Battery health detection method and system based on battery charging tail end voltage, terminal and storage medium

Abstract

The invention relates to the technical field of battery management analysis, and discloses a battery health detection method and system based on a battery charging tail end voltage, a terminal and a storage medium, and the method comprises the steps: obtaining voltage data, charging duration data and historical reference data of a to-be-analyzed battery at a charging tail end stage, performing curve alignment processing according to the voltage data, the charging duration data and the historical reference data to obtain a quantitative difference value; performing circulation trend analysis on the quantitative difference value according to a preset attenuation threshold value to obtain a battery capacity attenuation early warning signal; and inputting the battery capacity attenuation early warning signal into a trained battery health scoring model for analysis to obtain a battery health state score. According to the voltage data, the charging duration data and the historical reference data of the battery charging end stage, the battery capacity attenuation is early warned, and accurate detection of the battery capacity attenuation is realized.

Description

Technical Field

[0001] This invention relates to the field of battery management and analysis technology, and in particular to a battery health detection method, system, terminal, and computer-readable storage medium based on the battery charging end voltage. Background Technology

[0002] Currently, with the continuous advancement of intelligent mining construction and the increasing demands for safe production, the importance of health status monitoring technology for mining power batteries is becoming increasingly prominent. Whether it's underground communication equipment, portable testing instruments, or emergency power systems, reliable battery health management technology provides crucial protection for safe mining operations. However, current battery health testing methods primarily employ traditional impedance spectroscopy analysis or multi-parameter fusion methods, which are insufficient for achieving cost-effective and efficient online health monitoring in mining power supply scenarios.

[0003] Traditional methods often rely on complex impedance spectroscopy analysis or multi-parameter fusion techniques, which make it difficult to achieve low-cost, low-complexity online health monitoring in embedded systems of mining power supplies. This results in high detection costs, high system complexity, and poor practicality. Under the use of traditional detection methods, it is impossible to achieve economical and effective health analysis of batteries in mining power supply scenarios, which has become an urgent problem to be solved.

[0004] Therefore, existing technologies still need to be improved and developed. Summary of the Invention

[0005] The main objective of this invention is to provide a battery health detection method, system, terminal, and computer-readable storage medium based on the battery charging terminal voltage, aiming to solve the problem that in the prior art, traditional detection methods cannot be used to perform battery health analysis in mining power supply scenarios.

[0006] To achieve the above objectives, the present invention provides a battery health detection method based on the battery charging terminal voltage, the battery health detection method based on the battery charging terminal voltage comprising the following steps: The voltage data, charging time data, and historical benchmark data of the battery to be analyzed at the end of the charging stage are obtained. Based on the voltage data, the charging time data, and the historical benchmark data, curve alignment processing is performed to obtain the quantitative difference value. Based on a preset attenuation threshold, a cyclical trend analysis is performed on the quantified difference value to obtain a battery capacity attenuation warning signal; The battery capacity degradation warning signal is input into the trained battery health scoring model for analysis to obtain the battery health status score.

[0007] Optionally, the battery health detection method based on the battery charging end voltage, wherein the step of acquiring the voltage data, charging time data, and historical benchmark data of the battery to be analyzed at the end of the charging stage, and performing curve alignment processing based on the voltage data, the charging time data, and the historical benchmark data to obtain a quantified difference value, further includes: Determine the target scenario type of the battery to be analyzed; Based on the target scenario type, determine the health parameters of the battery to be analyzed, and based on the health parameters, determine the target capacity parameters of the battery to be analyzed. The target scenario types include battery material analysis scenario type and mining power supply scenario type.

[0008] Optionally, the battery health detection method based on the battery charging end voltage, wherein acquiring the voltage data, charging time data, and historical benchmark data of the battery to be analyzed at the end of the charging stage, and performing curve alignment processing based on the voltage data, the charging time data, and the historical benchmark data to obtain a quantified difference value, specifically includes: Based on the TCP protocol, voltage data, charging time data, and historical benchmark data of the battery to be analyzed are collected at the end of the charging process. The voltage data, the charging time data, and the historical baseline data are subjected to curve alignment processing to obtain a quantified difference value. The voltage data includes power input voltage, input current, battery output current, battery charge, and battery temperature.

[0009] Optionally, the battery health detection method based on the battery charging end voltage, wherein the step of performing curve alignment processing based on the voltage data and the charging duration data with the historical benchmark data to obtain a quantified difference value specifically includes: Based on the voltage data, the charging time data, and the historical benchmark data, curve alignment processing is performed to obtain the quantified difference value: ; in, Indicates the average voltage deviation. Indicates the sampling point. Indicates the sampling point index. Indicates the first The SOC value corresponding to each sampling point Indicates the current voltage curve at Voltage value at that location, Indicates the historical voltage curve at The voltage value at that location.

[0010] Optionally, the battery health detection method based on the battery charging end voltage, wherein the step of performing cyclic trend analysis on the quantified difference value according to a preset attenuation threshold to obtain a battery capacity attenuation warning signal specifically includes: Based on a preset attenuation threshold, a cyclic trend analysis is performed on the quantized difference value to obtain the capacity attenuation factor parameter. The number of cycles is obtained, and a battery capacity decay warning signal is determined based on the number of cycles and the capacity decay factor parameter.

[0011] Optionally, in the battery health detection method based on the battery charging end voltage, the capacity decay factor parameter includes constant current charging duration and charging end voltage; The process of obtaining the number of cycles and determining a battery capacity degradation warning signal based on the number of cycles and the capacity degradation factor parameter specifically includes: Algorithm for obtaining loop count, control commands, and health status; Based on the TCP protocol, the battery under test is charged and discharged according to the control instructions to obtain the constant current charging duration and the charging end voltage. The health status algorithm is used to dynamically calculate the number of cycles, the constant current charging duration, and the charging end voltage to obtain a battery capacity degradation warning signal.

[0012] Optionally, the battery health detection method based on the battery charging end voltage, wherein the step of inputting the battery capacity degradation warning signal into the trained battery health scoring model for analysis to obtain a battery health status score specifically includes: The power supply input voltage, input current, battery output current, battery capacity, and battery temperature are divided to obtain a training set and a validation set; The battery health scoring model is trained based on the training set to obtain the trained battery health scoring model. The battery capacity decay warning signal is input into the trained battery health scoring model for analysis to obtain the target capacity decay factor parameter. Based on the target capacity decay parameter, the battery to be analyzed is subjected to a health status assessment to obtain a battery health status score. The validation set is used to validate the performance of the trained battery health scoring model.

[0013] Furthermore, to achieve the above objectives, the present invention also provides a battery health detection method system based on the battery charging terminal voltage, wherein the battery health detection method system based on the battery charging terminal voltage: The battery difference calculation module is used to acquire voltage data, charging time data and historical benchmark data of the battery to be analyzed at the end of the charging stage, and to perform curve alignment processing based on the voltage data, the charging time data and the historical benchmark data to obtain a quantitative difference value. The battery degradation early warning module is used to perform cyclic trend analysis on the quantified difference value according to a preset degradation threshold to obtain a battery capacity degradation early warning signal. The battery health assessment module is used to input the battery capacity degradation warning signal into the trained battery health scoring model for analysis, and obtain the battery health status score.

[0014] Furthermore, to achieve the above objectives, the present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a battery health detection program based on the battery charging terminal voltage, and the battery health detection program based on the battery charging terminal voltage, when executed by a processor, implements the steps of the battery health detection method based on the battery charging terminal voltage as described above.

[0015] In this invention, voltage data, charging time data, and historical benchmark data of the battery to be analyzed at the end of the charging process are acquired. Curve alignment processing is performed based on the voltage data, charging time data, and historical benchmark data to obtain a quantified difference value. Cyclic trend analysis is then performed on the quantified difference value according to a preset attenuation threshold to obtain a battery capacity attenuation warning signal. This warning signal is then input into a trained battery health scoring model for analysis to obtain a battery health status score. This invention provides early warning of battery capacity attenuation based on voltage data, charging time data, and historical benchmark data at the end of the charging process, achieving accurate detection of battery capacity attenuation. Attached Figure Description

[0016] Figure 1 This is a flowchart of a preferred embodiment of the battery health detection method based on the battery charging end voltage of the present invention; Figure 2 This is a flowchart of the network architecture of a preferred embodiment of the battery health detection method based on the battery charging end voltage of the present invention; Figure 3 This is a flowchart of the system architecture of a preferred embodiment of the battery health detection method based on the battery charging end voltage of the present invention; Figure 4 This is a flowchart of power state acquisition, which is a preferred embodiment of the battery health detection method based on the battery charging end voltage of the present invention. Figure 5 This is a flowchart of the power control process of a preferred embodiment of the battery health detection method based on the battery charging end voltage of the present invention. Figure 6This is a structural diagram of a preferred embodiment of the battery health detection method system based on the battery charging end voltage of the present invention; Figure 7 This is a structural diagram of a preferred embodiment of the terminal of the device of the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer and more explicit, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0018] Traditional methods often rely on complex impedance spectroscopy analysis or multi-parameter fusion techniques, which are difficult to implement in low-cost, low-complexity online health monitoring in embedded systems of mining power supplies. This results in high detection costs, high system complexity, and poor practicality. Under the use of traditional detection methods, it is impossible to achieve economical and effective health analysis of batteries in mining power supply scenarios. Therefore, there is a need for a battery health detection method based on the battery charging end voltage. This method can provide early warning of battery capacity degradation based on voltage data, charging time data, and historical benchmark data at the end of the battery charging stage, thereby achieving accurate detection of battery capacity degradation.

[0019] The battery health detection method based on the battery charging end voltage described in the preferred embodiment of the present invention, such as... Figure 1 , Figure 2 and Figure 3 As shown, the battery health detection method based on the battery charging end voltage includes the following steps: Step S10: Obtain the voltage data, charging time data, and historical benchmark data of the battery to be analyzed at the end of the charging stage. Perform curve alignment processing based on the voltage data, the charging time data, and the historical benchmark data to obtain the quantified difference value.

[0020] Step S10 includes: Step S11: Based on the TCP protocol, collect voltage data, charging time data, and historical benchmark data of the battery to be analyzed at the end of the charging process. Step S12: Perform curve alignment processing on the voltage data, the charging time data, and the historical reference data to obtain a quantified difference value.

[0021] Specifically, before step S10, the method further includes determining the target scenario type of the battery to be analyzed, determining the health parameters of the battery to be analyzed based on the target scenario type, and determining the target capacity parameters of the battery to be analyzed based on the health parameters. The target scenario type includes battery material analysis scenario type and mining power supply scenario type. Based on the TCP protocol, voltage data, charging time data, and historical benchmark data of the battery to be analyzed at the end of the charging stage are collected (power input voltage, input current, battery output current, battery capacity, and battery temperature are collected periodically via the TCP protocol). Based on the voltage data and the charging time data, curve alignment processing is performed with the historical benchmark data to obtain a quantified difference value (aligning the current charging curve (voltage data - charging time data) with the historical curve according to SOC (historical benchmark data)). The voltage data includes power input voltage, input current, battery output current, battery capacity, and battery temperature.

[0022] In this embodiment, the voltage data, charging time data, and historical reference data are subjected to curve alignment processing to obtain a quantified difference value: ; in, Indicates the average voltage deviation. Indicates the sampling point. Indicates the sampling point index. Indicates the first The SOC value corresponding to each sampling point Indicates the current voltage curve at Voltage value at that location, Indicates the historical voltage curve at The voltage value at that location.

[0023] For example, such as Figure 2 and Figure 3 As shown, Figure 2 The system schedules power equipment through a control room, and then utilizes the data center's data acquisition, storage, message queue, and application services. Figure 3 Data collected from mining power supplies is uploaded to the power adapter via TCP protocol, then transmitted to the equipment data center for storage and push to user clients and mobile devices.

[0024] Step S20: Perform cyclic trend analysis on the quantified difference value according to the preset attenuation threshold to obtain a battery capacity attenuation warning signal.

[0025] Step S20 includes: Step S21: Perform cyclic trend analysis on the quantization difference value according to the preset attenuation threshold to obtain the capacity attenuation factor parameter; Step S22: Obtain the number of cycles, and determine the battery capacity decay warning signal based on the number of cycles and the capacity decay factor parameter.

[0026] Specifically, the quantified difference value is subjected to cyclic trend analysis based on a preset attenuation threshold to obtain the capacity attenuation factor parameter, the number of cycles is obtained, and a battery capacity attenuation warning signal is determined based on the number of cycles and the capacity attenuation factor parameter (if the threshold is exceeded for 3 consecutive cycles (e.g., 10mV, the threshold is set according to different battery capacities), the battery capacity attenuation warning signal is triggered).

[0027] Step S22 includes: Step S221: Obtain the loop count, control instructions, and health status algorithm; Step S222: Based on the TCP protocol, perform charge and discharge tests on the battery under test according to the control instructions to obtain the constant current charging duration and the charging end voltage; Step S223: Dynamically calculate the number of cycles, the constant current charging duration, and the charging end voltage using the health status algorithm to obtain a battery capacity decay warning signal.

[0028] Specifically, the number of cycles, control commands, and health status algorithm are obtained. Based on the TCP protocol, the battery under test is charged and discharged according to the control commands to obtain the constant current charging duration and the charging end voltage. The health status algorithm is used to dynamically calculate the number of cycles, the constant current charging duration, and the charging end voltage to obtain a battery capacity decay warning signal.

[0029] Specifically, the charging data comparison is shown in Table 1:

[0030] Table 1: Comparison of Charging Data Step S30: Input the battery capacity decay warning signal into the trained battery health scoring model for analysis to obtain the battery health status score.

[0031] Step S30 includes: Step S31: Divide the power supply input voltage, input current, battery output current, battery capacity, and battery temperature to obtain a training set and a validation set; Step S32: Train the battery health scoring model based on the training set to obtain the trained battery health scoring model; Step S33: Input the battery capacity decay warning signal into the trained battery health scoring model for analysis to obtain the target capacity decay factor parameter; Step S34: Perform a health status assessment on the battery to be analyzed based on the target capacity decay parameter to obtain a battery health status score.

[0032] Specifically, the power supply input voltage, input current, battery output current, battery capacity, and battery temperature are divided into training and validation sets. The battery health scoring model is trained based on the training set to obtain a trained battery health scoring model. The battery capacity decay warning signal is input into the trained battery health scoring model for analysis to obtain the target capacity decay factor parameter. Based on the target capacity decay parameter, the battery to be analyzed is subjected to health status assessment processing to obtain a battery health status score.

[0033] In this embodiment, as Figure 4 and Figure 5 As shown, Figure 4 The tracker periodically sends collection commands. After receiving the command, the tracker collects specific device information from the device and reports it. Subsequently, the system calls the interface to persistently store the reported device data. Finally, this device data is published to the data center in the form of telemetry, completing a complete data collection and reporting cycle.

[0034] As an example Figure 5 The system issues control commands from the management platform, which are transmitted to the target device via the adapter. After receiving the commands, the device performs the corresponding operations and reports the processing results. The system then calls the interface to update the relevant data in the device. Finally, the updated device data is pushed to the data center in a hierarchical publishing manner, completing this control and data synchronization.

[0035] Furthermore, such as Figure 6 As shown, based on the above-described battery health detection method based on the battery charging terminal voltage, the present invention also provides a battery health detection method system based on the battery charging terminal voltage, wherein the battery health detection method system based on the battery charging terminal voltage includes: The battery difference calculation module 51 is used to acquire voltage data, charging time data and historical benchmark data of the battery to be analyzed at the end of the charging stage, and to perform curve alignment processing based on the voltage data, the charging time data and the historical benchmark data to obtain a quantitative difference value. The battery degradation early warning module 52 is used to perform cyclic trend analysis on the quantified difference value according to a preset degradation threshold to obtain a battery capacity degradation early warning signal. The battery health assessment module 53 is used to input the battery capacity decay warning signal into the trained battery health scoring model for analysis, and obtain the battery health status score.

[0036] Furthermore, such as Figure 7As shown, based on the above-described battery health detection method and system based on the battery charging end voltage, the present invention also provides a terminal, which includes a processor 10, a memory 20 and a display 30. Figure 7 Only some of the terminal components are shown; however, it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.

[0037] In some embodiments, the memory 20 may be an internal storage unit of the terminal, such as a hard disk or memory. In other embodiments, the memory 20 may be an external storage device of the terminal, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc. Further, the memory 20 may include both internal and external storage devices. The memory 20 is used to store application software and various types of data installed on the terminal, such as the program code installed on the terminal. The memory 20 can also be used to temporarily store data that has been output or will be output. In one embodiment, the memory 20 stores a battery health detection program 40 based on the battery charging terminal voltage. This battery health detection program 40 can be executed by the processor 10 to implement the battery health detection method based on the battery charging terminal voltage of this application.

[0038] In some embodiments, the processor 10 may be a central processing unit (CPU), a microprocessor, or other data processing chip, used to run program code stored in the memory 20 or process data, such as executing the battery health detection method based on the battery charging end voltage.

[0039] In some embodiments, the display 30 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen. The display 30 is used to display information on the terminal and to display a visual user interface. The terminals communicate with each other via a system bus.

[0040] In one embodiment, when the processor 10 executes the battery health detection program 40 based on the battery charging terminal voltage in the memory 20, the following steps are performed: The voltage data, charging time data, and historical benchmark data of the battery to be analyzed at the end of the charging stage are obtained. Based on the voltage data, the charging time data, and the historical benchmark data, curve alignment processing is performed to obtain the quantitative difference value. Based on a preset attenuation threshold, a cyclical trend analysis is performed on the quantified difference value to obtain a battery capacity attenuation warning signal; The battery capacity degradation warning signal is input into the trained battery health scoring model for analysis to obtain the battery health status score.

[0041] The process of acquiring voltage data, charging duration data, and historical baseline data of the battery to be analyzed at the end of the charging phase, and performing curve alignment processing based on the voltage data, charging duration data, and historical baseline data to obtain a quantified difference value, includes the following preceding steps: Determine the target scenario type of the battery to be analyzed; Based on the target scenario type, determine the health parameters of the battery to be analyzed, and based on the health parameters, determine the target capacity parameters of the battery to be analyzed. The target scenario types include battery material analysis scenario type and mining power supply scenario type; The process involves acquiring voltage data, charging duration data, and historical baseline data of the battery under analysis at the end of the charging phase. Curve alignment is then performed based on the voltage data, charging duration data, and historical baseline data to obtain a quantified difference value. Specifically, this includes: Based on the TCP protocol, voltage data, charging time data, and historical benchmark data of the battery to be analyzed are collected at the end of the charging process. The voltage data, the charging time data, and the historical baseline data are subjected to curve alignment processing to obtain a quantified difference value. The voltage data includes power input voltage, input current, battery output current, battery charge, and battery temperature.

[0042] Specifically, the step of performing curve alignment processing based on the voltage data, the charging time data, and the historical benchmark data to obtain a quantified difference value includes: Based on the voltage data, the charging time data, and the historical benchmark data, curve alignment processing is performed to obtain the quantified difference value: ; in, Indicates the average voltage deviation. Indicates the sampling point. Indicates the sampling point index. Indicates the first The SOC value corresponding to each sampling point Indicates the current voltage curve at Voltage value at that location, Indicates the historical voltage curve at The voltage value at that location.

[0043] Specifically, the step of performing cyclic trend analysis on the quantified difference value based on a preset attenuation threshold to obtain a battery capacity attenuation warning signal includes: Based on a preset attenuation threshold, a cyclic trend analysis is performed on the quantized difference value to obtain the capacity attenuation factor parameter. The number of cycles is obtained, and a battery capacity decay warning signal is determined based on the number of cycles and the capacity decay factor parameter.

[0044] The capacity decay factor parameters include constant current charging duration and charging end voltage; The process of obtaining the number of cycles and determining a battery capacity degradation warning signal based on the number of cycles and the capacity degradation factor parameter specifically includes: Algorithm for obtaining loop count, control commands, and health status; Based on the TCP protocol, the battery under test is charged and discharged according to the control instructions to obtain the constant current charging duration and the charging end voltage. The health status algorithm dynamically calculates the number of cycles, the constant current charging duration, and the charging end voltage to obtain a battery capacity degradation warning signal.

[0045] Specifically, the step of inputting the battery capacity degradation warning signal into the trained battery health scoring model for analysis to obtain a battery health status score includes: The power supply input voltage, input current, battery output current, battery capacity, and battery temperature are divided to obtain a training set and a validation set; The battery health scoring model is trained based on the training set to obtain the trained battery health scoring model. The battery capacity decay warning signal is input into the trained battery health scoring model for analysis to obtain the target capacity decay factor parameter. Based on the target capacity decay parameter, the battery to be analyzed is subjected to a health status assessment to obtain a battery health status score. The validation set is used to validate the performance of the trained battery health scoring model.

[0046] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a battery health detection program based on the battery charging end voltage, and the battery health detection program based on the battery charging end voltage, when executed by a processor, implements the steps of the battery health detection method based on the battery charging end voltage as described above.

[0047] In summary, this invention provides a battery health detection method, system, terminal, and storage medium based on the battery charging terminal voltage. The method includes: acquiring voltage data, charging duration data, and historical benchmark data of the battery to be analyzed at the terminal stage of charging; performing curve alignment processing based on the voltage data, charging duration data, and historical benchmark data to obtain a quantified difference value; performing cyclic trend analysis on the quantified difference value according to a preset attenuation threshold to obtain a battery capacity attenuation warning signal; and inputting the battery capacity attenuation warning signal into a trained battery health scoring model for analysis to obtain a battery health status score. This invention provides early warning of battery capacity attenuation based on voltage data, charging duration data, and historical benchmark data at the terminal stage of battery charging, achieving accurate detection of battery capacity attenuation.

[0048] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal system that includes that element.

[0049] Of course, those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware (such as a processor, controller, etc.). The program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The computer-readable storage medium can be a memory, magnetic disk, optical disk, etc.

[0050] It should be understood that the application of the present invention is not limited to the examples above. Those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims.

Claims

1. A battery health detection method based on the battery charging terminal voltage, characterized in that, The battery health detection method based on the battery charging end voltage includes: The voltage data, charging time data, and historical benchmark data of the battery to be analyzed at the end of the charging stage are obtained. Based on the voltage data, the charging time data, and the historical benchmark data, curve alignment processing is performed to obtain the quantitative difference value. Based on a preset attenuation threshold, a cyclical trend analysis is performed on the quantified difference value to obtain a battery capacity attenuation warning signal; The battery capacity degradation warning signal is input into the trained battery health scoring model for analysis to obtain the battery health status score.

2. The battery health detection method based on the battery charging terminal voltage according to claim 1, characterized in that, The process of acquiring voltage data, charging time data, and historical benchmark data of the battery to be analyzed at the end of the charging phase, and performing curve alignment processing based on the voltage data, charging time data, and historical benchmark data to obtain a quantified difference value, also includes: Determine the target scenario type of the battery to be analyzed; Based on the target scenario type, determine the health parameters of the battery to be analyzed, and based on the health parameters, determine the target capacity parameters of the battery to be analyzed. The target scenario types include battery material analysis scenario type and mining power supply scenario type.

3. The battery health detection method based on the battery charging terminal voltage according to claim 1, characterized in that, The process involves acquiring voltage data, charging duration data, and historical baseline data of the battery under analysis at the end of the charging phase. Curve alignment is then performed based on the voltage data, charging duration data, and historical baseline data to obtain a quantified difference value. Specifically, this includes: Based on the TCP protocol, voltage data, charging time data, and historical benchmark data of the battery to be analyzed are collected at the end of the charging process. The voltage data, the charging time data, and the historical baseline data are subjected to curve alignment processing to obtain a quantified difference value. The voltage data includes power input voltage, input current, battery output current, battery charge, and battery temperature.

4. The battery health detection method based on the battery charging end voltage according to claim 3, characterized in that, The step of performing curve alignment processing based on the voltage data, the charging time data, and the historical benchmark data to obtain a quantified difference value specifically includes: Based on the voltage data, the charging time data, and the historical benchmark data, curve alignment processing is performed to obtain the quantified difference value: ； in, Indicates the average voltage deviation. Indicates the sampling point. Indicates the sampling point index. Indicates the first The SOC value corresponding to each sampling point Indicates the current voltage curve at Voltage value at that location, Indicates the historical voltage curve at The voltage value at that location.

5. The battery health detection method based on the battery charging terminal voltage according to claim 3, characterized in that, The step of performing cyclic trend analysis on the quantified difference value based on a preset attenuation threshold to obtain a battery capacity attenuation warning signal specifically includes: Based on a preset attenuation threshold, a cyclic trend analysis is performed on the quantized difference value to obtain the capacity attenuation factor parameter. The number of cycles is obtained, and a battery capacity decay warning signal is determined based on the number of cycles and the capacity decay factor parameter.

6. The battery health detection method based on the battery charging end voltage according to claim 5, characterized in that, The capacity decay factor parameters include constant current charging duration and charging end voltage; The process of obtaining the number of cycles and determining a battery capacity degradation warning signal based on the number of cycles and the capacity degradation factor parameter specifically includes: Algorithm for obtaining loop count, control commands, and health status; Based on the TCP protocol, the battery under test is charged and discharged according to the control instructions to obtain the constant current charging duration and the charging end voltage. The health status algorithm dynamically calculates the number of cycles, the constant current charging duration, and the charging end voltage to obtain a battery capacity degradation warning signal.

7. The battery health detection method based on the battery charging terminal voltage according to claim 5, characterized in that, The step of inputting the battery capacity degradation early warning signal into the trained battery health scoring model for analysis to obtain a battery health status score specifically includes: The power supply input voltage, input current, battery output current, battery capacity, and battery temperature are divided to obtain a training set and a validation set; The battery health scoring model is trained based on the training set to obtain the trained battery health scoring model. The battery capacity decay warning signal is input into the trained battery health scoring model for analysis to obtain the target capacity decay factor parameter. Based on the target capacity decay parameter, the battery to be analyzed is subjected to a health status assessment to obtain a battery health status score. The validation set is used to validate the performance of the trained battery health scoring model.

8. A battery health detection method system based on the battery charging end voltage, characterized in that, The battery health detection method system based on the battery charging end voltage includes: The battery difference calculation module is used to acquire voltage data, charging time data and historical benchmark data of the battery to be analyzed at the end of the charging stage, and to perform curve alignment processing based on the voltage data, the charging time data and the historical benchmark data to obtain a quantitative difference value. The battery degradation early warning module is used to perform cyclic trend analysis on the quantified difference value according to a preset degradation threshold to obtain a battery capacity degradation early warning signal. The battery health assessment module is used to input the battery capacity degradation warning signal into the trained battery health scoring model for analysis, and obtain the battery health status score.

9. A terminal, characterized in that, The terminal includes: a memory, a processor, and a battery health detection program based on the battery charging terminal voltage stored in the memory and executable on the processor. When the battery health detection program based on the battery charging terminal voltage is executed by the processor, it implements the steps of the battery health detection method based on the battery charging terminal voltage as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a battery health detection program based on the battery charging terminal voltage, which, when executed by a processor, implements the steps of the battery health detection method based on the battery charging terminal voltage as described in any one of claims 1-7.

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