision analog-to-digital converter (ADC) for precise amplification, filtering, and sampling of multi-channel signals. The low-noise amplifier effectively improves the signal-to-noise ratio of weak signals, while the filter circuit removes high-frequency noise, ensuring data accuracy. The high-precision ADC ensures accurate conversion of analog signals to digital signals while reducing errors during the conversion process. Multiple channel signals can be acquired in parallel, avoiding crosstalk problems found in traditional single-channel systems and guaranteeing high data fidelity.
[0038] Data Processing Unit 3: Based on an embedded microprocessor, Data Processing Unit 3 employs Kalman filtering, dynamic filtering depth algorithm, and intelligent data fusion technology to enhance the system's real-time data processing capabilities. The Kalman filtering algorithm dynamically adjusts filtering parameters in real time to adapt to different working environments and measurement requirements, ensuring signal accuracy. The dynamic filtering depth algorithm automatically identifies and corrects measurement errors caused by factors such as battery state changes and environmental interference. When the battery is under low load, the system can reduce sampling accuracy to reduce power consumption; while under high load or during rapid charging and discharging, the system automatically increases sampling accuracy to ensure measurement accuracy. Intelligent data fusion technology comprehensively analyzes signals collected by multiple sensors, improving the overall measurement accuracy of the system.
[0039] Communication Interface Module 4: This invention employs high-speed data bus CAN FD and Ethernet technology to solve the data latency and bandwidth bottlenecks that may occur in traditional RS485 or Modbus communication. The high-speed data bus enables low-latency, high-bandwidth data transmission, adapting to the real-time transmission requirements of large data volumes in flow batteries, ensuring that monitoring data can be quickly and losslessly transmitted to the host computer or monitoring platform.
[0040] Cloud platform monitoring module 5: The storage module is responsible for saving historical data for subsequent analysis and trend prediction. The anomaly monitoring module, combined with AI algorithms, monitors the operating status of the flow battery in real time, promptly detecting potential anomalies or malfunctions and issuing alerts to operators through the early warning system. This module not only improves system reliability but also provides strong support for predictive maintenance, preventing unpredictable downtime or damage to the battery due to malfunctions.
[0041] This utility model discloses a precise analog measurement system for flow batteries. By optimizing multi-channel signal acquisition, intelligent data processing, and adaptive accuracy adjustment, it significantly improves the measurement accuracy, stability, and real-time performance of the flow battery monitoring system.
[0042] like Figure 1 This is the overall schematic diagram of a flow battery precision analog quantity measurement system. The system consists of a flow battery, sensors, signal conditioning circuits, a data processing unit, a communication module, and a data analysis platform. First, the system converts traditional physical quantity signals into 4-20mA analog signals using various sensors, signal transmitters, and PT100. The signals undergo conversion, filtering, and amplification by hardware conditioning circuits before being input to the acquisition chip. The acquisition chip communicates with the STM32F429 platform via SPI. The main control chip acquires the data and performs software processing, including algorithms such as Kalman filtering, multi-channel data fusion, and weighted averaging. The processed data is then transmitted to other internal systems via CAN_FD communication and to a host system via Ethernet. The remote monitoring platform can also remotely acquire data via the external network. The cloud platform enables data storage, analysis of historical data, and state estimation, preventing system downtime due to fault alarms.
[0043] like Figure 2 This is a schematic diagram of a signal conditioning circuit. This circuit achieves accurate acquisition of raw data and filters out high-frequency interference signals. First, the analog signal is converted from a current signal to a voltage signal through a parallel resistor. The signal then passes through a low-pass filter to filter out high-frequency interference signals, and is amplified in the first stage by an operational amplifier circuit. Finally, the signal is further amplified by a second operational amplifier circuit to improve signal quality.
[0044] like Figure 4This is a circuit diagram of a multi-channel acquisition module. The amplified signal enters the ADS8344 multi-channel acquisition chip, which can simultaneously acquire 8 channels of analog signals, providing basic data for subsequent multi-channel fusion algorithms in the software layer. This chip ultimately outputs data in SPI format.
[0045] like Figure 3 The diagram shows the circuit of the isolation module. The output SPI format data is transmitted to the MCU through the ADuM1401BRWZ isolation chip to isolate the SPI signal and provide 2.5kV RMS high voltage isolation to prevent ground potential difference interference and improve signal anti-interference capability and safety.
[0046] Analog signals are acquired by external circuitry and chip and then enter the MCU. The Kalman filter module continuously performs prediction and update steps to smooth and accurately measure the data, reducing noise interference. Because signals are highly susceptible to interference during high-power charging and discharging, the filter depth is dynamically adjusted based on the system's operating power status to ensure a balance between filter strength and MCU load. Finally, multi-channel data is fused and a weighted average algorithm is applied to output the analog data. The final data can be used for internal system communication via CAN_FD, as described in the following principle... Figure 5 It can also communicate externally via Ethernet and upload data to a data analysis platform, the principle of which is as follows: Figure 6 The platform records raw data and performs status prediction and fault alarms to ensure stable system operation.
[0047] The above description is only a preferred embodiment of the present utility model. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present utility model, and these improvements and modifications should also be considered within the protection scope of the present utility model.
Claims
1. A precise analog quantity measurement system for flow batteries, characterized in that, It includes a multi-channel high-precision acquisition module (1), a signal conditioning module (2), a data processing unit (3), a communication module (4), and a cloud platform monitoring module (5). The pressure sensor, temperature sensor, flow sensor, current sensor, voltage sensor, and reference battery in the flow battery circuit are connected to the signal conditioning module (2) through the multi-channel high-precision acquisition module (1). The signal conditioning module (2), data processing unit (3), communication module (4), and cloud platform monitoring module (5) are connected in sequence for communication.
2. The flow battery precise analog quantity measurement system according to claim 1, characterized in that, The multi-channel high-precision acquisition module (1) uses the ADS8344 chip, which processes multiple sensor signals simultaneously through multi-channel parallel acquisition and outputs SPI format data.
3. The flow battery precise analog quantity measurement system according to claim 2, characterized in that, The output SPI format data is connected to the data processing unit (3) through an isolation module circuit, and the isolator of the isolation module circuit is ADuM1401BRWZ.
4. The flow battery precise analog quantity measurement system according to claim 1, characterized in that, The signal conditioning module (2) includes a high-precision analog-to-digital converter (ADC) (21), a filter circuit (22), and a low-noise amplifier (23). The high-precision analog-to-digital converter (ADC) (21), the filter circuit (22), and the low-noise amplifier (23) are connected in sequence. The analog signal is converted into a voltage signal by the parallel resistor of the high-precision analog-to-digital converter (ADC) (21). The signal is filtered out by the low-pass filter of the filter circuit (22) to remove high-frequency interference signals. The signal is amplified by the operational amplifier circuit of the low-noise amplifier (23) for one stage. Finally, the signal is amplified by the operational amplifier circuit for two stages.
5. The flow battery precise analog quantity measurement system according to claim 3, characterized in that, The data processing unit (3) adopts an embedded microprocessor MCU, model STM32F429.
6. The flow battery precise analog quantity measurement system according to claim 5, characterized in that, The communication module (4) adopts two communication methods: high-speed data bus CAN FD and Ethernet. It uses CAN FD for internal system communication and Ethernet for external communication, uploading data to the cloud platform monitoring module.