A method, device and storage medium for monitoring distributed battery packs

Abstract

A distributed battery pack monitoring method, device, and storage medium include: a battery monitoring sensor group uploading multi-frequency internal resistance, voltage, and temperature data to a main control unit via a daisy-chain communication unit; the main control unit sending real-time battery parameters to an LCD display for direct display; the main control unit processing and plotting the multi-frequency internal resistance using intelligent algorithms to obtain the battery's internal resistance spectrum; the main control unit sending the internal resistance spectrum to a battery capacity assessment host or battery monitoring software platform to estimate capacity and provide real-time temperature feedback; a storage unit storing the monitoring data; and a switch output unit outputting fault status to peripheral devices. This invention comprehensively monitors the real-time operating status of the battery by collecting multiple key parameters. The daisy-chain communication structure enables efficient aggregation and uploading of multi-point data. The intelligent algorithm analyzes and processes the internal resistance data to accurately plot the battery's internal resistance spectrum.

Description

Technical Field

[0001] This invention belongs to the field of battery monitoring technology, and particularly relates to a method, device and storage medium for monitoring distributed battery packs. Background Technology

[0002] With the development of battery technology, existing monitoring methods typically employ single-point or limited monitoring points, failing to comprehensively reflect the operating status of battery packs. They struggle to accurately capture differences in parameters such as voltage and temperature within the battery pack, and the accuracy of monitoring data needs improvement. Furthermore, large-scale battery packs utilize a centralized monitoring architecture, requiring numerous sensors and complex wiring designs. This results in a complex system structure, greater difficulty in installation, commissioning, and maintenance, and higher costs.

[0003] Furthermore, in a centralized architecture, a large amount of monitoring data needs to be transmitted to the central control unit through limited communication channels. As the scale of the battery pack increases, the data transmission efficiency will decrease significantly, easily leading to information bottlenecks. Most monitoring solutions only focus on battery parameter acquisition and status monitoring, lacking intelligent analysis and early warning functions, and cannot meet the needs of intelligent management and optimization of battery packs.

[0004] Therefore, how to design a distributed battery monitoring method to solve problems related to monitoring accuracy, system complexity, data transmission, reliability, scalability, and limited functionality is an urgent issue that needs to be addressed in current battery management technology. Summary of the Invention

[0005] To address the shortcomings of the existing technology, the present invention provides a method for monitoring distributed battery packs, the method comprising the following steps:

[0006] The battery pack is charged by a DC power supply, and backup batteries are connected in series according to different voltage levels to serve as backup power for the DC power supply.

[0007] The battery monitoring sensor group is connected in parallel with the positive and negative terminals of the backup battery via a four-wire acquisition line. The battery monitoring sensor group is powered by an external power supply / battery pack through an auxiliary power supply unit.

[0008] The battery monitoring sensor group collects multi-frequency internal resistance, voltage, and temperature data and uploads them to the main control unit via a daisy-chain communication unit. At the same time, the battery pack voltage acquisition unit converts the voltage across the battery pack into a digital signal and uploads it to the main control unit. The battery pack current acquisition unit converts the battery pack current collected by the DC Hall sensor into a digital signal and uploads it to the main control unit.

[0009] The main control unit sends the real-time parameters of the battery, including battery pack voltage, battery pack current, single cell voltage, single cell internal resistance, and single cell temperature, to the LCD display for direct display via the display connection unit.

[0010] The main control unit, combined with intelligent algorithms, processes and plots the internal resistance at multiple frequencies to obtain the battery's internal resistance spectrum.

[0011] The main control unit sends the plotted internal resistance spectrum to the battery capacity assessment host via the 485 communication unit, which then estimates the capacity and provides real-time temperature feedback. Alternatively, it sends the spectrum to the battery monitoring software platform via the Ethernet communication unit, which then estimates the capacity and provides real-time temperature feedback.

[0012] The monitoring data is stored in the storage unit and transmitted to the USB memory via the USB storage communication unit. The working status is displayed by the indicator unit through LED lights. The working status includes communication, operation, and fault. The fault status is output to the peripheral device by the switch output unit.

[0013] The multi-frequency internal resistance spectrum plotting includes:

[0014] Input an excitation current ΔI to the battery and change the frequency Fn. Collect the fluctuation voltage ΔV generated by the current excitation of the battery. Calculate the real internal resistance Xn of the battery according to the function f(Xn)=Fn(ΔV / ΔI), where n is a frequency variable with a range of 0.5HZ-7.5KHZ.

[0015] The phase difference θ between the battery excitation current ΔI and the fluctuating voltage ΔV caused by the resistance is calculated by the FFT Fourier function. The internal resistance value Yn of the imaginary part of the battery is calculated by the function f(Yn)=tanθ*Fn(ΔV / ΔI), where n is a frequency variable ranging from 0.5HZ to 7.5KHZ.

[0016] The Narquist plot is plotted using the real internal resistance Xn and imaginary internal resistance Yn of the battery calculated at the corresponding frequency. The horizontal axis represents the real internal resistance and the vertical axis represents the imaginary internal resistance. The battery capacity and real-time temperature feedback are analyzed using the plot.

[0017] Among them, battery capacity prediction based on multi-frequency internal resistance spectrum comparison includes:

[0018] Test and plot the internal resistance spectrum of the battery in the float charging state, and mark it as C100;

[0019] When the battery pack discharges externally, the capacity of each battery is recorded. When the battery discharges to 5% capacity, the internal resistance spectrum is tested, plotted, and marked as C95, C90...C10, C05.

[0020] Capacity is calculated based on current detection, voltage detection, and time detection, C = IT (AH), with a voltage range of 2.35V-1.8V;

[0021] Establish a database of internal resistance graphs for each battery at different capacities. Use a lookup table (LUT) to compare the internal resistance graph of the battery with the database, find the graph of the real-time test that is closest to the graph in the database, and use the graph in the database to reflect the battery capacity.

[0022] The battery pack is charged and discharged according to a preset cycle to verify its capacity, and the internal resistance spectrum test is automatically started and the spectrum is updated in a timely manner.

[0023] Among them, the battery capacity internal temperature feedback based on multi-frequency internal resistance spectrum comparison includes:

[0024] The battery was placed in an adjustable constant temperature chamber for internal resistance spectrum testing and plotting. The calibration was performed every 10℃. The corresponding markings were made at temperatures of -20℃, -10℃, 0℃, 10℃, 20℃, 30℃, 40℃, 50℃, and 60℃. The marked spectrums were compiled into a table.

[0025] During battery operation, battery monitoring sensors collect the battery surface temperature and terminal temperature.

[0026] The real-time internal resistance spectrum is compared with the battery's internal resistance spectrum database using a lookup table (LUT) method. The spectrum that is closest to the real-time spectrum is found, and the temperature range of the battery is reflected by the spectrum in the database.

[0027] Among them, the multi-band internal resistance acquisition based on the adaptive frequency band selection algorithm includes:

[0028] Determine the initial set of frequency bands and threshold parameters, and train a battery state-frequency band response model using a support vector machine.

[0029] A four-terminal measurement method was adopted, with voltage measurement terminals connected to both ends of the battery and current measurement terminals connected to both ends and inside the battery. An AC impedance analyzer was used to measure the internal resistance of the battery in sequence in the initial frequency band set. The measured internal resistance data was stored in the database, and the rate of change of internal resistance under each frequency band was calculated.

[0030] The current battery state is analyzed based on the battery state-frequency band response model. The frequency band most sensitive to the corresponding battery state is selected as the priority measurement frequency band. The internal resistance change rate of the priority frequency band is calculated and compared with the preset threshold. If the change rate exceeds the threshold, it indicates that the battery state has changed significantly and the frequency band set needs to be expanded for more detailed measurement. If the change rate is lower than the threshold, it indicates that the battery state is relatively stable and the current frequency band set can be maintained. The measurement frequency band set is dynamically adjusted according to the comparison results.

[0031] By integrating and analyzing historical internal resistance data, the internal parameters of the battery are extracted using an equivalent circuit model fitting algorithm. Combined with the battery state-frequency response model, the battery capacity and state of harmlessness (SOH) are predicted and diagnosed.

[0032] Voltage acquisition includes:

[0033] Voltage measurement lines are connected to the positive and negative terminals of the battery, and a voltage acquisition circuit with high input impedance is used to monitor the voltage in real time.

[0034] Voltage is acquired using the MCU's built-in ADC channel or an external dedicated voltage acquisition chip.

[0035] Temperature acquisition includes:

[0036] Temperature sensors are attached to key locations on the surface or inside the battery, and a temperature acquisition circuit is used to amplify, filter, and digitize the output of the temperature sensors. By monitoring changes in battery temperature, the battery's operating status is determined, and thermal management control is performed.

[0037] Among them, an NTC surface contact temperature sensor is used to monitor the temperature change of the battery in real time, and the internal resistance spectrum test is automatically started when the temperature change threshold is triggered.

[0038] This invention utilizes a battery monitoring sensor group to collect multi-frequency internal resistance, voltage, and temperature data, which are then uploaded to a main control unit via a daisy-chain communication unit. The main control unit transmits the battery's real-time parameters to an LCD display via a display connection unit for direct display. The main control unit then processes and plots the multi-frequency internal resistance data using intelligent algorithms to obtain the battery's internal resistance graph. This graph is then sent to a battery capacity assessment host or battery monitoring software platform for capacity estimation and real-time temperature feedback. The monitoring data is stored in a storage unit, and a switch output unit outputs fault status signals to peripheral devices. This invention collects multiple key parameters of the battery, such as internal resistance, voltage, and temperature, enabling comprehensive monitoring of the battery's real-time operating status. The daisy-chain communication structure allows for efficient aggregation and uploading of data from multiple points. The main control unit uses intelligent algorithms to analyze and process the internal resistance data, accurately plotting the battery's internal resistance graph. Attached Figure Description

[0039] The above and other objects, features, and advantages of exemplary embodiments of the present disclosure will become readily apparent upon reading the following detailed description with reference to the accompanying drawings. In the drawings, several embodiments of the present disclosure are illustrated by way of example and not limitation, and like or corresponding reference numerals denote like or corresponding parts, wherein:

[0040] Figure 1This is a flowchart illustrating a distributed battery pack monitoring method according to an embodiment of the present invention.

[0041] Figure 2 This illustrates the circuit structure of a distributed battery pack monitoring device according to an embodiment of the present invention. Detailed Implementation

[0042] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0043] The terminology used in the embodiments of this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “a,” “the,” and “the” as used in the embodiments of this invention and the appended claims are also intended to include the plural forms, and “multiple” generally includes at least two unless the context clearly indicates otherwise.

[0044] It should be understood that although the terms first, second, third, etc., may be used to describe... in the embodiments of the present invention, these... should not be limited to these terms. These terms are only used to distinguish... For example, first... may also be referred to as second... without departing from the scope of the embodiments of the present invention, and similarly, second... may also be referred to as first...

[0045] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0046] Depending on the context, the words “if” or “suppose” as used here can be interpreted as “when” or “in response to determination” or “in response to detection.” Similarly, depending on the context, the phrases “if determination” or “if detection (of the stated condition or event)” can be interpreted as “when determination” or “in response to determination” or “when detection (of the stated condition or event)” or “in response to detection (of the stated condition or event).”

[0047] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that an article or device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such an article or device. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the article or device that includes said element.

[0048] Existing monitoring methods typically employ single-point or limited monitoring points, failing to comprehensively reflect the operating status of battery packs. They struggle to accurately capture differences in parameters such as voltage and temperature within the battery pack, resulting in a need for improved monitoring data accuracy. Furthermore, large-scale battery packs utilize a centralized monitoring architecture, requiring the transmission of substantial amounts of monitoring data to a central control unit via limited communication channels. As the scale of battery packs expands, data transmission efficiency significantly decreases, potentially leading to information bottlenecks. Currently, most monitoring solutions focus solely on battery parameter acquisition and status monitoring, lacking intelligent analysis and early warning capabilities, thus failing to meet the demands of intelligent battery pack management and optimization applications.

[0049] like Figure 1 As shown, this invention discloses a method for monitoring distributed battery packs, the method comprising:

[0050] The battery pack is charged by a DC power supply, and backup batteries are connected in series according to different voltage levels to serve as backup power for the DC power supply.

[0051] In one embodiment, such as Figure 2 As shown, DC power supply 701 charges the battery pack. Batteries 201-204 (illustrated quantity, connected in series according to different voltage levels) are connected in series with positive and negative terminals, serving as backup power for DC power supply 701. Battery monitoring sensors 101-104 are connected in parallel with the positive and negative terminals of batteries 201-204 via four-wire acquisition lines. External power supply / battery pack power supply provides power to battery monitoring sensors 201-204 through auxiliary power supply unit 304, ensuring that they do not draw battery power during operation and avoiding battery drain.

[0052] The battery monitoring sensor group is connected in parallel with the positive and negative terminals of the backup battery via a four-wire acquisition line. The battery monitoring sensor group is powered by an external power supply / battery pack through an auxiliary power supply unit.

[0053] The four-wire measurement method effectively eliminates the influence of measurement line resistance, obtaining more accurate battery voltage data. This method can more accurately reflect the actual operating voltage of the battery, which is beneficial for precise monitoring of battery status. Using a four-wire connection to the battery monitoring sensor group can significantly improve measurement accuracy and anti-interference ability, ensure data reliability, and also simplify the system structure and installation process.

[0054] The four-wire structure reduces the impedance of the measurement circuit, improves the anti-interference capability of the measurement signal, and effectively suppresses measurement errors caused by changes in line resistance or external interference. The four-wire connection method ensures continuous and stable acquisition of battery voltage data, reduces measurement interruptions caused by poor contact, and improves the reliability and stability of the entire battery monitoring system.

[0055] The battery monitoring sensor group collects multi-frequency internal resistance, voltage, and temperature data, which are then uploaded to the main control unit via a daisy-chain communication unit. Simultaneously, the battery pack voltage acquisition unit converts the voltage across the battery pack into a digital signal and uploads it to the main control unit. The battery pack current acquisition unit converts the battery pack current collected by the DC Hall sensor into a digital signal and uploads it to the main control unit.

[0056] Employing a daisy-chain communication architecture simplifies system wiring and improves scalability and reliability. The daisy-chain structure is easily expandable, allowing for the flexible addition or removal of monitoring units without requiring large-scale modifications to the entire system, thus enhancing scalability. Furthermore, it offers high reliability; even if a single monitoring unit fails, it will not affect the normal operation of the entire system, improving fault tolerance and reliability.

[0057] The main control unit sends the real-time parameters of the battery, including battery pack voltage, battery pack current, individual cell voltage, individual cell internal resistance, and individual cell temperature, to the LCD display for direct display via the display connection unit.

[0058] In one embodiment, the main control unit 301 sends the real-time parameters of the battery, such as battery pack voltage, battery pack current, single cell voltage, single cell internal resistance, and single cell temperature, to the LCD display 312 for direct display via the display connection unit 311.

[0059] The main control unit, combined with intelligent algorithms, processes and plots the internal resistance at multiple frequencies to obtain the battery's internal resistance spectrum.

[0060] In one embodiment, the multi-frequency internal resistance plotting includes:

[0061] Input an excitation current ΔI to the battery and change the frequency Fn. Collect the fluctuation voltage ΔV generated by the current excitation of the battery. Calculate the real internal resistance Xn of the battery according to the function f(Xn)=Fn(ΔV / ΔI), where n is a frequency variable with a range of 0.5HZ-7.5KHZ.

[0062] The phase difference θ between the battery excitation current ΔI and the fluctuating voltage ΔV caused by the resistance is calculated by the FFT Fourier function. The internal resistance value Yn of the imaginary part of the battery is calculated by the function f(Yn)=tanθ*Fn(ΔV / ΔI), where n is a frequency variable ranging from 0.5HZ to 7.5KHZ.

[0063] The Narquist plot is plotted using the real internal resistance Xn and imaginary internal resistance Yn of the battery calculated at the corresponding frequency. The horizontal axis represents the real internal resistance and the vertical axis represents the imaginary internal resistance. The battery capacity and real-time temperature feedback are analyzed using the plot.

[0064] The main control unit sends the plotted internal resistance spectrum to the battery capacity assessment host via the 485 communication unit, which then estimates the capacity and provides real-time temperature feedback. Alternatively, it sends the spectrum to the battery monitoring software platform via the Ethernet communication unit, which then estimates the capacity and provides real-time temperature feedback.

[0065] In one embodiment, battery capacity estimation based on multi-frequency internal resistance spectrum comparison includes:

[0066] Test and plot the internal resistance spectrum of the battery in the float charging state, and mark it as C100;

[0067] When the battery pack discharges externally, the capacity of each battery is recorded. When the battery discharges to 5% capacity, the internal resistance spectrum is tested, plotted, and marked as C95, C90...C10, C05.

[0068] Capacity is calculated based on current detection, voltage detection, and time detection, C = IT (AH), with a voltage range of 2.35V-1.8V;

[0069] Establish a database of internal resistance graphs for each battery at different capacities. Use a lookup table (LUT) to compare the internal resistance graph of the battery with the database, find the graph of the real-time test that is closest to the graph in the database, and use the graph in the database to reflect the battery capacity.

[0070] The battery pack is charged and discharged according to a preset cycle to verify its capacity, and the internal resistance spectrum test is automatically started and the spectrum is updated in a timely manner.

[0071] In one embodiment, the battery capacity internal temperature feedback based on multi-frequency internal resistance spectrum comparison includes:

[0072] The battery was placed in an adjustable constant temperature chamber for internal resistance spectrum testing and plotting. The calibration was performed every 10℃. The corresponding markings were made at temperatures of -20℃, -10℃, 0℃, 10℃, 20℃, 30℃, 40℃, 50℃, and 60℃. The marked spectrums were compiled into a table.

[0073] During battery operation, battery monitoring sensors collect the battery surface temperature and terminal temperature.

[0074] The real-time internal resistance spectrum is compared with the battery's internal resistance spectrum database using a lookup table (LUT) method. The spectrum that is closest to the real-time spectrum is found, and the temperature range of the battery is reflected by the spectrum in the database.

[0075] In one embodiment, multi-band internal resistance acquisition is performed based on an adaptive frequency band selection algorithm, including:

[0076] Determine the initial set of frequency bands and threshold parameters, and train a battery state-frequency band response model using a support vector machine.

[0077] A four-terminal measurement method was adopted, with voltage measurement terminals connected to both ends of the battery and current measurement terminals connected to both ends and inside the battery. An AC impedance analyzer was used to measure the internal resistance of the battery in sequence in the initial frequency band set. The measured internal resistance data was stored in the database, and the rate of change of internal resistance under each frequency band was calculated.

[0078] The current battery state is analyzed based on the battery state-frequency band response model. The frequency band most sensitive to the corresponding battery state is selected as the priority measurement frequency band. The internal resistance change rate of the priority frequency band is calculated and compared with the preset threshold. If the change rate exceeds the threshold, it indicates that the battery state has changed significantly and the frequency band set needs to be expanded for more detailed measurement. If the change rate is lower than the threshold, it indicates that the battery state is relatively stable and the current frequency band set can be maintained. The measurement frequency band set is dynamically adjusted according to the comparison results.

[0079] By integrating and analyzing historical internal resistance data, the internal parameters of the battery are extracted using an equivalent circuit model fitting algorithm. Combined with the battery state-frequency response model, the battery capacity and state of harmlessness (SOH) are predicted and diagnosed.

[0080] In one embodiment, voltage acquisition includes:

[0081] Voltage measurement lines are connected to the positive and negative terminals of the battery, and a voltage acquisition circuit with high input impedance is used to monitor the voltage in real time.

[0082] Voltage is acquired using the MCU's built-in ADC channel or an external dedicated voltage acquisition chip.

[0083] In one embodiment, temperature acquisition includes:

[0084] Temperature sensors are attached to key locations on the surface or inside the battery, and a temperature acquisition circuit is used to amplify, filter, and digitize the output of the temperature sensors. By monitoring changes in battery temperature, the battery's operating status is determined, and thermal management control is performed.

[0085] The monitoring data is stored in the storage unit and transmitted to the USB memory via the USB storage communication unit. The working status is displayed by the indicator unit through LED lights. The working status includes communication, operation, and fault. The fault status is output to the peripheral device by the switch output unit.

[0086] Among them, the monitoring data is stored in a storage unit, which can save data independently of the communication unit. In the event of communication interruption or failure, the data will not be lost and can be manually downloaded via USB or other interfaces, thus improving the reliability of the system's data storage.

[0087] In one embodiment, the switch output unit can output fault status signals to peripheral devices, enabling remote monitoring of fault status and facilitating further integration into higher-level monitoring and management systems. Specifically, the indicator light unit 307 uses LEDs to directly display the device's operating status, including communication, operation, and fault states. The switch output unit 310, through settings, will output fault status signals to peripheral devices such as environmental monitoring systems, alarms, and fire suppression systems.

[0088] In one embodiment, an NTC surface contact temperature sensor is used to monitor the temperature change of the battery in real time, and the internal resistance spectrum test is automatically started when the temperature change threshold is triggered.

[0089] This invention utilizes a battery monitoring sensor group to collect multi-frequency internal resistance, voltage, and temperature data, which are then uploaded to a main control unit via a daisy-chain communication unit. The main control unit transmits the battery's real-time parameters to an LCD display via a display connection unit for direct display. The main control unit then processes and plots the multi-frequency internal resistance data using intelligent algorithms to obtain the battery's internal resistance graph. This graph is then sent to a battery capacity assessment host or battery monitoring software platform for capacity estimation and real-time temperature feedback. The monitoring data is stored in a storage unit, and a switch output unit outputs fault status signals to peripheral devices. This invention collects multiple key parameters of the battery, such as internal resistance, voltage, and temperature, enabling comprehensive monitoring of the battery's real-time operating status. The daisy-chain communication structure allows for efficient aggregation and uploading of data from multiple points. The main control unit uses intelligent algorithms to analyze and process the internal resistance data, accurately plotting the battery's internal resistance graph.

[0090] It should be noted that the computer-readable medium described in this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0091] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device.

[0092] Computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0093] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0094] The units described in the embodiments of this disclosure can be implemented in software or hardware. The names of the units are not, in some cases, intended to limit the specific unit.

[0095] The preferred embodiments of the present invention have been described above to make the spirit of the present invention clearer and easier to understand, and are not intended to limit the present invention. All modifications, substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope summarized by the appended claims.

Claims

1. A method for monitoring distributed battery packs, characterized in that: The battery pack is charged by a DC power supply, and backup batteries are connected in series according to different voltage levels to serve as backup power for the DC power supply. The battery monitoring sensor group is connected in parallel with the positive and negative terminals of the backup battery via a four-wire acquisition line. The battery monitoring sensor group is powered by an external power supply / battery pack through an auxiliary power supply unit. The battery monitoring sensor group collects multi-frequency internal resistance, voltage, and temperature data and uploads them to the main control unit via a daisy-chain communication unit. At the same time, the battery pack voltage acquisition unit converts the voltage across the battery pack into a digital signal and uploads it to the main control unit. The battery pack current acquisition unit converts the battery pack current collected by the DC Hall sensor into a digital signal and uploads it to the main control unit. The main control unit sends the real-time parameters of the battery, including battery pack voltage, battery pack current, single cell voltage, single cell internal resistance, and single cell temperature, to the LCD display for direct display via the display connection unit. The main control unit, combined with intelligent algorithms, processes and plots the internal resistance at multiple frequencies to obtain the battery's internal resistance spectrum. The main control unit sends the plotted internal resistance spectrum to the battery capacity assessment host via the 485 communication unit, which then estimates the capacity and provides real-time temperature feedback. Alternatively, it sends the spectrum to the battery monitoring software platform via the Ethernet communication unit, which then estimates the capacity and provides real-time temperature feedback. The monitoring data is stored in the storage unit and transmitted to the USB memory through the USB storage communication unit. The working status is displayed by the indicator unit through LED lights. The working status includes communication, operation, and fault. The fault status is output to the peripheral device by the switch output unit. The multi-frequency internal resistance spectrum plotting includes: Input an excitation current ΔI to the battery and change the frequency Fn. Collect the fluctuation voltage ΔV generated by the current excitation of the battery. Calculate the real internal resistance Xn of the battery according to the function f(Xn)=Fn(ΔV / ΔI), where n is a frequency variable with a range of 0.5HZ-7.5KHZ. The phase difference θ between the battery excitation current ΔI and the fluctuating voltage ΔV caused by the resistance is calculated by the FFT Fourier function. The internal resistance value Yn of the imaginary part of the battery is calculated by the function f(Yn)=tanθ*Fn(ΔV / ΔI), where n is a frequency variable ranging from 0.5HZ to 7.5KHZ. The Narquist plot is plotted using the real internal resistance Xn and imaginary internal resistance Yn of the battery calculated at the corresponding frequency. The horizontal axis represents the real internal resistance and the vertical axis represents the imaginary internal resistance. The battery capacity and real-time temperature feedback are analyzed using the plot.

2. The distributed battery pack monitoring method as described in claim 1, characterized in that, Battery capacity prediction based on multi-frequency internal resistance spectrum comparison includes: Test and plot the internal resistance spectrum of the battery in the float charging state, and mark it as C100; When the battery pack discharges externally, the capacity of each battery is recorded. When the battery discharges to 5% capacity, the internal resistance spectrum is tested, plotted, and marked as C95, C90...C10, C05. Capacity is calculated based on current detection, voltage detection, and time detection, C=IT(AH), with a voltage range of 2.35V-1.8V; Establish a database of internal resistance graphs for each battery at different capacities. Use a lookup table (LUT) to compare the internal resistance graph of the battery with the database, find the graph of the real-time test that is closest to the graph in the database, and use the graph in the database to reflect the battery capacity. The battery pack is charged and discharged according to a preset cycle to verify its capacity, and the internal resistance spectrum test is automatically started and the spectrum is updated in a timely manner.

3. The distributed battery pack monitoring method as described in claim 2, characterized in that, Battery capacity internal temperature feedback based on multi-frequency internal resistance spectrum comparison includes: The battery was placed in an adjustable constant temperature chamber for internal resistance spectrum testing and plotting. The calibration was performed every 10℃. The corresponding markings were made at temperatures of -20℃, -10℃, 0℃, 10℃, 20℃, 30℃, 40℃, 50℃, and 60℃. The marked spectrums were compiled into a table. During battery operation, battery monitoring sensors collect the battery surface temperature and terminal temperature. The real-time internal resistance spectrum is compared with the battery's internal resistance spectrum database using a lookup table (LUT) method. The spectrum that is closest to the real-time spectrum is found, and the temperature range of the battery is reflected by the spectrum in the database.

4. The distributed battery pack monitoring method as described in claim 1, characterized in that, Multi-band internal resistance acquisition based on adaptive frequency band selection algorithm, including: Determine the initial set of frequency bands and threshold parameters, and train a battery state-frequency band response model using a support vector machine. A four-terminal measurement method was adopted, with voltage measurement terminals connected to both ends of the battery and current measurement terminals connected to both ends and inside the battery. An AC impedance analyzer was used to measure the internal resistance of the battery in sequence in the initial frequency band set. The measured internal resistance data was stored in the database, and the rate of change of internal resistance under each frequency band was calculated. The current battery state is analyzed based on the battery state-frequency band response model. The frequency band most sensitive to the corresponding battery state is selected as the priority measurement frequency band. The internal resistance change rate of the priority frequency band is calculated and compared with the preset threshold. If the change rate exceeds the threshold, it indicates that the battery state has changed significantly and the frequency band set needs to be expanded for more detailed measurement. If the change rate is lower than the threshold, it indicates that the battery state is relatively stable and the current frequency band set can be maintained. The measurement frequency band set is dynamically adjusted according to the comparison results. By integrating and analyzing historical internal resistance data, the internal parameters of the battery are extracted using an equivalent circuit model fitting algorithm. Combined with the battery state-frequency response model, the battery capacity and state of harmlessness (SOH) are predicted and diagnosed.

5. The distributed battery pack monitoring method as described in claim 1, characterized in that, Voltage acquisition includes: Voltage measurement lines are connected to the positive and negative terminals of the battery, and a voltage acquisition circuit with high input impedance is used to monitor the voltage in real time. Voltage is acquired using the MCU's built-in ADC channel or an external dedicated voltage acquisition chip.

6. The distributed battery pack monitoring method as described in claim 1, characterized in that, Temperature acquisition includes: Temperature sensors are attached to key locations on the surface or inside the battery, and a temperature acquisition circuit is used to amplify, filter, and digitize the output of the temperature sensors. By monitoring changes in battery temperature, the battery's operating status is determined, and thermal management control is performed.

7. The distributed battery pack monitoring method as described in claim 3, characterized in that: An NTC surface contact temperature sensor is used to monitor the battery temperature change in real time. When the temperature change threshold is triggered, the internal resistance spectrum test is automatically started.

8. A distributed battery pack monitoring device, comprising: At least one processor; as well as At least one memory including computer program code, The at least one memory and the computer program code are configured, together with the at least one processor, to cause the device to perform the method according to any one of claims 1-7.

9. A computer-readable storage medium having a computer program stored thereon, the computer program, when executed by a processor, implementing the method of any one of claims 1-7.

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