Battery internal resistance measuring method based on pulse type charging and discharging of Poe

Abstract

The invention relates to the technical field of pulse resistance measurement, in particular to a Poe-based pulse type charging and discharging battery internal resistance measurement method. The method comprises the following steps: acquiring a total current state signal and a charging state signal of a battery pack in real time through a controller, and analyzing the total current state signal and the charging state signal to obtain a charging mode identifier; judging whether the battery pack is in a charging state or an idle state based on the charging mode identifier, if the battery pack is in the charging state, activating a bus power supply mode, and acquiring module working electric energy from a control bus through a Poe network interface; and when the charging state is judged, controlling the pulse injection unit to superpose high-frequency pulse charging current to the battery pack, and synchronously acquiring voltage transient data and current waveform data in a pulse action period according to the working electric energy of the module. Through high-frequency pulse charging and discharging, real-time internal resistance calculation and dynamic state monitoring, the problems of hysteresis, insufficient precision and difficult state judgment in traditional battery internal resistance measurement and health assessment are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of pulse resistance measurement, and particularly to a method for measuring the internal resistance of a battery based on pulse charge and discharge of Poe. Background Art

[0002] Most of the methods adopted by traditional battery modules for measuring internal resistance are discharge methods. There are also AC injection methods. These methods are effective for static batteries, but they cannot accurately measure the internal resistance of batteries in the charging state. When the battery is in the equalizing charge state, there is a current flowing into the battery. If the discharge method is used for measurement at this time, it cannot be guaranteed that the current collected by the module completely passes through the battery. Therefore, the battery voltage drop measured at this time is also inaccurate. Summary of the Invention

[0003] Based on this, it is necessary to provide a method for measuring the internal resistance of a battery based on pulse charge and discharge of Poe to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for measuring the internal resistance of a battery based on pulse charge and discharge of Poe is applied to a battery management module powered by a bus. The battery management module includes a controller and a pulse injection unit. The method includes the following steps:

[0005] Step S1: Obtain the total current state signal and charging state signal of the battery pack in real time through the controller, and parse to obtain the charging mode identifier;

[0006] Step S2: Judge whether the battery pack is in the charging or idle state based on the charging mode identifier. If it is in the charging state, activate the bus power supply mode, and obtain the module working electrical energy from the control bus through the Poe network interface;

[0007] Step S3: When it is determined that the battery is in the charging state, control the pulse injection unit to superimpose a high-frequency pulse charging current on the battery pack, and synchronously collect the voltage transient data and current waveform data during the pulse action period according to the module working electrical energy;

[0008] Step S4: If it is determined that the battery is in the idle state, start the internal discharge circuit to apply a pulse discharge load, and obtain the voltage drop data and discharge current characteristic value under the pulse discharge according to the module working electrical energy;

[0009] Step S5: Select the corresponding voltage and current data set according to the charging or idle state; confirm the pulse time window of the high-frequency pulse charging current and the pulse discharge load; use the pulse time window to extract the phase difference between the voltage change rate and the current change rate in the voltage and current data set to calculate the real-time internal resistance value of the battery;

[0010] Step S6: Dynamically compare the real-time internal resistance value of the battery with a preset resistance threshold, generate a battery health status assessment report, and trigger an abnormal alarm signal.

[0011] The present invention monitors the total current status signal and the charging status signal of the battery pack in real time, analyzes the charging mode, and automatically switches the charge and discharge management to optimize energy utilization and improve system energy efficiency. By using a high-frequency pulsed charging current and a pulsed discharge load, voltage transient data and current waveform data are obtained in different modes to accurately calculate the battery internal resistance, thereby dynamically evaluating the battery health status. The working electrical energy of the module is obtained through a Poe network interface to ensure intelligent power supply management. At the same time, the phase difference between the voltage change rate and the current change rate is extracted by combining the pulse time window to improve the accuracy of internal resistance calculation and optimize the battery management strategy. Further, based on the dynamic comparison between the real-time internal resistance value of the battery and a preset threshold, this method can identify battery degradation, aging, or abnormal conditions, generate a health status assessment report, and trigger an abnormal alarm signal to enhance the safety warning ability and prevent safety hazards such as overheating, short circuit, or overcharging. In addition, the pulsed charge and discharge technology reduces the polarization effect, improves the charge and discharge efficiency, extends the battery life, and enhances the overall stability of the system. Combining intelligent health assessment and maintenance strategies, this method can provide effective support for accurate battery management and optimized operation and maintenance, improving the reliability and usage efficiency of the system. Therefore, the present invention solves the problems of lag, insufficient accuracy, and difficulty in state judgment in traditional battery internal resistance measurement and health assessment through high-frequency pulsed charge and discharge, real-time internal resistance calculation, and dynamic status monitoring.

[0012] Preferably, step S1 includes the following steps:

[0013] Step S11: Communicate with the battery management system in real time through a controller to collect the total current status signal of the battery pack;

[0014] Step S12: Obtain the charging status signal of the battery pack through the controller;

[0015] Step S13: Perform timing analysis on the total current status signal to generate battery pack current timing data; analyze the charging mode of the charging status signal according to the battery pack current timing data to generate battery charging mode analysis data;

[0016] Step S14: Classify and identify the charging status signal based on the battery charging mode analysis data to obtain a charging mode identifier, where the mode classification includes a constant current charging mode, a constant voltage charging mode, or a trickle charging mode.

[0017] Through the real-time communication between the controller and the battery management system, the present invention accurately collects the total current status signal and charging status signal of the battery pack, and generates the battery pack current timing data by using timing analysis to improve the accuracy and timeliness of data processing. Based on the battery current timing data, the charging status signal is deeply analyzed to accurately identify the charging mode of the battery, ensuring the intelligentization and adaptive adjustment ability of charge and discharge management. Through mode classification, the constant current charging, constant voltage charging and trickle charging modes can be accurately distinguished, making the charging management more refined, which helps to improve the charge and discharge efficiency of the battery and optimize the energy utilization. This method can not only provide a more accurate charging mode identification, but also serve as an important data basis for subsequent battery status monitoring, health assessment and anomaly detection, improving the safety and stability of the system, extending the battery life, and enhancing the intelligent level of the overall battery management system.

[0018] Preferably, step S2 includes the following steps:

[0019] Step S21: Judge whether the battery pack is in the charging or idle state based on the charging mode identification. If the battery pack is in constant current charging or constant voltage charging, it is confirmed that the battery pack is in the charging state;

[0020] Step S22: If the battery pack is in trickle charging, it is confirmed that the battery pack is in the idle state, and the charging state and the idle state are integrated into the battery pack working state data;

[0021] Step S23: Use the battery pack working state data to judge whether the battery pack is in the charging state, and generate a charging state judgment result; if the charging state judgment result is that the battery pack is in the charging state, activate the bus power supply mode and generate a bus power supply activation signal;

[0022] Step S24: Connect to the control bus through the PoE network interface according to the bus power supply activation signal and monitor the electrical energy state of the battery pack to obtain the working electrical energy of the module.

[0023] The present invention accurately determines the working state of the battery pack through the charging mode identifier, ensuring the intelligence of charge and discharge management. First, it distinguishes whether the battery pack is in the charging or idle state according to the constant current charging, constant voltage charging or trickle charging mode, and integrates the charging and idle state information to form the battery pack working state data, so as to improve the accuracy of state recognition and data integrity. Based on the battery pack working state data, a charging state judgment result is further generated to ensure that the system can accurately respond to different charging demands. When the battery pack is in the charging state, the bus power supply mode is automatically activated and connected to the control bus through the PoE network interface to realize real-time monitoring of the electrical energy state of the battery pack, ensuring the stability and efficiency of power supply. This method can dynamically adapt to different charging modes, optimize the power distribution, avoid energy waste, and at the same time improve the intelligence level and operation efficiency of the system, providing reliable basic data support for subsequent battery health management and anomaly detection.

[0024] Preferably, step S24 includes the following steps:

[0025] Step S241: Initialize and configure the Poe interface according to the bus power supply activation signal to generate Poe network interface connection data;

[0026] Step S242: Collect the battery pack electrical energy data of the control bus through the Poe network interface connection data to obtain the battery pack electrical energy state time series data;

[0027] Step S243: Analyze the electrical energy demand of the battery pack electrical energy state time series data to generate module electrical energy demand data;

[0028] Step S244: Extract the electrical energy change trend of the battery pack electrical energy state time series data, and use the module electrical energy demand data to perform battery pack electrical energy distribution balance processing on the battery pack electrical energy state time series data to generate module working electrical energy.

[0029] The present invention initializes and configures the PoE interface through the bus power supply activation signal to achieve a stable network connection and ensure the accurate collection of the battery pack electrical energy data. Obtain the battery pack electrical energy state time series data of the control bus through the PoE network interface, and use the electrical energy demand analysis to calculate the electrical energy demand of the module to ensure the dynamic matching of battery power supply and module demand. Further, this method extracts the electrical energy change trend of the battery pack electrical energy state time series data, combines the module electrical energy demand data, and realizes the optimal distribution and balance processing of the battery pack electrical energy to ensure power supply stability and improve the electrical energy utilization efficiency. In addition, this method can monitor the electrical energy state in real time, ensure the rationality of the battery pack power supply under different working conditions, avoid electrical energy waste or overload problems, improve the overall operation efficiency and reliability of the system, and at the same time provide accurate data support for subsequent battery health management and anomaly detection.

[0030] Preferably, step S3 includes the following steps:

[0031] Step S31: When it is determined to be in the charging state, activate the bus power supply mode and send a trigger instruction to the pulse injection unit to generate a pulse injection trigger signal;

[0032] Step S32: According to the pulse injection trigger signal, configure the frequency and amplitude parameters of the high-frequency pulse charging current to generate a high-frequency pulse parameter set;

[0033] Step S33: Based on the high-frequency pulse parameter set, superimpose a pulse current on the battery pack bus to generate superimposed pulse current data and record the pulse trigger timestamp;

[0034] Step S34: Synchronously collect the original voltage signal and current signal during the pulse action period in the charging circuit to generate original voltage transient data and original current waveform data;

[0035] Step S35: Perform effective time window data screening on the original voltage transient data and original current waveform data through the pulse trigger timestamp to obtain the voltage transient data and current waveform data during the pulse action period.

[0036] The present invention realizes the optimized management of the charging state of the battery pack by precisely controlling the high-frequency pulse charging current. When the battery pack is in the charging state, the system automatically activates the bus power supply mode and sends a trigger instruction to the pulse injection unit to ensure the precise execution of pulse charging. Subsequently, based on the pulse injection trigger signal, the frequency and amplitude parameters of the high-frequency pulse charging current are configured to form an optimized high-frequency pulse parameter set to adapt to different battery characteristics and improve the charging efficiency. Using this parameter set, the system superimposes a pulse current on the battery pack bus and records the pulse trigger timestamp to ensure the traceability and timing accuracy of the charging process. At the same time, the system synchronously collects the original voltage signal and current signal during the pulse action period in the charging circuit to generate high-precision original voltage transient data and original current waveform data, and performs effective time window data screening through the pulse trigger timestamp to obtain accurate voltage transient data and current waveform data. This method can effectively improve the battery charging efficiency, optimize the pulse charging strategy, improve the acquisition accuracy of voltage and current data, provide high-quality data support for the battery health state assessment, and at the same time reduce the energy loss during the charging process, improve the battery service life and system stability.

[0037] Preferably, step S4 includes the following steps:

[0038] Step S41: When it is determined to be in the idle state, activate the internal discharge circuit and send an activation instruction to the load control unit to generate a discharge circuit trigger signal;

[0039] Step S42: According to the discharge circuit trigger signal, the amplitude and duty cycle parameters of the pulse discharge load are configured to generate a pulse discharge parameter set, wherein the configured amplitude range is 5%-30% of the rated capacity, the pulse width is 200μs-1ms, and the interval period is 10ms-50ms;

[0040] Step S43: controlling the MOSFET switch array to apply a pulse discharge load based on the pulse discharge parameter set, generating pulse discharge current data, and recording a discharge trigger timestamp;

[0041] Step S44: synchronously collecting the transient response signal of the battery terminal voltage and the load current signal in the discharge circuit to generate original voltage drop data and original discharge current data;

[0042] Step S45: performing baseline calibration on the original voltage drop data, and performing bandpass filtering on the original discharge current data to generate calibrated voltage drop data and filtered discharge current data;

[0043] Step S46: intercepting the effective response segments of the calibration voltage drop data and the filtered discharge current data according to the discharge trigger timestamp, thereby obtaining the voltage transient data and current waveform data during the pulse action period.

[0044] The present invention realizes health monitoring and energy management optimization of the battery in an idle state by accurately controlling the pulse discharge process. When the battery pack is in an idle state, the system automatically activates the internal discharge circuit and sends an activation instruction to the load control unit to trigger the precise execution of the discharge circuit. Subsequently, according to the discharge circuit trigger signal, the system dynamically configures the amplitude and duty cycle parameters of the pulse discharge load to form an optimized pulse discharge parameter set to ensure that the discharge process adapts to different battery characteristics and improves the accuracy of data acquisition. On this basis, the pulse discharge load is applied using a MOSFET switch array, and the discharge trigger timestamp is recorded to ensure the timing traceability and accuracy of the discharge process. At the same time, the system synchronously collects the transient response signal of the battery terminal voltage and the load current signal in the discharge circuit to generate high-precision original voltage drop data and original discharge current data. In order to improve the data quality, the method performs baseline calibration on the original voltage drop data and performs bandpass filtering on the original discharge current data to eliminate noise interference and obtain more accurate calibration voltage drop data and filtered discharge current data. Finally, the effective response segment is intercepted by the discharge trigger timestamp, and the voltage transient data and current waveform data during the pulse action period are extracted. This method can effectively improve the accuracy of battery health status monitoring, optimize the pulse discharge strategy, and enhance the reliability of battery performance analysis, while reducing energy loss and increasing battery service life, providing high-quality data support for subsequent battery internal resistance calculation and health status assessment.

[0045] Preferably, the confirmation of the high-frequency pulse charging current and the pulse time window of the pulse discharge load in step S5 includes:

[0046] Perform time alignment processing on the superimposed pulse current data and the pulse discharge current data, calculate the phase matching degree between the two, and generate pulse current time alignment data;

[0047] Extract transient characteristics from the pulse current time alignment data, extract the pulse rising edge, falling edge and peak points, and generate pulse transient characteristic data;

[0048] Perform adaptability analysis on the pulse transient characteristic data, calculate the current change rate and stability under different time windows, and generate pulse time window adaptability data;

[0049] Perform pulse window load optimization analysis on the pulse time window adaptability data to obtain the pulse time window.

[0050] The present invention realizes the dynamic optimization of the pulse time window by accurately identifying the time characteristics of the high-frequency pulse charging current and the pulse discharge load, so as to improve the accuracy of battery internal impedance calculation. First, perform time alignment processing on the superimposed pulse current data and the pulse discharge current data, calculate the phase matching degree between the two, and generate pulse current time alignment data to ensure the timing consistency of the charge and discharge process. Subsequently, perform transient characteristic extraction on the pulse current time alignment data to accurately identify the rising edge, falling edge and peak points of the pulse signal, thereby generating pulse transient characteristic data to capture the key dynamic characteristics of current changes. Based on the pulse transient characteristic data, further carry out adaptability analysis, calculate the current change rate and stability under different time windows, and form pulse time window adaptability data to evaluate the influence of different pulse time windows on the current waveform characteristics. Finally, perform pulse window load optimization analysis on the pulse time window adaptability data to extract the optimal pulse time window to ensure the best matching of the pulse charge and discharge signals. This method can effectively optimize the pulse charge and discharge process, improve the accuracy of battery internal resistance calculation, enhance the reliability of state-of-health assessment, reduce test errors, improve system stability and battery management efficiency.

[0051] Preferably, the calculation of the real-time battery internal resistance by using the phase difference between the voltage change rate and the current change rate in the voltage-current data set by using the pulse time window in step S5 includes:

[0052] Use the pulse time window to extract the voltage data and current data in the voltage-current data set during the pulse action period, and generate a voltage-current data set during the pulse action period;

[0053] Perform numerical differentiation on the voltage-current data set during the pulse action period, calculate the voltage change rate and the current change rate respectively, and generate voltage change rate data and current change rate data;

[0054] Perform a phase difference analysis on the voltage change rate data and the current change rate data, extract the phase offset of the two, and generate voltage-current phase difference data;

[0055] Perform impedance modeling calculation on the voltage-current phase difference data to generate battery internal resistance data;

[0056] Perform trend analysis and calibration on the battery internal resistance data to generate the real-time battery internal resistance value.

[0057] The present invention realizes high-precision calculation of the real-time battery internal resistance by accurately extracting the voltage and current change characteristics during the pulse action period. First, use the pulse time window to extract the voltage-current data set during the pulse action period to generate the voltage-current data set during the pulse action period to ensure the time consistency of data analysis. Subsequently, perform numerical differentiation calculation on this data set to obtain the voltage change rate and the current change rate respectively, thereby generating the voltage change rate data and the current change rate data to reflect the dynamic charge transfer inside the battery. Based on the above data, perform a phase difference analysis on the voltage change rate data and the current change rate data, extract the phase offset of the two, and generate voltage-current phase difference data to characterize the transient impedance characteristics inside the battery. Then, perform impedance modeling calculation on the voltage-current phase difference data to form battery internal resistance data to reflect the change trend of the battery health state. Finally, perform trend analysis and calibration on the battery internal resistance data, eliminate abnormal data and optimize the calculation model, thereby obtaining the real-time battery internal resistance value. This method can effectively improve the accuracy of battery internal resistance measurement, provide a highly reliable diagnostic basis for battery health state assessment, and optimize the energy efficiency strategy of the battery management system.

[0058] Preferably, step S6 includes the following steps:

[0059] Step S61: Dynamically compare the real-time battery internal resistance data with a preset resistance threshold, identify the situation where the resistance exceeds the limit, and generate preliminary battery health state data;

[0060] Step S62: Obtain the battery historical health state data; perform time series trend analysis based on the preliminary battery health state data and the battery historical health state data to generate health state change trend data;

[0061] Step S63: Perform anomaly detection on the health state change trend data to generate battery anomaly detection data;

[0062] Step S64: Evaluate the alarm level of the battery anomaly detection data to generate an anomaly alarm signal;

[0063] Step S65: Integrate the anomaly alarm signal and the health state change trend data to generate a battery health state assessment report.

[0064] Through dynamic comparison and trend analysis, the present invention realizes accurate assessment and abnormal warning of the battery health state. First, the battery internal resistance data calculated in real time is dynamically compared with a preset resistance threshold to identify whether there is a situation where the resistance exceeds the limit, and preliminary battery health state data is generated. Subsequently, the system obtains the historical battery health state data and performs time series trend analysis in combination with the preliminary battery health state data to generate health state change trend data, so as to comprehensively evaluate the long-term change trend of the battery state. Then, abnormal detection is performed on the health state change trend data to identify potential abnormal patterns and generate battery abnormal detection data. Based on this data, the warning level of the abnormal situation is evaluated to determine the severity of the abnormality, and an abnormal warning signal is generated. Finally, the abnormal warning signal and the health state change trend data are integrated to generate a complete battery health state assessment report. This method can provide accurate battery health state monitoring, improve the intelligent level of the battery management system, help to early warn of battery failures, extend the battery service life, and optimize the battery operation and maintenance strategy.

[0065] Preferably, step S63 includes the following steps:

[0066] Step S631: Extract the abnormal change trend characteristics of the health state change trend data, and divide the health state change trend data into data sets according to the abnormal change trend characteristics to generate a model training set and a model test set;

[0067] Step S632: Train the model training set through a convolutional neural network algorithm to generate a preliminary battery abnormal detection model; use the model test set to optimize and iterate the preliminary battery abnormal detection model to generate a battery abnormal detection model;

[0068] Step S633: Import the health state change trend data into the battery abnormal detection model for abnormal detection to generate battery abnormal detection data.

[0069] The present invention deeply analyzes the trend data of the battery health state by using the Convolutional Neural Network (CNN) algorithm, so as to achieve efficient anomaly detection. First, extract the anomaly change trend features in the trend data of the health state, and based on these features, divide the trend data of the health state into a data set to generate a model training set and a model test set. This step lays the foundation for the training and optimization of the model. Subsequently, use the Convolutional Neural Network algorithm to train the model training set to generate a preliminary battery anomaly detection pre-model, and use the model test set to optimize and iterate the pre-model, continuously adjusting and improving the accuracy of the model. Finally, obtain a mature battery anomaly detection model. Finally, import the trend data of the health state into the optimized battery anomaly detection model for real-time anomaly detection to generate battery anomaly detection data. Through this method, the abnormal changes in the battery health state can be accurately identified, the intelligent level of the battery management system can be improved, early warnings of battery failures can be given, and the use and maintenance of the battery can be optimized. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 FIG. is a schematic flow chart of the steps of a battery internal resistance measurement method based on pulsed charge and discharge of Poe;

[0071] Figure 2 is Figure 1 a detailed implementation step flow chart of step S2 in;

[0072] Figure 3 is Figure 1 a detailed implementation step flow chart of step S3 in;

[0073] The realization, functional characteristics and advantages of the object of the present invention will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0074] The technical method of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0075] In addition, the accompanying drawings are only schematic diagrams of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings represent the same or similar parts, and thus their repeated description will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

[0076] It should be understood that although terms such as "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0077] To achieve the above object, please refer to Figures 1 to 3 , a method for measuring the internal resistance of a battery based on pulsed charge and discharge of Poe, the method comprising the following steps:

[0078] Step S1: Obtain the total current status signal and the charge status signal of the battery pack in real time through a controller, and parse to obtain a charge mode identifier;

[0079] Step S2: Judge whether the battery pack is in a charging or idle state based on the charge mode identifier. If it is in a charging state, activate the bus power supply mode, and obtain the module working electrical energy from the control bus through the Poe network interface;

[0080] Step S3: When it is determined that it is in a charging state, control the pulse injection unit to superimpose a high-frequency pulse charging current on the battery pack, and synchronously collect the voltage transient data and current waveform data during the pulse action period according to the module working electrical energy;

[0081] Step S4: If it is determined that it is in an idle state, start the internal discharge circuit to apply a pulsed discharge load, and obtain the voltage drop data and discharge current characteristic values under the pulsed discharge according to the module working electrical energy;

[0082] Step S5: Select the corresponding voltage and current data set according to the charging or idle state; confirm the pulse time window of the high-frequency pulse charging current and the pulsed discharge load; use the pulse time window to extract the phase difference between the voltage change rate and the current change rate in the voltage and current data set to calculate the real-time battery internal resistance value;

[0083] Step S6: Dynamically compare the real-time battery internal resistance value with a preset resistance threshold, generate a battery health status evaluation report and trigger an abnormal alarm signal.

[0084] The present invention optimizes energy utilization and improves system energy efficiency by real-time monitoring the total current status signal and charging status signal of the battery pack, analyzing the charging mode, and automatically switching the charge and discharge management. High-frequency pulsed charging current and pulsed discharge load are used to obtain voltage transient data and current waveform data under different modes, realizing accurate calculation of the battery internal resistance, and thus dynamically evaluating the battery health status. The working electrical energy of the module is obtained through the Poe network interface to ensure the intelligence of the power supply management. At the same time, the phase difference between the voltage change rate and the current change rate is extracted by combining the pulse time window, improving the accuracy of the internal resistance calculation and optimizing the battery management strategy. Further, based on the dynamic comparison between the real-time battery internal resistance value and the preset threshold, this method can identify battery decline, aging or abnormal conditions, generate a health status assessment report, and trigger an abnormal alarm signal to enhance the safety warning ability and prevent safety hazards such as overheating, short circuit or overcharging. In addition, the pulsed charge and discharge technology reduces the polarization effect, improves the charge and discharge efficiency, extends the battery service life, and enhances the overall stability of the system. Combining intelligent health assessment and maintenance strategies, this method can provide effective support for accurate battery management and optimized operation and maintenance, improving the reliability and usage efficiency of the system. Therefore, through high-frequency pulsed charge and discharge, real-time internal resistance calculation and dynamic status monitoring, the present invention solves the problems of lag, insufficient accuracy and difficult status judgment in traditional battery internal resistance measurement and health assessment.

[0085] In the embodiment of the present invention, with reference to Figure 1 shown in the figure, it is a schematic flow chart of the steps of the method for measuring the internal resistance of a battery based on Poe pulsed charge and discharge of the present invention. In this example, the method for measuring the internal resistance of a battery based on Poe pulsed charge and discharge includes the following steps:

[0086] Step S1: The controller obtains the total current status signal and the charging status signal of the battery pack in real time, and analyzes to obtain the charging mode identifier;

[0087] In the embodiments of the present invention, by installing current sensors at the input and output ends of the battery pack, it is ensured that the current changes of the battery pack can be monitored in real time. The sensors selected should have high response speed and accuracy to adapt to rapidly changing current signals. Through the controller or data acquisition module, the total current signals of the battery pack are collected at regular intervals. These signals can be converted from analog signals to digital signals and transmitted to the controller for processing. A charging management module is set in the charging circuit of the battery pack to monitor various states during the charging process, including the charging voltage, current, and temperature of the battery, etc. The charging status signals are transmitted to the controller through the communication interface to ensure that the charging status is updated in real time. The current signals received by the controller are preprocessed through a signal processing module (such as a filter, amplifier, etc.) to eliminate noise and extract useful current data. Through a preset algorithm, the current signals are analyzed to determine whether the battery pack is currently in a discharging, charging, or standby state, or whether there are any abnormal conditions. The controller determines the current charging mode of the battery pack (such as constant current charging, constant voltage charging, trickle charging, etc.) based on the charging status signals, combined with the voltage, current, and temperature data of the battery pack. A charging mode identifier is generated according to the judgment result. The identifier includes "fast charging mode", "conventional charging mode", "trickle charging mode", etc. The charging mode identifier is output through the display module or communication interface of the controller for subsequent charging control or feedback to the user for monitoring.

[0088] Step S2: Determine whether the battery pack is in a charging or idle state based on the charging mode identifier. If it is in a charging state, activate the bus power supply mode and obtain the working electrical energy of the module from the control bus through the Poe network interface;

[0089] In the embodiments of the present invention, the controller determines whether the battery pack is currently in a charging state or an idle state according to the charging mode identifier obtained in step S1. The charging mode identifier includes "charging" or "idle", and the determination is made based on these identifiers. If the charging mode identifier is "charging", it indicates that the battery pack is in a charging state. If the identifier is "idle" or "charging completed", it indicates that the battery pack is in an idle state. When the controller determines that the battery pack is in a charging state, the bus power supply mode is immediately activated. At this time, the controller enables the bus power supply function through the built-in power management module to ensure the working energy supply of the battery pack. The controller switches the power supply to the bus power supply mode according to the charging state of the battery pack, which means that the battery pack can obtain power supply through the control bus in the charging state without consuming the power of the battery itself. The battery pack is connected to the control bus through a PoE (Power over Ethernet) network interface. The PoE interface can provide power through a standard Ethernet cable without the need for an additional power adapter or battery module. After the controller activates the bus power supply mode, the PoE interface obtains the required working power from the control bus, and this power is used to power the module to ensure that the normal operation of the module is not affected during the battery charging process. The controller manages the power transmitted through the PoE interface to ensure reasonable power distribution, and at the same time monitors the charging state of the battery pack to avoid excessive consumption of power from the control bus. The controller continuously monitors the charging current of the battery pack and the power consumption of the module to ensure the balance between battery charging and PoE power supply. If the charging state of the battery pack changes, the controller will adjust the bus power supply mode according to the actual situation. Through the data feedback mechanism, the controller can report to the user or the upper-level system whether the battery pack is in a charging state, whether it obtains power through the PoE interface, as well as the current charging progress and the power consumption of the module.

[0090] Step S3: When it is determined to be in the charging state, the control pulse injection unit superimposes a high-frequency pulse charging current on the battery pack, and synchronously collects the voltage transient data and current waveform data during the pulse action period according to the working power of the module;

[0091] In the embodiments of the present invention, when the controller determines that the battery pack is in a charging state, it controls the pulse injection unit to start working. The pulse injection unit is responsible for superimposing a high-frequency pulsed charging current on the battery pack. These pulses can improve the battery charging efficiency, reduce the internal resistance of the battery, and improve the energy transfer efficiency during the charging process. The controller sets the frequency, amplitude, and duration of the pulses according to the charging characteristics and charging mode of the battery. Generally, the pulse frequency is relatively high (such as in the kHz level), and the pulse width is relatively short to ensure non-invasiveness to the battery charging. The pulse injection unit injects a high-frequency pulsed current according to the set parameters, and the controller adjusts the injection frequency and amplitude of the pulses in real time to ensure the coordination of the pulsed current and the normal charging current during the battery charging process. When the pulse injection unit is working, the controller monitors the voltage and current waveforms of the battery pack in real time through voltage sensors and current sensors. The voltage sensor collects the voltage changes at both ends of the battery pack, while the current sensor collects the current data flowing in the battery pack. To accurately analyze the impact of the pulses on the battery, the controller uses a high-precision data acquisition system to ensure the synchronous acquisition of voltage transient data and current waveform data during the pulse action period. Synchronous acquisition can ensure that the obtained voltage and current data are synchronized with the time of the pulses, facilitating subsequent analysis. The controller sets the acquisition period and sampling accuracy, usually at the microsecond level, to capture the voltage and current details during the high-frequency pulse injection process and avoid missing important instantaneous data. Through the controller or an external storage module, the collected voltage transient data and current waveform data are stored in a database or a data buffer for subsequent analysis. The data can be saved in a time series manner to ensure the timing of the voltage and current data. The controller performs signal processing (such as filtering, denoising, etc.) on the stored data and conducts analysis. The purpose of the analysis is to evaluate the impact of pulse injection on the battery charging process and detect whether the battery exhibits abnormal behaviors (such as overheating, voltage instability, etc.) under the action of high-frequency pulses. Time-domain and frequency-domain analyses are performed on the current waveform and voltage transient data to obtain information on the performance changes of the battery during the charging process, including parameters such as the internal resistance of the battery and the charging efficiency. According to the synchronously collected voltage transient data and current waveform data, the controller can monitor the charging state of the battery pack and the impact of the pulse action in real time. If abnormalities occur during the battery charging process (such as too high or too low voltage, excessive current fluctuations, etc.), the controller will automatically adjust the pulse parameters or stop the pulse injection. The controller generates a feedback report based on the voltage and current data under the action of the pulses and can transmit the data to a host computer or a monitoring system through a network interface for further analysis and decision-making by the user.

[0092] Step S4: If it is determined to be in the idle state, start the internal discharge circuit to apply a pulsed discharge load, and obtain the voltage drop data and discharge current characteristic values under the pulsed discharge action according to the working electrical energy of the module;

[0093] In the embodiments of the present invention, the controller determines whether the battery pack is currently in an idle state based on the charging mode identifier obtained in the previous step S1. If the charging mode identifier of the battery pack is "idle" or "charging completed", the system determines that the battery pack is in an idle state. When the controller determines that the battery pack is in an idle state, it triggers the entry into the discharge management mode. At this time, the system will activate the internal discharge circuit and prepare to apply a pulsed discharge load. The controller controls the internal discharge circuit to apply a pulsed discharge load through a power electronic switch (such as a MOSFET or an IGBT). This load simulates the load state of the battery pack under different usage scenarios and discharges the battery in the form of high-frequency pulses. The frequency, amplitude, and duration of the pulsed discharge are adjusted according to the battery type and its charging and discharging characteristics. The controller determines the intensity and duration of the pulsed discharge load based on the current idle state of the battery. Generally, high-frequency pulsed discharge can help the battery pack to self-regulate and avoid being in a low-load state for a long time. The discharge circuit controls the pulsed current to be applied to the battery pack at specific time intervals, thereby simulating a high-load situation and measuring the response of the battery. During the discharge process, the controller monitors the voltage change of the battery pack in real time through a voltage sensor. Due to the application of the pulsed discharge load, there will be a certain transient change (voltage drop) in the output voltage of the battery. The controller will collect these voltage transient data and record the voltage fluctuations during the pulsed discharge process. By analyzing the characteristics of the voltage drop, the controller can judge the change in the internal resistance of the battery, the discharge efficiency, and the battery health status. A larger voltage drop indicates problems such as battery aging or capacity attenuation, while a smaller drop indicates better battery performance. At the same time, the controller also collects the current waveform data generated by the battery pack during the discharge process through a current sensor. The pulsed discharge will cause changes in the current. The controller obtains the current fluctuations in real time and records the instantaneous values of the current. The controller extracts the characteristics of the collected current waveform and extracts key characteristic values such as the current peak value, average current, and pulse period during the discharge process. The characteristic values of the discharge current can be used to analyze the discharge performance and health status of the battery. Through the relationship between the current waveform data and the battery capacity, discharge efficiency, etc., the controller can evaluate the health status, discharge capacity of the battery, and whether it is suitable for continued use.

[0094] Step S5: Select the corresponding voltage-current data set according to the charging or idle state; confirm the pulse time window of the high-frequency pulsed charging current and the pulsed discharge load; use the pulse time window to extract the phase difference between the voltage change rate and the current change rate in the voltage-current data set to calculate the real-time internal resistance value of the battery;

[0095] In the embodiments of the present invention, in steps S4 and S3, the working state of the battery pack has been judged according to the charging or idle state. When the battery is in the charging state, the system will select the voltage and current data sets during the charging process stored in the controller; when the battery is in the idle state, the voltage and current data sets during the discharging process stored in the controller are selected. Which data set to specifically select is judged by the controller according to the current charging / idle state of the battery to ensure that the voltage and current data set corresponding to the current working state is selected. All the collected voltage and current data have been stored in steps S3 and S4. The controller accesses these data in real time for subsequent analysis. The voltage and current data sets should include the instantaneous voltage, instantaneous current and other related characteristic information during the charging or discharging process. The high-frequency pulse charging current and the pulse time window of the pulse discharge load in the charging or idle state are key parameters. The controller needs to set the duration (time window) of the pulse, as well as the frequency and amplitude of the pulse according to the characteristics of the battery (such as internal resistance, capacity, health state, etc.). The pulse time window usually refers to the time window during which the pulse current is applied, usually at the millisecond to microsecond level. The pulse duration should be dynamically adjusted according to factors such as the battery response time, charging rate, temperature, etc. During the charging or discharging process, the controller ensures the synchronization of pulse injection and data acquisition. The selection of the pulse time window should ensure the accuracy of voltage and current data acquisition during the charging or discharging process of the battery. For the voltage and current data collected during the charging or discharging process, the controller calculates the change rate of each data point, usually by performing differential operations on the voltage and current data: calculate the change rate of voltage with respect to time, that is, ΔV / Δt, and calculate the change rate of current with respect to time, that is, ΔI / Δt. The change rates of voltage and current are closely related within the pulse time window. During the pulse injection period, the changes in voltage and current usually exhibit a certain phase difference, especially when the pulse generates a transient response to the battery. The controller extracts the phase difference between the voltage change rate and the current change rate within the pulse time window. Common methods are through Fourier transform or phase correlation analysis. The phase difference can reflect the response degree of the battery internal resistance to the changes in current and voltage. By calculating the phase difference, the resistance characteristics of the battery can be analyzed. Using the phase difference between the voltage and current change rates, the real-time internal resistance value of the battery can be calculated through Ohm's law. Assume that the internal resistance R of the battery and the voltage change rate and the current change rate The relationship between them is: Where: V 变化率 is the change rate of voltage within the pulse time window; I 变化率 is the change rate of current within the pulse time window. By measuring the voltage and current change rates within the pulse time window, the controller can calculate the battery internal resistance in real time. This value can reflect the health state and charge-discharge performance of the battery. A larger internal resistance usually means battery aging or loss, while a smaller internal resistance means better battery performance.

[0096] Step S6: Dynamically compare the real-time internal resistance value of the battery with a preset resistance threshold, generate a battery health status assessment report, and trigger an abnormal alarm signal.

[0097] In the embodiments of the present invention, different internal resistance thresholds are set according to different types of batteries (such as lithium batteries, nickel-metal hydride batteries, etc.) and application scenarios. Generally, as the battery ages and is used, the internal resistance of the battery gradually increases. The controller sets a set of dynamic resistance thresholds as the standard for judging the battery health status based on information such as the battery model, manufacturer, and usage history. The threshold can be dynamically adjusted according to the working environment of the battery (such as temperature, humidity) and usage history (such as the number of charge and discharge cycles). For example, there are different standards for the internal resistance of the battery under high temperature or long-term load. The controller compares the real-time calculated internal resistance value of the battery with the preset threshold. The comparison process is achieved through mathematical calculations (such as simple inequality judgment): R_real-time ≥ R_threshold. If the real-time internal resistance value is greater than or equal to the preset resistance threshold, it indicates that there are faults or aging problems with the battery. If the real-time internal resistance value is less than the preset resistance threshold, it means that the internal resistance of the battery is within the normal range and the health status is good. The controller generates a battery health status assessment report based on the comparison result of the real-time internal resistance and the resistance threshold. The report includes the following content: record the current internal resistance value of the battery as an important parameter of the health status, display the preset internal resistance threshold for users to compare and reference, and generate a health status assessment according to the internal resistance comparison result (such as: "normal" or "abnormal"). If the internal resistance of the battery exceeds the threshold, the assessment report can include recommended maintenance or replacement suggestions, such as "the battery needs further inspection" or "it is recommended to replace the battery". The battery health status is divided into several levels according to the internal resistance value: Normal: The internal resistance is lower than the threshold, and the battery performance is good. Warning: The internal resistance is close to or slightly exceeds the threshold, and the battery performance begins to degrade, but it can still be used. Abnormal: The internal resistance is much higher than the threshold, and the battery has serious faults or performance decline and needs to be replaced. The generated battery health status assessment report will be output through the user interface, control panel, or monitoring system, and users can view detailed battery health data. If the internal resistance value of the battery exceeds the preset resistance threshold, or the internal resistance continuously increases beyond the set range within multiple cycles, the controller will trigger an abnormal alarm signal. The triggering conditions of the alarm signal include: the real-time internal resistance value is greater than or equal to the preset resistance threshold, the internal resistance of the battery rises sharply in a short time, which is a manifestation of internal battery faults, and within multiple detection cycles, the internal resistance of the battery is always in an abnormal state.

[0098] Preferably, step S1 includes the following steps:

[0099] Step S11: Collect the total current status signal of the battery pack through real-time communication between the controller and the battery management system;

[0100] Step S12: Obtain the charging status signal of the battery pack through the controller;

[0101] Step S13: Perform timing analysis on the total current status signal to generate battery pack current timing data; perform charging mode analysis on the charging status signal according to the battery pack current timing data to generate battery charging mode analysis data;

[0102] Step S14: Classify and identify the charging status signal based on the battery charging mode analysis data to obtain a charging mode identifier, where the mode classification includes a constant current charging mode, a constant voltage charging mode, or a trickle charging mode.

[0103] In an embodiment of the present invention, the controller uses communication protocols such as I2C, SPI, CAN, etc. to transmit data through a real-time communication interface with the Battery Management System (BMS). This interface needs to ensure data real-time performance and reliability. The controller periodically or continuously reads the total current status signal of the battery pack from the battery management system. This signal represents the real-time current change during the charging process of the battery pack, including information such as charging current and discharging current. The total current status signal of the battery pack is usually obtained through a current sensor (such as a Hall sensor), and the signal contains the current magnitude, direction (charging or discharging), and fluctuation characteristics of the battery pack. The controller ensures the integrity and timeliness of the data by sampling and recording these current signals at regular intervals. The controller obtains the charging status signal of the battery pack from the battery management system, which indicates the charging status of the battery pack, such as charging, discharging, idle, etc. The acquisition of the charging status signal is determined by the charging voltage, charging current, or other sensor data in the battery management system. The controller ensures the synchronous acquisition of the battery pack charging status signal and the total current status signal for subsequent data analysis. The controller performs a timing analysis on the total current status signal, that is, performs time-domain data processing on the current signal of the battery pack. By sampling and performing timing analysis on the current signal, the change trend of the battery pack current is obtained. During the timing analysis process, a filtering algorithm can be used to remove noise to ensure the accuracy of the current signal. By analyzing the change of the current waveform, the controller can capture the time-varying characteristics of the current of the battery pack at different charging stages (such as constant current stage, constant voltage stage, etc.). The current timing data includes the current value, change rate, and trend information at each time point, which provides a basis for subsequent charging mode analysis. The controller analyzes the current timing data of the battery pack and combines it with the charging status signal of the battery pack to parse the current battery charging mode. The analysis of the charging mode is to judge which charging stage the battery is in by analyzing the change of the current signal. Constant current charging mode: When the current of the battery pack remains at a constant value, it indicates that the battery is in the constant current charging stage. Constant voltage charging mode: When the voltage of the battery pack approaches the set maximum charging voltage value and the current gradually decreases, it indicates that the battery enters the constant voltage charging stage. Trickle charging mode: When the battery charging is nearly complete, the current becomes very small and approaches zero, the battery enters the trickle charging stage to maintain the charging state of the battery. Through the analysis of the charging mode, the controller classifies the charging process of the battery into the above three charging modes and generates corresponding charging mode analysis data. The analysis data includes the charging mode identifier, charging current, and voltage information for each time period. The controller classifies according to the charging mode analysis data: If the current of the battery pack is in a constant state and in the charging state, it is the "constant current charging mode". If the voltage of the battery pack approaches the set value and the current decreases, it is the "constant voltage charging mode". If the current of the battery pack is extremely low and the charging is complete, it is the "trickle charging mode".The controller generates a charging mode identifier and records this identifier together with other data in the battery management system for subsequent battery state analysis and optimization. This identifier can be used for subsequent adjustment of the charging management strategy, optimization of the charging process, and prediction of battery life.

[0104] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes:

[0105] Step S21: Based on the charging mode identifier, determine whether the battery pack is in a charging or idle state. If the battery pack is in constant current charging or constant voltage charging, confirm that the battery pack is in a charging state;

[0106] Step S22: If the battery pack is in trickle charging, confirm that the battery pack is in an idle state, and integrate the charging state and the idle state into the battery pack working state data;

[0107] Step S23: Use the battery pack working state data to determine whether the battery pack is in a charging state, and generate a charging state determination result; if the charging state determination result is that the battery pack is in a charging state, activate the bus power supply mode and generate a bus power supply activation signal;

[0108] Step S24: Connect to the control bus through the PoE network interface according to the bus power supply activation signal and monitor the electrical energy state of the battery pack to obtain the working electrical energy of the module.

[0109] In an embodiment of the present invention, the controller first determines the current charging state of the battery pack according to the charging mode identifier. If the charging mode identifier indicates "constant current charging mode" or "constant voltage charging mode", the controller confirms that the battery pack is charging. At this time, the current of the battery pack is a constant value, and the battery is in the charging process. The controller determines it as the "charging state". At this time, the voltage of the battery pack is close to the preset charging voltage, and the current gradually decreases. The charging state of the battery is maintained, but the current will gradually decrease, still regarded as the "charging state". The controller marks it as the "charging state" according to the charging mode identifier and the current state of the battery pack. The controller determines whether the battery pack is in the "trickle charging mode" according to the charging mode identifier. At this time, the current of the battery pack is very small, close to zero, and the charging process is nearly completed. The controller regards this as the battery pack being in the "idle state". Whether the battery pack is in the charging state or the idle state, the controller integrates the information of the charging state and the idle state into the "battery pack working state data". The controller uses the battery pack working state data to determine whether the battery pack is currently in the charging state. If the battery pack working state data indicates that it is in the charging state, the controller confirms that the charging state determination result is the "charging state". When the battery pack is in the charging state, the controller activates the "bus power supply mode" to support the charging of the battery. The bus power supply mode can provide working electrical energy to the battery pack through the PoE (Power over Ethernet) network interface, thus supporting the continuous progress of the charging process. After activating the bus power supply mode, the controller generates a "bus power supply activation signal" to notify other modules of the system to prepare for power supply. The controller connects to the control bus through the PoE (Power over Ethernet) network interface by means of the bus power supply activation signal. The PoE interface can transmit electrical energy through the Ethernet, realizing the dual functions of network and power supply, thus reducing the need for a separate power supply line. By connecting to the PoE interface of the battery pack, the controller can monitor the electrical energy state of the battery pack in real time and obtain the current working electrical energy information of the battery pack. The electrical energy monitoring data of the battery pack will include key parameters such as the voltage, current, and remaining power of the battery, which are used for subsequent analysis and optimization of the charging strategy. The controller obtains the working electrical energy data of the module through the PoE interface, and this data is used to analyze the energy efficiency of the battery pack and the charging / discharging state of the battery, and further optimize the use and charging process of the battery.

[0110] Preferably, step S24 includes the following steps:

[0111] Step S241: Initialize and configure the Poe interface according to the bus power supply activation signal to generate Poe network interface connection data;

[0112] Step S242: Collect the battery pack electrical energy data of the control bus through the Poe network interface connection data to obtain the battery pack electrical energy state time series data;

[0113] Step S243: Conduct power demand analysis on the time-series data of the battery pack's power state to generate module power demand data;

[0114] Step S244: Extract the power change trend of the time-series data of the battery pack's power state, and use the module power demand data to perform battery pack power distribution balance processing on the time-series data of the battery pack's power state to generate module operating power.

[0115] In the embodiments of the present invention, after receiving the bus power supply activation signal, the controller initializes and configures the PoE network interface of the battery pack through the PoE interface. The initialization and configuration include setting the communication protocol, data transmission rate, power supply mode, etc. of the PoE interface to ensure the stability of subsequent data collection and power transmission. This process can be achieved by sending configuration commands to the PoE interface and confirming whether the configuration of the feedback confirmation interface is successful. After the configuration is completed, the controller generates PoE network interface connection data, which includes the connection status, configuration parameters, and power supply capacity of the PoE interface. These connection data will be used for subsequent power collection and data transmission to ensure the normal communication and power supply between the battery pack and the control bus. The controller uses the PoE network interface connection data to connect to the control bus through the PoE interface. The controller communicates with the battery pack through this interface and regularly obtains the power status data of the battery pack. The power status data of the battery pack includes information such as battery voltage, current, charge / discharge status, and remaining battery power. These data are collected in a time-series manner, that is, the power status of the battery pack at each time point is recorded in a time series to form the time-series data of the battery pack power status. The collected data is transmitted to the control bus and stored in real time through the PoE network interface for subsequent analysis. The controller analyzes the time-series data of the battery pack power status to identify the power demand pattern of the battery pack. The analysis process involves the load status, current fluctuation, charge status, etc. of the battery pack to evaluate the current power level required by the battery pack. According to the power status and load demand of the battery pack, the controller generates module power demand data, which represents the power required by the battery pack in different time periods. The module power demand data includes the amount of electrical energy that the battery pack needs to provide at each moment and the time window of these power demands. This power demand data is used for subsequent battery energy allocation and optimization to ensure that the battery pack optimizes the power usage efficiency while meeting the working requirements. The controller analyzes the time-series data of the battery pack power status to extract the change trend of the battery power, including the change rate and direction of voltage and current. By calculating the fluctuation trend of the battery pack power status, the controller can identify the energy change pattern of the battery pack during the charging and discharging processes. Using the module power demand data, the controller allocates and schedules the time-series data of the battery pack power status to ensure the reasonable allocation of the battery pack energy. The goal of the battery pack power distribution balance processing is to balance the power supply of each module according to the power demand of the battery pack to avoid overcharging or over-discharging. This process involves dynamically adjusting the charging rate, load current, etc. of the battery pack to ensure the health status and working stability of the battery pack. The power status data of the battery pack after the balance processing is used to generate the module working power.

[0116] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes:

[0117] Step S31: When it is determined to be in the charging state, activate the bus power supply mode and send a trigger instruction to the pulse injection unit to generate a pulse injection trigger signal;

[0118] Step S32: Configure the frequency and amplitude parameters of the high-frequency pulse charging current according to the pulse injection trigger signal to generate a high-frequency pulse parameter set;

[0119] Step S33: Superimpose a pulse current on the battery pack bus based on the high-frequency pulse parameter set to generate superimposed pulse current data and record the pulse trigger timestamp;

[0120] Step S34: Synchronously collect the original voltage signal and current signal during the pulse action period in the charging circuit to generate original voltage transient data and original current waveform data;

[0121] Step S35: Perform effective time window data screening on the original voltage transient data and original current waveform data through the pulse trigger timestamp to obtain the voltage transient data and current waveform data during the pulse action period.

[0122] In the embodiments of the present invention, it is determined whether the battery pack is in a charging state according to the charging state determination result in the previous steps. If it is confirmed that the battery pack is in a charging state, the next operation is entered. When the charging state is confirmed, the controller sends a power supply activation signal to the battery pack bus through the PoE interface or other power management modules. The activation signal ensures that the electrical energy of the battery pack can be supplied to the charging circuit through the bus to support subsequent charging operations. The controller sends a trigger instruction to the pulse injection unit, and the trigger unit prepares to inject a high-frequency pulsed charging current. The trigger instruction includes information such as the frequency, amplitude, and duration of the pulse, ensuring that the pulse injection unit charges according to the preset parameters. The controller generates and sends a pulse injection trigger signal to indicate that the pulse injection unit starts to work and superimposes a high-frequency pulsed charging current on the battery pack. The controller configures the frequency and amplitude parameters of the pulsed current according to the pulse injection trigger signal. The frequency and amplitude parameters need to be optimized according to the charging requirements of the battery pack to ensure the stability of the charging process and the health of the battery. The frequency of the high-frequency pulsed current is set in the range of several kHz to dozens of kHz, and the amplitude is adjusted according to the load capacity and safety limit of the battery pack. The controller generates a detailed parameter set of the pulsed current according to the configuration, including data such as frequency, amplitude, and duration. This parameter set is used to precisely control the working state of the pulse injection unit to ensure that the pulsed current is superimposed according to the predetermined requirements. The controller injects a high-frequency pulsed charging current into the bus of the battery pack based on the high-frequency pulse parameter set. This operation generates a superimposed high-frequency pulsed current in the battery charging circuit by controlling the pulse injection unit, and the amplitude and frequency of the superimposed current conform to the previously configured pulse parameter set. The injected pulsed current flows through the battery pack bus to generate superimposed pulsed current data. This data includes the instantaneous change of the current under the action of the pulse. The controller records the exact timestamp of the pulse injection trigger for subsequent data synchronization and time window screening. While injecting the pulsed current, the controller real-time collects the voltage and current signals in the charging circuit. The voltage signal is the instantaneous change of the battery pack bus voltage under the action of the pulse, and the current signal is the current fluctuation passing through the battery pack in the charging circuit. The voltage and current signals are recorded in real time by the acquisition system to form the original voltage transient data and the original current waveform data, which can provide basic data for subsequent internal resistance calculation and battery health state analysis. Using the pulse trigger timestamp recorded in step S33, the controller performs time window screening on the original voltage transient data and the original current waveform data. The screening process will select the data during the pulse action period (i.e., the time period from the start of the pulse trigger to the end of the pulse), and remove the irrelevant parts. The screened data is the voltage transient data and the current waveform data during the pulse action period, which are used for subsequent analysis and calculation.

[0123] Preferably, step S4 includes the following steps:

[0124] Step S41: When it is determined to be in the idle state, activate the internal discharge circuit and send an activation instruction to the load control unit to generate a discharge circuit trigger signal;

[0125] Step S42: According to the discharge circuit trigger signal, configure the amplitude and duty cycle parameters of the pulsed discharge load to generate a pulsed discharge parameter set, where the configured amplitude range is 5%-30% of the rated capacity, the pulse width is 200 μs - 1 ms, and the interval period is 10 ms - 50 ms;

[0126] Step S43: Based on the pulsed discharge parameter set, control the MOSFET switch array to apply the pulsed discharge load, generate pulsed discharge current data, and record the discharge trigger timestamp;

[0127] Step S44: Synchronously collect the transient response signal of the battery terminal voltage and the load current signal in the discharge circuit to generate the original voltage drop data and the original discharge current data;

[0128] Step S45: Perform baseline calibration on the original voltage drop data and apply band-pass filtering to the original discharge current data to generate calibrated voltage drop data and filtered discharge current data;

[0129] Step S46: According to the discharge trigger timestamp, intercept the effective response segments of the calibrated voltage drop data and the filtered discharge current data to obtain the voltage transient data and current waveform data during the pulse action period.

[0130] In an embodiment of the present invention, based on the data parsed from the battery pack charging mode and the charging state judgment result by the controller, it is confirmed that the battery pack is in an idle state (such as the trickle charging mode). In the idle state, the controller activates the discharge circuit through the internal discharge circuit control module to apply a pulsed discharge load. The controller sends an activation instruction to the load control unit, instructing it to start working and generate a discharge circuit trigger signal. The discharge circuit trigger signal instructs the control unit to apply a load according to the preset pulse parameters to discharge the battery. The controller sets the amplitude of the pulsed discharge load according to the state and requirements of the battery. The amplitude configuration range is 5% to 30% of the rated capacity of the battery. For example, if the rated capacity of the battery is 100 Ah, the amplitude of the pulsed discharge load is configured between 5 Ah and 30 Ah. The controller further configures the pulse width and duty cycle of the pulsed discharge load: the pulse width range is set to 200 μs to 1 ms to ensure that the pulse intensity of the discharge load is moderate. The interval period is set to 10 ms to 50 ms to avoid overheating of the battery pack or causing over-discharge. According to the above settings, the controller generates a pulsed discharge parameter set, including the specific values of the amplitude, pulse width, and interval period, and this parameter set is used for subsequent precise control of the pulsed discharge load. The controller applies the pulsed discharge load through the MOSFET switch array to precisely control the amplitude and pulse width of each discharge pulse. The switching operation of the MOSFET array is based on the pulsed discharge parameter set to ensure that the discharge process conforms to the preset current and time parameters. When the discharge pulse acts on the battery, the pulsed discharge current data is recorded in real time through the current sensor, reflecting the current response of the battery under the pulsed load. The controller records the timestamp of the discharge trigger signal to ensure accurate time alignment of the current signal and the voltage signal during subsequent data analysis. The controller synchronously collects the battery terminal voltage signal and the load current signal in the discharge circuit through the sensor to ensure that the two signal data are consistent in time. The battery terminal voltage signal reflects the voltage drop under the action of the discharge pulse. The load current signal records the current fluctuations when the discharge pulse is applied, and these raw data are used for subsequent calibration and filtering processing. Baseline calibration is performed on the original voltage drop data. The purpose of baseline calibration is to eliminate the deviation of the measurement device or the non-essential fluctuations caused by the change of the battery state. Baseline calibration includes offset adjustment of the original data through the no-load voltage of the battery. A band-pass filter is applied to the original discharge current data to remove the high-frequency noise and low-frequency drift in the current signal to obtain a more stable current waveform. The setting of the band-pass filter will be optimized according to the characteristics of the battery discharge to ensure that the current waveform truly reflects the pulsed discharge load. The processed voltage data is the calibrated voltage drop data, eliminating the influence of baseline offset. The filtered current data is the filtered discharge current data, removing the noise components and accurately reflecting the current change during the discharge process. The controller performs time window screening on the calibrated voltage drop data and the filtered discharge current data according to the discharge trigger timestamp recorded in step S43.Filter out the effective time period related to the action of the pulsed discharge load and remove the irrelevant data segments. The data obtained after time window filtering are the voltage transient data and current waveform data during the pulse action period.

[0131] Therefore, from any perspective, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the application documents are intended to be encompassed by the present invention.

[0132] Preferably, the confirmation of the high-frequency pulse charging current and the pulse time window of the pulsed discharge load in step S5 includes:

[0133] Perform time alignment processing on the superimposed pulse current data and the pulsed discharge current data, calculate the phase matching degree between the two, and generate pulsed current time alignment data;

[0134] Extract transient characteristics from the pulsed current time alignment data, extract the pulse rising edge, falling edge and peak points, and generate pulsed transient characteristic data;

[0135] Perform adaptive analysis on the pulsed transient characteristic data, calculate the current change rate and stability under different time windows, and generate pulsed time window adaptive data;

[0136] Perform pulsed window load optimization analysis on the pulsed time window adaptive data to obtain the pulsed time window.

[0137] In the embodiments of the present invention, the superimposed pulse current data and the pulse discharge current data are respectively from the charging circuit and the discharging circuit, and these data have been recorded in the previous steps S33 and S43. The original data of the pulse charging current and the pulse discharge current are aligned by timestamps. The controller ensures the consistency of the time series of the two data sets according to the recorded timestamps. Using the cross-correlation analysis method, the phase matching degree between the superimposed pulse current data and the pulse discharge current data is calculated. By calculating the time delay and the phase difference, the matching degree between the two is judged. The calculation result of the phase matching degree can be a correlation coefficient. When the value is close to 1, it indicates that the phases of the two are completely matched, and when it is close to 0, it indicates a low matching degree. According to the calculated phase matching degree, pulse current time alignment data are generated. These data are the accurate alignment results of the two signals in time, ensuring that the effects of pulse charging and discharging can be synchronously compared in subsequent analyses. Based on the pulse current time alignment data, signal processing methods (such as differentiation and filtering) are used to extract the transient characteristics of the current waveform. Pulse rising edge: refers to the starting point when the current rapidly rises from the lowest value to the peak value. Pulse falling edge: refers to the ending point when the current rapidly drops from the peak value to the lowest value. Peak point: refers to the maximum value of the pulse current, which can also be defined as the maximum amplitude point of the waveform. The above transient characteristic data are extracted, and the positions and amplitudes of the pulse rising edge, falling edge, and peak point are recorded. This data can provide the key dynamic characteristics of the current waveform. Based on the pulse transient characteristic data, adaptive analysis is implemented. The controller divides the pulse current signal into multiple time windows, and evaluates the adaptability of the pulse time windows by calculating the current change rate and stability within each time window. Within each time window, the current change rate (for example, the amount of current change per unit time) is calculated. If the current change rate is too large, it means that the pulse is too intense and affects the health of the battery; if the change is too small, it cannot provide an effective charging or discharging effect. By analyzing the stability of the current waveform (such as calculating the fluctuation amplitude or standard deviation), the stability of the pulse is evaluated. Pulse time windows with higher stability usually can more effectively avoid excessive fluctuations and damage to the battery. Pulse time window adaptability data are generated according to the calculated current change rate and stability. This data includes the current rate change conditions, stability evaluations, and adaptive analysis results under different time windows, and is used for subsequent optimization processing. Based on the pulse time window adaptability data, the controller implements optimization analysis. The goal of the optimization analysis is to select the pulse time window that is most suitable for the battery health state and the charge and discharge requirements. The controller will select the optimal time window according to the adaptability data of different time windows to balance the charge or discharge load and the working state of the battery. Through the pulse window load optimization analysis, the most suitable pulse time window is selected. This time window ensures that during the pulse charging or discharging process, the current change rate and stability reach the best balance, avoiding excessive stress on the battery.

[0138] Preferably, the step S5 of calculating the real-time internal resistance of the battery by extracting the phase difference between the voltage change rate and the current change rate in the voltage-current dataset using a pulse time window includes:

[0139] Using the pulse time window to extract the voltage data and current data in the voltage-current dataset during the pulse action period to generate a voltage-current dataset during the pulse action period;

[0140] Performing numerical differentiation on the voltage-current dataset during the pulse action period to calculate the voltage change rate and the current change rate respectively, and generating a voltage change rate data and a current change rate data;

[0141] Performing phase difference analysis on the voltage change rate data and the current change rate data, extracting the phase offset between the two, and generating voltage-current phase difference data;

[0142] Performing impedance modeling calculation on the voltage-current phase difference data to generate battery internal resistance data;

[0143] Performing trend analysis and calibration on the battery internal resistance data to generate the real-time internal resistance value of the battery.

[0144] In the embodiments of the present invention, voltage transient data and current waveform data during the pulse action period (these data are from the processing results of steps S34 and S46) are used to extract voltage and current signals within the pulse time window. This part of the data includes the real-time changes of the voltage and current of the battery pack under the action of the pulse during pulse charging or discharging, ensuring that the data is synchronized and collected within the same time period. The waveforms of voltage and current are extracted from it to form a complete data set including the pulse action period. This data set includes the time series of voltage and current under the action of the pulse, which is used for subsequent differential analysis. For the extracted voltage and current data during the pulse action period, numerical differential methods (such as the finite difference method) are used to perform differential processing on them to calculate the rate of change of voltage and current with time. Voltage change rate (dV / dt): represents the speed of voltage change with time. Current change rate (dI / dt): represents the speed of current change with time. According to the differential results, voltage change rate data and current change rate data are respectively generated, and these data will be used for subsequent phase difference analysis to help determine the timing relationship between voltage and current changes. Phase analysis techniques (such as Fourier transform or cross-correlation analysis) are used to analyze the phase relationship between the voltage change rate and the current change rate. Calculate the phase difference between the voltage change rate data and the current change rate data to determine the timing offset between them. Usually, there is a certain phase difference between voltage and current at certain frequencies, which reflects the resistive, capacitive, and inductive characteristics inside the battery. Extract the phase offset amount between voltage and current from the analysis results to obtain the specific phase difference value. This phase difference can be used as the basis for subsequent battery internal resistance calculation. Record and generate voltage-current phase difference data, which is used to reflect the synchronization relationship between voltage and current and helps to judge the electrochemical characteristics of the battery. According to the voltage-current phase difference data, impedance modeling calculation is performed using the equivalent circuit model of the battery (usually an R-C or R-L model). By analyzing the phase difference between voltage and current and combining the electrochemical characteristics of the battery, the internal resistance of the battery is calculated. The internal resistance calculation formula usually involves Ohm's law and the electrochemical reaction parameters inside the battery. Through the fitting of the phase difference data and the battery model, the internal resistance data of the battery is generated. Through the above impedance modeling calculation, real-time battery internal resistance data is obtained, and these data can reflect the health state of the battery and help to judge the charge and discharge efficiency and state of the battery. Perform trend analysis on the generated battery internal resistance data to observe the change of internal resistance with time. Trend analysis can reveal the rising or falling trend of the battery internal resistance, reflecting the degree of battery aging or damage. Based on historical data and known battery performance standards, the real-time internal resistance data is calibrated. The calibration process includes correcting the change of the battery internal resistance to ensure its consistency with the actual working state of the battery. By comparing with the preset standard internal resistance value, the accuracy and reliability of the internal resistance data are ensured. Finally, through trend analysis and calibration, the real-time battery internal resistance value is obtained, which can reflect the health state of the battery and serve as an important basis for subsequent battery maintenance and use decisions.

[0145] Preferably, step S6 includes the following steps:

[0146] Step S61: Dynamically compare the real-time battery internal resistance data with a preset resistance threshold to identify cases of resistance exceeding the limit and generate preliminary battery health status data;

[0147] Step S62: Obtain the battery historical health status data; perform time series trend analysis based on the preliminary battery health status data and the battery historical health status data to generate health status change trend data;

[0148] Step S63: Perform anomaly detection on the health status change trend data to generate battery anomaly detection data;

[0149] Step S64: Evaluate the warning level of the battery anomaly detection data to generate an anomaly warning signal;

[0150] Step S65: Integrate the anomaly warning signal with the health status change trend data to generate a battery health status evaluation report.

[0151] In the embodiments of the present invention, the current battery internal resistance value is extracted from the real-time battery internal resistance data generated in step S55. This real-time battery internal resistance value is dynamically compared with a preset resistance threshold. This resistance threshold is usually the maximum allowable internal resistance value set based on the battery specifications, usage environment, and historical data. If the real-time battery internal resistance value exceeds the preset threshold, it is identified that the battery health state is abnormal, and preliminary battery health state data is generated. According to the internal resistance comparison result, the battery health state data is obtained. If the battery internal resistance is normal, the health state is good; if the internal resistance exceeds the limit, the battery health state is abnormal. The historical health state data of the battery is obtained from the battery management system (BMS) or related database. These data usually include historical monitoring data such as battery internal resistance, temperature, current, etc., which can reflect the long-term health changes of the battery. The preliminary battery health state data is combined with the historical health state data, and trend analysis is performed based on time series analysis methods (such as moving average, regression analysis, time series prediction models, etc.) to generate health state change trend data, which describes the change of the battery health state over time, such as the increasing trend of internal resistance, the decline of charge and discharge performance, etc. By analyzing the change trend of the battery health state in the past period of time, a data model is generated to predict the future health condition of the battery. Anomaly detection algorithms (such as statistics-based methods, machine learning anomaly detection models, etc.) are used to analyze the health state change trend data. A threshold is set or a model is used to detect mutations, abnormal fluctuations, or trend deviations in the battery health state data, such as abnormal growth of internal resistance, sharp decline in performance, etc. If the health state change trend data shows abnormal fluctuations or trend deviations, battery anomaly detection data is generated, indicating that there are problems with the battery, such as faults or performance decline. According to the battery anomaly detection data, the severity of the anomaly is evaluated. Common evaluation criteria include: Minor alarm: such as a slight increase in internal resistance, but not reaching the threshold warning. Moderate alarm: such as the internal resistance approaching the threshold or a more obvious decline in performance. Severe alarm: such as the internal resistance significantly exceeding the standard or a major fault occurring in the battery. According to the evaluation result, the corresponding alarm signal is generated. The alarm signal should include the alarm level, alarm type (such as internal resistance exceeding the limit, performance decline, etc.), and recommended subsequent treatment measures (such as stopping charging, immediately replacing the battery, etc.). The abnormal alarm signal is integrated with the health state change trend data. The integrated data includes the historical health state, current health state, anomaly detection result, alarm level, and trend analysis of the battery. Based on the integrated data, a detailed battery health state evaluation report is generated.

[0152] Preferably, step S63 includes the following steps:

[0153] Step S631: Extract the abnormal change trend characteristics of the health state change trend data, and divide the health state change trend data into data sets according to the abnormal change trend characteristics to generate a model training set and a model test set;

[0154] Step S632: Perform model training on the model training set through the convolutional neural network algorithm to generate a battery anomaly detection pre-model; use the model test set to perform model optimization iteration on the battery anomaly detection pre-model, thereby generating a battery anomaly detection model;

[0155] Step S633: Import the health state change trend data into the battery anomaly detection model for anomaly detection to generate battery anomaly detection data.

[0156] In the embodiments of the present invention, by analyzing the health status change trend data, the abnormal change trend features therein are extracted, and these features may include: sudden health status fluctuations or sharp changes, such as a sharp increase in the internal resistance of the battery within a short period of time. Long-term health decline trends, such as a gradual decrease in the discharge capacity of the battery. Short-term abnormal events, such as current fluctuations of the battery, abnormal fluctuations during the charging process, etc. Statistical methods or rule-based detection methods are used to identify these abnormal changes. According to the extracted abnormal change trend features, the health status change trend data is divided into data sets. A common division method is: Model training set: includes most of the historical data, especially normal samples of health status changes and some data samples with known abnormalities. Model test set: includes a part of unknown abnormal samples selected from the historical data as verification data after model optimization. Ensure that the data set division is balanced and covers sufficient normal and abnormal change trend data to ensure the representativeness and efficiency of model training and testing. The training set is trained using the Convolutional Neural Network (CNN) algorithm. CNN is a deep learning model that is good at processing time series data and image data. In the health status change trend data, CNN can automatically extract deep-level features, such as the time series pattern of battery abnormal changes. During the training process, the network will automatically learn how to identify abnormal patterns from the health status change trend data and generate a prediction label (normal or abnormal) for each input sample. The battery anomaly detection pre-model is evaluated using the test set to examine performance metrics such as the accuracy, recall rate, and F1-score of the model. If the performance of the model is poor, it is optimized by adjusting the hyperparameters, number of layers, convolutional kernel size, learning rate, etc. of the CNN. Through multiple iterations of optimization, the recognition accuracy of the battery anomaly detection model is improved, and the occurrence of false positives and false negatives is reduced. After multiple rounds of optimization, a final battery anomaly detection model is generated, which can efficiently and accurately detect abnormal changes in the battery health status. The new health status change trend data is input into the trained battery anomaly detection model for real-time anomaly detection. These input data include the latest battery health status changes, internal resistance changes, charging process, discharging process, etc. The trained battery anomaly detection model is used to identify abnormalities in the input data. The model determines whether the current battery state is abnormal by comparing the input data with the previously learned abnormal patterns. According to the output of the model, battery anomaly detection data is generated. This data includes the current anomaly detection results of the battery, such as whether it is in an abnormal state and the type of abnormality (for example: increased internal resistance, voltage fluctuation, unstable charge and discharge, etc.). According to the degree of abnormality, the model can also generate a severity classification of the abnormality, such as minor abnormality, moderate abnormality, or severe abnormality.

[0157] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features invented herein.

Claims

1. A method for measuring the internal resistance of a battery based on pulsed charge and discharge of Poe, characterized in that, Applied to a bus-powered battery management module, the battery management module includes a controller and a pulse injection unit, and comprises the following steps: Step S1: The controller obtains the total current status signal and the charging status signal of the battery pack in real time, and parses to obtain a charging mode identifier; Step S2: Based on the charging mode identifier, it is judged whether the battery pack is in a charging or idle state. If it is in a charging state, the bus-powered mode is activated, and the module operating electrical energy is obtained from the control bus through the Poe network interface; Step S3: When it is determined that it is in a charging state, the control pulse injection unit superimposes a high-frequency pulse charging current on the battery pack, and synchronously collects the voltage transient data and current waveform data during the pulse action period according to the module operating electrical energy; Step S4: If it is determined that it is in an idle state, an internal discharge circuit is started to apply a pulse discharge load, and the voltage drop data and discharge current characteristic values under the pulse discharge are obtained according to the module operating electrical energy; Step S5: Select the corresponding voltage and current data sets according to the charging or idle state; confirm the pulse time window of the high-frequency pulse charging current and the pulse discharge load; use the pulse time window to extract the phase difference between the voltage change rate and the current change rate in the voltage and current data sets to calculate the real-time internal resistance value of the battery; Step S6: Dynamically compare the real-time internal resistance value of the battery with a preset resistance threshold, generate a battery health status assessment report and trigger an abnormal alarm signal.

2. The method for measuring the internal resistance of a battery based on Poe pulse charge and discharge according to claim 1, wherein Step S1 includes the following steps: Step S11: The controller communicates with the battery management system in real time to collect the total current status signal of the battery pack; Step S12: The controller obtains the charging status signal of the battery pack; Step S13: Perform timing analysis on the total current status signal to generate battery pack current timing data; perform charging mode analysis on the charging status signal according to the battery pack current timing data to generate battery charging mode analysis data; Step S14: Classify and identify the charging status signal based on the battery charging mode analysis data to obtain a charging mode identifier, where the mode classification includes a constant current charging mode, a constant voltage charging mode, or a trickle charging mode.

3. The battery internal resistance measurement method based on pulse charge and discharge of Poe according to claim 1, characterized in that, Step S2 includes the following steps: Step S21: Based on the charging mode identifier, it is judged whether the battery pack is in a charging or idle state. If the battery pack is in constant current charging or constant voltage charging, it is confirmed that the battery pack is in a charging state; Step S22: If the battery pack is in trickle charging, it is confirmed that the battery pack is in an idle state, and the charging state and the idle state are integrated into the battery pack operating state data; Step S23: Use the battery pack operating state data to judge whether the battery pack is in a charging state, and generate a charging state judgment result; if the charging state judgment result is that the battery pack is in a charging state, activate the bus-powered mode and generate a bus-powered activation signal; Step S24: Connect to the control bus through the Poe network interface according to the bus-powered activation signal and monitor the electrical energy state of the battery pack to obtain the module operating electrical energy.

4. The battery internal resistance measurement method based on Poe pulse charge and discharge according to claim 3, characterized in that Step S24 includes the following steps: Step S241: Initialize and configure the Poe interface according to the bus-powered activation signal to generate Poe network interface connection data; Step S242: Connect to the control bus through the Poe network interface to collect the battery pack power data, and obtain the battery pack power status time series data; Step S243: Conduct power demand analysis on the battery pack power status time series data to generate module power demand data; Step S244: Extract the power change trend of the battery pack power status time series data, and use the module power demand data to perform battery pack power distribution balance processing on the battery pack power status time series data to generate module working power.

5. The method for measuring the internal resistance of a battery based on pulse charging and discharging using Poe according to claim 1, wherein Step S3 includes the following steps: Step S31: When it is determined to be in the charging state, activate the bus power supply mode and send a trigger instruction to the pulse injection unit to generate a pulse injection trigger signal; Step S32: According to the pulse injection trigger signal, configure the frequency and amplitude parameters of the high-frequency pulse charging current to generate a high-frequency pulse parameter set; Step S33: Based on the high-frequency pulse parameter set, superimpose a pulse current on the battery pack bus to generate superimposed pulse current data, and record the pulse trigger timestamp; Step S34: Synchronously collect the original voltage signal and current signal during the pulse action period in the charging circuit to generate original voltage transient data and original current waveform data; Step S35: Screen the effective time window data of the original voltage transient data and the original current waveform data through the pulse trigger timestamp to obtain the voltage transient data and current waveform data during the pulse action period.

6. The method for measuring the internal resistance of a battery based on pulse charge and discharge of Poe according to claim 1, characterized in that Step S4 includes the following steps: Step S41: When it is determined to be in the idle state, activate the internal discharge circuit and send an activation instruction to the load control unit to generate a discharge circuit trigger signal; Step S42: According to the discharge circuit trigger signal, configure the amplitude and duty cycle parameters of the pulse discharge load to generate a pulse discharge parameter set, where the configured amplitude range is 5%-30% of the rated capacity, the pulse width is 200 μs - 1 ms, and the interval period is 10 ms - 50 ms; Step S43: Based on the pulse discharge parameter set, control the MOSFET switch array to apply the pulse discharge load to generate pulse discharge current data, and record the discharge trigger timestamp; Step S44: Synchronously collect the transient response signal of the battery terminal voltage and the load current signal in the discharge circuit to generate original voltage drop data and original discharge current data; Step S45: Perform baseline calibration on the original voltage drop data and apply band-pass filtering to the original discharge current data to generate calibrated voltage drop data and filtered discharge current data; Step S46: Intercept the effective response segments of the calibrated voltage drop data and the filtered discharge current data according to the discharge trigger timestamp, so as to obtain the voltage transient data and current waveform data during the pulse action period.

7. The method for measuring the internal resistance of a battery based on pulse charging and discharging of Poe according to claim 1, wherein The pulse time window for confirming the high-frequency pulse charging current and the pulse discharge load in Step S5 includes: Perform time alignment processing on the superimposed pulse current data and the pulse discharge current data, calculate the phase matching degree between the two, and generate pulse current time alignment data; Extract the transient characteristics of the pulse current time alignment data, extract the pulse rising edge, falling edge and peak points, and generate pulse transient characteristic data; Conduct an adaptive analysis on the pulse transient characteristic data, calculate the current change rate and stability under different time windows, and generate pulse time window adaptive data; Conduct a pulse window load optimization analysis on the pulse time window adaptive data to obtain the pulse time window.

8. The method for measuring the internal resistance of a battery based on pulse charge and discharge of Poe according to claim 8, characterized in that, The content described in step S5 includes using the pulse time window to extract the phase difference between the voltage change rate and the current change rate in the voltage-current dataset to calculate the real-time internal resistance of the battery, including: Use the pulse time window to extract the voltage data and current data in the voltage-current dataset during the pulse action period to generate a voltage-current dataset during the pulse action period; Perform numerical differentiation on the voltage-current dataset during the pulse action period, calculate the voltage change rate and the current change rate respectively, and generate voltage change rate data and current change rate data; Conduct a phase difference analysis on the voltage change rate data and the current change rate data, extract the phase offset amount between the two, and generate voltage-current phase difference data; Conduct impedance modeling calculation on the voltage-current phase difference data to generate battery internal resistance data; Conduct trend analysis and calibration on the battery internal resistance data to generate the real-time internal resistance value of the battery.

9. The method for measuring the internal resistance of a battery based on pulse charging and discharging using Poe according to claim 1, characterized in that Step S6 includes the following steps: Step S61: Dynamically compare the real-time battery internal resistance data with a preset resistance threshold to identify the situation of resistance exceeding the limit, and generate preliminary battery health status data; Step S62: Obtain the battery historical health status data; conduct time series trend analysis based on the preliminary battery health status data and the battery historical health status data to generate health status change trend data; Step S63: Conduct anomaly detection on the health status change trend data to generate battery anomaly detection data; Step S64: Evaluate the alarm level of the battery anomaly detection data to generate an anomaly alarm signal; Step S65: Integrate the anomaly alarm signal and the health status change trend data to generate a battery health status assessment report.

10. The method for measuring the internal resistance of a battery based on pulse charging and discharging of Poe according to claim 9, wherein Step S63 includes the following steps: Step S631: Extract the abnormal change trend characteristics of the health status change trend data, and divide the health status change trend data into datasets according to the abnormal change trend characteristics to generate a model training set and a model test set; Step S632: Train the model training set through the convolutional neural network algorithm to generate a preliminary battery anomaly detection model; use the model test set to optimize and iterate the preliminary battery anomaly detection model to generate a battery anomaly detection model; Step S633: Import the health status change trend data into the battery anomaly detection model for anomaly detection to generate battery anomaly detection data.

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