Active equalization device and method for storage battery of backup power supply of distribution network

Abstract

The invention discloses an active equalization device for a distribution network backup power supply storage battery, and the device comprises an unbalance detection module which is used for monitoring the voltage, internal resistance and temperature of each single battery in a storage battery pack in real time, and judging whether the battery pack is in an unbalance state or not; the energy transfer module is connected with the unbalance detection module and is used for quickly transferring energy among the battery packs through an energy transfer circuit based on a switched capacitor according to a judgment result of the unbalance detection module; and the open circuit state prediction algorithm module is used for collecting and analyzing historical data and real-time data of the battery pack, predicting the open circuit state of the battery pack and outputting early warning information. The invention further discloses a corresponding method. By implementing the invention, rapid energy transfer between the battery packs can be realized, open-circuit faults are effectively prevented, and the working efficiency and the service life of the battery packs are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power management, and in particular to an active balancing device and method for a distribution network backup power supply battery. Background Art

[0002] In the distribution network system, the backup power battery is a key component to ensure the uninterrupted operation of the system. Lead-acid batteries are widely used in the field of backup power due to their mature technology and low cost. However, the battery pack is prone to imbalance in voltage, capacity and internal resistance during use, resulting in overall performance degradation and even causing problems such as overcharging and over-discharging, which seriously affects the service life of the battery and the reliability of the system.

[0003] In the prior art, battery balancing technology is mainly divided into passive balancing and active balancing. Passive balancing dissipates excess energy through resistors. Although it has a simple structure, it has high energy consumption and low efficiency. In contrast, active balancing achieves effective utilization of energy between batteries through energy transfer units, improves balancing efficiency, and is more suitable for backup power systems with long life and high stability. However, traditional active balancing solutions still have shortcomings in balancing speed, control complexity, and fault prevention.

[0004] Specifically, the traditional active balancing method based on switched capacitors, although compact in structure and easy to control, has a slow balancing speed when the battery pack size increases, and it is difficult to meet the demand for fast balancing, which is particularly prominent in occasions such as distribution network backup power supply that require instant response. In addition, the active balancing system in the prior art often lacks an effective prevention mechanism for the open circuit state of the battery, resulting in uneven battery performance due to self-discharge and other reasons when the battery pack is not working, thereby reducing the overall performance. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide an active balancing device for distribution network backup power supply batteries, which can realize rapid energy transfer between battery packs, effectively prevent open circuit failures, and improve the working efficiency and service life of the battery pack.

[0006] In order to solve the above technical problems, as a first aspect of the present invention, an active balancing device for a distribution network backup power supply battery is provided, which comprises:

[0007] The unbalance detection module is used to monitor the voltage, internal resistance and temperature of each single cell in the battery pack in real time and determine whether the battery pack is in an unbalanced state;

[0008] An energy transfer module is connected to the unbalance detection module, and realizes rapid energy transfer between battery packs through an energy transfer circuit based on a switch capacitor according to the judgment result of the unbalance detection module;

[0009] The open circuit state prediction algorithm module is used to collect and analyze the historical data and real-time data of the battery pack, predict the open circuit state of the battery pack, and output warning information.

[0010] Optionally, the imbalance detection module further includes an analog-to-digital converter and a data processing unit, wherein:

[0011] The analog-to-digital converter is used to convert the monitored analog signal into a digital signal;

[0012] The data processing unit is used to perform calculations, comparisons and machine learning on the digital signals to more accurately determine the imbalance state of the battery pack.

[0013] Optionally, the energy transfer module also includes overvoltage protection, overcurrent protection and short circuit protection circuits to ensure the safety and stability of the energy transfer process.

[0014] Optionally, the open circuit state prediction algorithm module adopts a deep learning neural network, which is trained by collecting a large amount of historical data and real-time data to improve the accuracy and reliability of open circuit state prediction; the deep learning neural network includes:

[0015] A data collection unit, used to collect real-time data and historical data of the battery pack;

[0016] Data processing unit, used to clean, organize and analyze data;

[0017] Predictive models to predict the risk of open circuit failures based on data;

[0018] Output interface, used to output prediction results and warning information to operation and maintenance personnel.

[0019] Optionally, the energy transfer circuit of the energy transfer module adopts an efficient switch capacitor design, including an inductor, a switch device and a photocoupler, to achieve fast and efficient transfer of energy between batteries.

[0020] Accordingly, as another aspect of the present invention, there is also provided an active balancing method for a distribution network backup power supply battery, which comprises the following steps:

[0021] Real-time monitoring of the voltage, internal resistance and temperature of each single cell in the battery pack;

[0022] Determine whether the battery pack is in an unbalanced state based on the monitoring data;

[0023] When it is determined to be unbalanced, the energy transfer process is started, and the excess energy of the high-capacity battery is transferred to the low-capacity battery through the energy transfer circuit based on the switched capacitor;

[0024] Collect and analyze historical and real-time data of the battery pack, predict the open circuit status of the battery pack, and output early warning information.

[0025] Optionally, the step of determining whether the battery pack is in an unbalanced state further includes:

[0026] The voltage of each single cell is measured by a precision voltage detection circuit, the operating temperature of each single cell is monitored by a temperature sensor, and the internal resistance of each single cell is detected by an internal resistance detection circuit;

[0027] Preprocess and extract features of monitoring data;

[0028] A machine learning algorithm is used in the control operation circuit to determine the imbalance state of the battery pack.

[0029] Optionally, during the energy transfer process, the energy transfer circuit is also provided with steps of overvoltage protection, overcurrent protection and short circuit protection to ensure the safety and stability of the energy transfer.

[0030] Optionally, the step of predicting the open circuit state of the battery pack includes:

[0031] Collect real-time and historical data of battery packs;

[0032] Clean, organize and analyze data;

[0033] Use the trained deep learning neural network model to predict the risk of open circuit faults;

[0034] Generate early warning information based on the prediction results.

[0035] Optionally, after outputting the warning information, the method also includes taking maintenance and adjustment measures in a timely manner according to the warning information to avoid failures and ensure stable operation of the distribution network backup power supply.

[0036] The implementation of the embodiments of the present invention has the following beneficial effects:

[0037] The present invention proposes an active balancing device and method for a distribution network backup power supply battery. The device can realize active energy transfer between battery packs, avoid overcharging or over-discharging of a single battery, extend the battery life, and help prevent the occurrence of open circuit failures. It can not only improve the working efficiency and service life of the battery pack, but also reduce maintenance costs, meeting the requirements of high reliability and high stability of modern distribution networks.

[0038] Specifically, in this embodiment, the active balancing technology is used to achieve rapid energy transfer between battery packs, significantly improving the balancing efficiency. It can effectively prevent problems such as overcharging, over-discharging and imbalance, and extend the service life of the battery. The open circuit state prediction algorithm can be used to detect potential faults in advance, and maintenance and adjustment measures can be taken in time according to the early warning information, thereby improving the reliability and safety of the system. It can reduce the downtime and maintenance costs caused by battery failure and improve the economic benefits of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, without paying creative labor, other drawings obtained based on these drawings still belong to the scope of the present invention.

[0040] Figure 1 A schematic diagram of the structure of an embodiment of an active balancing device for backup batteries in a distribution network provided by the present invention;

[0041] Figure 2 for Figure 1 The structural diagram of the imbalance detection module;

[0042] Figure 3 for Figure 1 Schematic diagram of the structure of the energy transfer module;

[0043] Figure 4 A schematic diagram of the main process of an embodiment of a method for active balancing of a distribution network backup battery provided by the present invention;

[0044] Figure 5 It is a schematic diagram of the process flow of the open circuit state prediction algorithm involved in the present invention. DETAILED DESCRIPTION

[0045] In order to make the objectives, technical solutions and advantages of the present invention more clear, the present invention will be further described in detail below with reference to the accompanying drawings.

[0046] like Figure 1 FIG. 1 is a schematic diagram showing a structure of an embodiment of an active balancing device for a backup battery in a distribution network provided by the present invention; and FIG. Figure 2 and Figure 3 As shown, in this embodiment, the distribution network backup battery active balancing device 1 at least includes:

[0047] The unbalance detection module 10 is used to monitor the voltage, internal resistance and temperature of each single cell in the battery pack in real time, and determine whether the battery pack is in an unbalanced state; it can be understood that through the unbalance detection module 10, the power operation status data of each single cell in the battery pack can be monitored in real time, including but not limited to the voltage, internal resistance and temperature of the battery.

[0048] The energy transfer module 11 is connected to the unbalanced detection module. According to the judgment result of the unbalanced detection module, the energy transfer circuit based on the switched capacitor is used to realize the rapid transfer of energy between battery packs. Specifically, when an unbalanced battery is detected in the battery pack, the energy transfer module is controlled to start, and the excess energy of the high-power battery is transferred to the low-power battery by using the efficient energy transfer circuit. The module allocates energy by setting the control logic of balancing the battery with the highest internal resistance or the lowest voltage first, and synchronously monitors the test feedback data during the energy transfer process to avoid overcharging or other potential risks.

[0049] The open circuit state prediction algorithm module 12 is used to collect and analyze the historical data and real-time data of the battery pack, predict the open circuit state of the battery pack, and output warning information. Specifically, the open circuit state prediction collects real-time data and combines it with past historical data to judge the health status and possible risks of the battery pack, implement the open circuit state prediction algorithm, conduct risk assessment on potential open circuit faults, and predict possible faults to ensure that the operation and maintenance personnel take maintenance and adjustment measures in time according to the warning information to avoid faults and ensure the stable operation of the distribution network backup power supply.

[0050] Optionally, the unbalanced detection module 10 includes: a precision voltage detection circuit 100, a temperature sensor 101, an internal resistance detection circuit 102, and a control operation circuit 103. The precision voltage detection circuit 100, the temperature sensor 101, and the internal resistance detection circuit 102 complete the real-time monitoring of the operating status data of each single cell of the battery pack, including but not limited to various status parameters such as voltage, temperature, and internal resistance change. The control operation circuit 103 also includes an analog-to-digital converter and a data processing unit. First, the monitored analog signal is converted into a digital signal through the analog-to-digital converter, and then the digital signal processing is performed to remove noise and abnormal values, extract the features and labels of valid data, and finally the data processing unit performs calculations and comparisons on the valid data, and uses the random forest method to perform data mining and machine learning, and realizes cross-validation and hyperparameter adjustment optimization verification to determine whether there is an unbalanced battery.

[0051] like Figure 3As shown, the energy transfer module 11 also includes overvoltage protection, overcurrent protection and short-circuit protection circuits to ensure the safety and stability of the energy transfer process. More specifically, the core of the energy transfer module 11 is a high-efficiency energy transfer circuit based on a switched capacitor, including an inductor unit 110, a photocoupler 111 and a switch device 112. The inductor unit 110 and the switch device 111 (field effect transistor MOS tube) quickly transfer energy between single cells to achieve active balancing; each single cell quickly switches between the inductor unit 110 and the switch device unit 111 connected to it, transferring excess energy from the battery with higher power to the battery with lower power, thereby achieving a balancing effect; the photocoupler 112 realizes the output of the status signal, completing the simple implementation of the human-computer interaction signal.

[0052] Optionally, the open circuit state prediction algorithm module 12 adopts a deep learning neural network, and is trained by collecting a large amount of historical data and real-time data to improve the accuracy and reliability of the open circuit state prediction; the deep learning neural network. It can be understood that in the embodiment of the present invention, the open circuit state prediction algorithm module 12 is based on the deep learning neural network structure framework design, learns and analyzes historical data and real-time data, performs training on a large amount of data, predicts the open circuit fault that may occur in the battery pack, and finally outputs the result through the output interface, and finally completes the risk assessment and fault prediction information reporting to the operation and maintenance personnel.

[0053] In one embodiment, the open circuit state prediction algorithm module 12 may include the following functional modules:

[0054] A data collection unit, used to collect real-time data and historical data of the battery pack;

[0055] Data processing unit, used to clean, organize and analyze data;

[0056] Predictive models to predict the risk of open circuit failures based on data;

[0057] Output interface, used to output prediction results and warning information to operation and maintenance personnel.

[0058] It can be understood that the active balancing device provided by the present invention can significantly improve the efficiency of the balancing device, and use active balancing methods instead of passive balancing to reduce energy loss and speed up the balancing speed; secondly, it prevents overcharging and over-discharging problems caused by inconsistent battery packs, and extends the overall service life of lead-acid batteries; in addition, through the open circuit state prediction algorithm, the power system's early warning capability for potential faults is enhanced, and the reliability and safety of the distribution network backup power supply are improved.

[0059] The device provided by the present invention has been implemented, through actual device application and multiple sets of data experimental verification. By comprehensively improving and optimizing the balancing efficiency, balancing speed and control complexity, it not only significantly improves the balancing speed and overall performance of the battery pack, but also effectively prevents open circuit failures, and provides an effective protection and optimization solution for the distribution network backup power supply. It has better solved many deficiencies found in the prior art and is not only suitable for the field of distribution network backup power supply, but also in the fields of new energy, artificial satellites, uninterruptible power supplies, etc. that require a large number of lead-acid batteries. It helps to promote further research and development of battery active balancing technology.

[0060] like Figure 4 FIG. 1 is a schematic diagram showing a main flow chart of an embodiment of an active balancing method for a distribution network backup power supply battery provided by the present invention. In this embodiment, the method comprises the following steps:

[0061] Step S1, real-time monitoring of the voltage, internal resistance and temperature of each single cell in the battery pack;

[0062] Step S2, judging whether the battery pack is in an unbalanced state according to the monitoring data;

[0063] In a specific example, the step of determining whether the battery pack is in an unbalanced state further includes:

[0064] The voltage of each single cell is measured by a precision voltage detection circuit, the operating temperature of each single cell is monitored by a temperature sensor, and the internal resistance of each single cell is detected by an internal resistance detection circuit;

[0065] Preprocess and extract features of monitoring data;

[0066] A machine learning algorithm is used in the control operation circuit to determine the imbalance state of the battery pack.

[0067] Step S3, when it is determined to be unbalanced, the energy transfer process is started, and the excess energy of the high-capacity battery is transferred to the low-capacity battery through the energy transfer circuit based on the switch capacitor;

[0068] In a specific example, during the energy transfer process, the energy transfer circuit is also protected against overvoltage, overcurrent and short circuit to ensure the safety and stability of the energy transfer.

[0069] Step S4, collecting and analyzing historical data and real-time data of the battery pack, predicting the open circuit state of the battery pack, and outputting warning information.

[0070] In a specific example, the step of predicting the open circuit state of the battery pack includes:

[0071] Collect real-time and historical data of battery packs;

[0072] Clean, organize and analyze data;

[0073] Use the trained deep learning neural network model to predict the risk of open circuit faults;

[0074] Generate early warning information based on the prediction results.

[0075] More specifically, after outputting the warning information, it also includes the step of taking maintenance and adjustment measures in time according to the warning information to avoid failures and ensure the stable operation of the distribution network backup power supply.

[0076] For more details, please refer to and combine the above Figures 1 to 3 The description is not repeated here.

[0077] like Figure 5 FIG. 1 is a more detailed schematic diagram of an application flow of an embodiment of the method provided by the present invention. Specifically, the following steps are included:

[0078] Build an application scenario, construct a battery test group in different states (charging, discharging, and static), connect the balancing device, and then perform continuous charge and discharge cycles on the test group, while collecting battery data and balancing device operation data.

[0079] Design deep learning neural network structure framework;

[0080] Study and analyze historical data and real-time data; including training a large amount of data to obtain a trained deep learning neural network. The data analysis in this step focuses on analyzing the balancing ability, energy loss, and improvement in the battery cell's power consistency, evaluating whether the device can effectively prevent over-discharge or over-charging, and its performance in extending the battery life. At the same time, the balancing control strategy is optimized, and the accuracy of the balancing control strategy, circuit design, and prediction algorithm are continuously optimized based on test results and feedback information;

[0081] Use trained deep learning neural networks to predict possible open circuit faults in battery packs;

[0082] Complete risk assessment and fault prediction and report the results to operation and maintenance personnel through the output interface;

[0083] This will ultimately achieve online active balancing of the distribution network backup power supply batteries, effectively optimize the use efficiency of the distribution network backup power supply batteries, improve battery performance and reduce the risk of potential failures, which is of significant significance to ensuring the stable operation of the power system.

[0084] The implementation of the embodiments of the present invention has the following beneficial effects:

[0085] The present invention proposes an active balancing device and method for a distribution network backup power supply battery. The device can realize active energy transfer between battery packs, avoid overcharging or over-discharging of a single battery, extend the battery life, and help prevent the occurrence of open circuit failures. It can not only improve the working efficiency and service life of the battery pack, but also reduce maintenance costs, meeting the requirements of high reliability and high stability of modern distribution networks.

[0086] Specifically, in this embodiment, the active balancing technology is used to achieve rapid energy transfer between battery packs, significantly improving the balancing efficiency. It can effectively prevent problems such as overcharging, over-discharging and imbalance, and extend the service life of the battery. The open circuit state prediction algorithm can be used to detect potential faults in advance, and maintenance and adjustment measures can be taken in time according to the early warning information, thereby improving the reliability and safety of the system. It can reduce the downtime and maintenance costs caused by battery failure and improve the economic benefits of the system.

[0087] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, devices, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0088] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0089] The above disclosure is only a preferred embodiment of the present invention, which certainly cannot be used to limit the scope of the present invention. Therefore, equivalent changes made according to the claims of the present invention are still within the scope of the present invention.

Claims

1. An active balancing device for a distribution network backup power battery, characterized in that: include: The unbalance detection module is used to monitor the voltage, internal resistance and temperature of each single cell in the battery pack in real time and determine whether the battery pack is in an unbalanced state; An energy transfer module is connected to the unbalance detection module, and realizes rapid energy transfer between battery packs through an energy transfer circuit based on a switch capacitor according to the judgment result of the unbalance detection module; The open circuit state prediction algorithm module is used to collect and analyze the historical data and real-time data of the battery pack, predict the open circuit state of the battery pack, and output warning information.

2. The active balancing device for the distribution network backup power supply battery according to claim 1, characterized in that: The imbalance detection module also includes an analog-to-digital converter and a data processing unit, wherein: The analog-to-digital converter is used to convert the monitored analog signal into a digital signal; The data processing unit is used to perform calculations, comparisons and machine learning on the digital signals to more accurately determine the imbalance state of the battery pack.

3. The active balancing device for the distribution network backup power supply battery according to claim 2, characterized in that: The energy transfer module also includes overvoltage protection, overcurrent protection and short circuit protection circuits to ensure the safety and stability of the energy transfer process.

4. The active balancing device for the distribution network backup power supply battery according to claim 3, characterized in that: The open circuit state prediction algorithm module adopts a deep learning neural network and is trained by collecting a large amount of historical data and real-time data to improve the accuracy and reliability of open circuit state prediction; The deep learning neural network comprises: A data collection unit, used to collect real-time data and historical data of the battery pack; Data processing unit, used to clean, organize and analyze data; Predictive models to predict the risk of open circuit failures based on data; Output interface, used to output prediction results and warning information to operation and maintenance personnel.

5. The active balancing device for the distribution network backup power supply battery according to claim 4, characterized in that: The energy transfer circuit of the energy transfer module adopts an efficient switch capacitor design, including an inductor, a switch device and a photocoupler, so as to realize fast and efficient transfer of energy between batteries.

6. An active balancing method for a distribution network backup power battery, characterized in that: The following steps are involved: Real-time monitoring of the voltage, internal resistance and temperature of each single cell in the battery pack; Based on the monitoring data, the trained deep learning neural network model is used to determine whether the battery pack is in an unbalanced state; When it is determined to be unbalanced, the energy transfer process is started, and the excess energy of the high-capacity battery is transferred to the low-capacity battery through the energy transfer circuit based on the switched capacitor; Collect and analyze historical and real-time data of the battery pack, predict the open circuit status of the battery pack, and output early warning information.

7. The active balancing method for distribution network backup power supply batteries according to claim 6, characterized in that: The step of determining whether the battery pack is in an unbalanced state also includes: The voltage of each single cell is measured by a precision voltage detection circuit, the operating temperature of each single cell is monitored by a temperature sensor, and the internal resistance of each single cell is detected by an internal resistance detection circuit; Preprocess and extract features of monitoring data; A machine learning algorithm is used in the control operation circuit to determine the imbalance state of the battery pack.

8. The active balancing method for distribution network backup power supply batteries according to claim 7, characterized in that: During the energy transfer process, the energy transfer circuit is also protected against overvoltage, overcurrent and short circuit to ensure the safety and stability of the energy transfer.

9. The active balancing method for distribution network backup power supply batteries according to claim 8, characterized in that: The steps of predicting the open circuit state of the battery pack include: Collect real-time and historical data of battery packs; Clean, organize and analyze data; Use the trained deep learning neural network model to predict the risk of open circuit faults; Generate early warning information based on the prediction results.

10. The active balancing method for distribution network backup power supply batteries according to claim 9, characterized in that: After outputting the warning information, it also includes steps to take maintenance and adjustment measures in a timely manner according to the warning information to avoid failures and ensure the stable operation of the distribution network backup power supply.

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