Control system and control method of battery measuring instrument

Abstract

The invention relates to the technical field of electrical variable measurement, in particular to a control system and a control method of a battery measuring instrument. The method comprises the following steps: acquiring single voltage, total voltage, current and internal resistance data of a battery pack to obtain initial electrical parameters; collecting the surface temperature of the battery pack to obtain an initial thermodynamic parameter; performing synchronous time sequence integration on the initial electrical parameters and the initial thermodynamic parameters, and unifying formats to obtain a battery running state monitoring data set; and performing dynamic threshold determination and trend analysis on each parameter based on the battery operation monitoring data set to obtain an abnormal state identification result. By constructing a complete intelligent monitoring system covering data acquisition, processing, abnormity identification, state evaluation, communication packaging and visualization, high-precision real-time monitoring, fault pre-judgment and remote communication of the running state of the battery are realized, and the synchronism, the accuracy and the adaptability of the system and the intelligence and the practicability of field application are remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of measuring electrical variables, in particular to a control system and control method of a battery measuring instrument. BACKGROUND

[0002] Traditional battery maintenance relies on manual inspection and periodic testing, which is not only inefficient, but also difficult to find hidden faults such as battery degradation and capacity decay in time, which can easily cause system interruption and even safety accidents. To meet the needs of high-power and high-density deployment of 5G base stations, the capacity of battery packs continues to increase, and the operation and maintenance complexity significantly increases, making traditional monitoring methods face serious challenges. Although some battery measuring devices with online monitoring functions have appeared in the market, there are still the following main technical defects:

[0003] First, the synchronization and consistency of data processing are insufficient. The traditional system lacks a time sequence calibration mechanism for multi-channel acquisition data, resulting in inaccurate analysis results and affecting the accuracy of early warning.

[0004] Second, there is a lack of intelligent diagnosis and trend modeling capabilities. Most devices only use static threshold alarms and cannot identify degradation trends, predict fault development, or quantify health status.

[0005] Third, the communication and visualization capabilities are limited. Some measuring instruments only provide basic serial output and lack the ability to support remote communication, multi-address networking, and real-time display on local liquid crystal interfaces, which is not conducive to industrial site deployment and multi-point centralized monitoring. SUMMARY

[0006] Therefore, it is necessary to provide a control system and control method of a battery measuring instrument to solve at least one of the above technical problems.

[0007] To achieve the above purpose, a control method of a battery measuring instrument includes the following steps:

[0008] Step S1: Collecting single cell voltage, total voltage, current and internal resistance data of the battery pack to obtain initial electrical parameters; collecting surface temperature of the battery pack to obtain initial thermodynamic parameters;

[0009] Step S2: Synchronizing and integrating the initial electrical parameters and the initial thermodynamic parameters, and unifying the format to obtain a battery operating state monitoring data set;

[0010] Step S3: Based on the battery operating monitoring data set, performing dynamic threshold determination and trend analysis on each parameter to obtain an abnormal state recognition result;

[0011] Step S4: Obtaining battery historical operation data; based on the abnormal state recognition result and the battery historical operation data, performing state modeling analysis to obtain battery health status evaluation data;

[0012] Step S5: Construct an information report based on the battery health state evaluation data, and perform encapsulation processing through a communication interface to obtain control communication data;

[0013] Step S6: Perform communication protocol analysis on the control communication data, send it to the upper computer, and perform real-time visualization to obtain operation display data.

[0014] The present application constructs a complete control process covering data collection, preprocessing, anomaly identification, state evaluation, communication encapsulation and visualization display, realizes intelligent monitoring and dynamic response of the battery running state in the whole cycle, all parameters and all channels, and effectively overcomes the defects of slow response and weak identification of traditional manual inspection and static detection means. The method adopts a multi-parameter parallel acquisition mechanism combined with a unified timestamp integration strategy, significantly improves the synchronization and consistency of data processing, ensures that various electrical and thermal parameters have high timeliness and high precision real-time correspondence; by introducing a dynamic threshold judgment and trend evolution analysis mechanism, the abnormal capture capability of the system for voltage anomaly, temperature rise risk, internal resistance degradation and current fluctuation is enhanced, and a health state evaluation model can be constructed based on historical operation rules to output quantifiable SOH state and risk level, realize fault prediction and trend tracking; by structuring and packaging the diagnostic data and mapping it into a standard communication frame format, the remote communication adaptability and protocol universality of the system are improved, supporting multi-device networking deployment and real-time data uploading; at the same time, combined with the remote graphical rendering of the upper computer and the local liquid crystal synchronous display mechanism, a stable, intuitive and visual operation interface is provided to meet the centralized supervision and distributed decision-making needs in complex application environments, and the intelligent level, communication compatibility and industrial field practicability of the battery monitoring system are improved as a whole.

[0015] Preferably, the present application also provides a battery measuring instrument control system for executing the above-mentioned battery measuring instrument control method, which comprises:

[0016] A data acquisition module is configured to acquire single cell voltage, total voltage, current and internal resistance data of the battery pack to obtain initial electrical parameters, and acquire surface temperature of the battery pack to obtain initial thermodynamic parameters;

[0017] A data processing module is configured to perform synchronous time sequence integration and unified formatting on the initial electrical parameters and the initial thermodynamic parameters to obtain a battery operation state monitoring data set;

[0018] An anomaly detection module is configured to perform dynamic threshold judgment and trend analysis on each parameter based on the battery operation monitoring data set to obtain an abnormal state identification result;

[0019] The health assessment module is used to obtain historical battery operation data; based on the abnormal state identification results and the battery's historical operation data, the state modeling analysis is performed to obtain battery health status assessment data;

[0020] The report generation and transmission module is used to construct information reports based on battery health status assessment data and encapsulate them through the communication interface to obtain control communication data;

[0021] The communication analysis and display module is used to perform communication protocol analysis on the control communication data, send it to the host computer, and perform real-time visualization to obtain operation display data.

[0022] The present invention effectively solves the shortcomings of traditional technologies in real-time monitoring, intelligence level and information interaction capabilities by constructing a battery measuring instrument control system covering the entire process of collection, processing, diagnosis, evaluation, communication and display, and has many significant beneficial effects: First, through the unified clock marking and timing alignment of all-channel data, high-precision synchronous processing of electrical parameters and thermal parameters is achieved, which avoids judgment errors caused by sampling offset and improves the accuracy and reliability of abnormality identification; secondly, through the diagnostic mechanism combining dynamic threshold, trend change analysis and historical offset evaluation, it can realize multi-dimensional parameter linkage judgment and health status quantitative output, and support early detection of battery degradation process. Perception and risk grading help extend the battery life cycle and reduce the sudden failure rate; secondly, the use of structured information packaging, address coding transmission and standard communication encapsulation design comprehensively improves the transmission compatibility and anti-interference capability of data frames, supports remote networking deployment and distributed monitoring architecture, and adapts to the operation and maintenance collaboration needs in high-density equipment scenarios; finally, with the help of the parallel driving mechanism of the host computer system and the LCD display terminal, remote centralized monitoring and on-site instant feedback can be synchronized, which makes it easier for users to quickly grasp the battery status and realize automated, visual and refined operation and maintenance management, significantly enhancing the system's practicality, safety and engineering deployment efficiency in key applications such as 5G communications and power storage. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0024] Figure 1 A schematic flow chart of the steps of a control method for a battery measuring instrument according to the present invention;

[0025] Figure 2 for Figure 1 Detailed step flow diagram of step S1;

[0026] Figure 3 for Figure 1 Detailed step flow chart of step S2 in FIG. DETAILED DESCRIPTION

[0027] The technical method of the present application will be described clearly and completely below in conjunction with the drawings. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0028] In addition, the drawings are only schematic illustrations of the present application and are not necessarily drawn to scale. Identical reference numerals in the drawings represent identical or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities, which do not necessarily have to correspond to physically or logically independent entities. The functional entities can be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0029] It should be understood that although the terms "first", "second" and the like can be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, without departing from the scope of the example embodiments, a first element can be referred to as a second element, and similarly a second element can be referred to as a first element. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0030] To achieve the above-mentioned purpose, please refer to Figures 1 to 3 The present application provides a control method of a battery measuring instrument, the method comprising the following steps:

[0031] Step S1: collecting single cell voltage, total voltage, current and internal resistance data of the battery pack to obtain initial electrical parameters; collecting surface temperature of the battery pack to obtain initial thermodynamic parameters;

[0032] Step S2: synchronously integrating the initial electrical parameters and the initial thermodynamic parameters, and unifying the format to obtain a battery operation state monitoring data set;

[0033] Step S3: performing dynamic threshold determination and trend analysis on each parameter based on the battery operation monitoring data set to obtain an abnormal state recognition result;

[0034] Step S4: obtaining battery historical operation data; performing state modeling analysis based on the abnormal state recognition result and the battery historical operation data to obtain battery health state evaluation data;

[0035] Step S5: constructing information report based on battery health state evaluation data, and performing encapsulation processing through communication interface to obtain control communication data;

[0036] Step S6: performing communication protocol analysis on the control communication data, sending to the upper computer, and performing real-time visualization to obtain operation display data.

[0037] In the embodiment of the present application, referring to Figure 1 As shown in the figure, it is a step flow schematic diagram of the control method of the battery measuring instrument, and in the present example, the control method of the battery measuring instrument comprises the following steps:

[0038] Step S1: collecting single cell voltage, total voltage, current and internal resistance data of the battery pack to obtain initial electrical parameters; collecting surface temperature of the battery pack to obtain initial thermodynamic parameters;

[0039] In the embodiment of the present application, first, a differential input circuit structure with high common-mode rejection ratio is used to introduce 26 single battery voltage signals in parallel, each voltage input channel is connected with a current-limiting protection resistor and a voltage stabilizing filter capacitor to build a protection circuit, which prevents measurement error or equipment damage caused by external voltage fluctuation or short circuit, and then the signals are input to a 24-bit high-speed analog-to-digital converter for digital processing after polling through an electronic switch matrix, the sampling accuracy is controlled within ±5mV, the sampling interval is fixed at 100ms, and the acquisition result is used as single voltage acquisition data; the total voltage signal is connected through two independent VOL+ and VOL- input terminals, and after voltage stabilization processing through a DC / DC isolation module, it is input to a special voltage sampling channel, the measurement range is limited to 20-60V, and the conversion resolution is set to 1mV; the charge and discharge current acquisition part uses a Hall current sensor to convert the current signal into an analog voltage signal, which is sent to an analog-to-digital converter after filtering noise components above 50Hz through a high-impedance input buffer and a low-pass filter, the current measurement range is limited to -100A to +100A, the accuracy is controlled within ±0.5A, and the sampling period is consistent with the voltage sampling to keep synchronization; the internal resistance measurement process is realized by pulse loading, and in the internal resistance sampling period, the PPTC resistance array is connected to the discharge circuit by the control module, the loading current is controlled between 30A and 40A, the duration is controlled for 300ms, the instantaneous voltage change ΔU before and after loading and the known constant current I are recorded, the battery internal resistance value is calculated according to the formula R=ΔU / I, all calculation processes are completed in the control system and output with 2 decimal places; the temperature acquisition process is realized by fixing three thermocouple sensors at key nodes of the battery shell, the sensor acquisition voltage signal is linearly compensated and cold end temperature corrected through three independent temperature conditioning channels, and the output is converted into a standard temperature value, the range is -20℃ to +80℃, the accuracy is controlled within ±0.5℃, the measurement result and the electrical parameter are packaged together with a unified timestamp and temporarily stored in the data buffer area of the measuring instrument, and the initial electrical parameter and the initial thermodynamic parameter are obtained.

[0040] Step S2: synchronously integrating the initial electrical parameter and the initial thermodynamic parameter, and unifying the format to obtain a battery operation state monitoring data set;

[0041] In the embodiment of the present application, first, a unified clock management unit is enabled in the control system, a high-precision real-time clock chip (RTC) is called to provide millisecond-level time reference for all acquisition channels, time stamp information of five types of data, i.e., single cell voltage, total voltage, current, internal resistance and surface temperature, is extracted according to a sampling control signal trigger flag, the time stamp is accurately formatted to three decimal places of seconds for unified processing, and a synchronous mapping table of the five types of data is established through a hash index structure, an interpolation method is used to time-align parameter data with slight sampling delay, the error is controlled within ±5 ms, and synchronous timing data with consistent structure is formed; then a data standardization submodule is started, each type of data is uniformly converted according to the field rules and unit conventions set in the control system, the single cell voltage is defined as BATn (mV), the total voltage is defined as TV (mV), the current is defined as I (mA), the internal resistance is defined as R (mΩ), and the temperature is defined as T (℃), the accuracy of all parameters is uniformly kept to two decimal places, and the numerical range is set as follows: the single cell voltage is 1500-2500 mV, the total voltage is 24000-54000 mV, the current is -100000-+100000 mA, the internal resistance is 0-500 mΩ, and the temperature is -20-+80℃, all fields are encoded as 16-bit signed integers after conversion, and are sequentially arranged and stored through a field recombination program, forming a data frame structure arranged according to the sampling time; in the integration process, for the missing value field in the acquisition frame, the missing value field is corrected through the mean value filling and forward continuation method, if the same field is lost for three consecutive frames, it is marked as invalid, and the filling value is set as a specific invalid identifier (-9999); finally, all standardized data frames are pressed into the running monitoring buffer area in the measurement instrument cache area, and the sampling cycle number and measurement time information are attached, and a complete battery running state monitoring data set is generated.

[0042] Step S3: based on the battery running monitoring data set, dynamic threshold determination and trend analysis are performed on each parameter to obtain an abnormal state recognition result;

[0043] In the embodiment of the application, in the step S3, based on the battery operation state monitoring data set obtained in the step S2, when performing the dynamic threshold value judgment and trend analysis operation on each operation parameter, first, the threshold value management module built in the control system reads the five types of standardized data frames of single cell voltage, total voltage, current, internal resistance and temperature in sequence, respectively calls the corresponding threshold value comparison logic circuit, and performs point-by-point judgment on each parameter value, wherein the upper and lower limits of the single cell voltage are respectively set to 1800mV and 2500mV, the total voltage is set to the range of 42000mV to 54000mV, the current is limited to -80000mA to +80000mA, the upper limit of the internal resistance is fixed to 300mΩ, the temperature range is -10℃ to 60℃, if any data exceeds the corresponding threshold value range, it is immediately marked as abnormal and an additional field “E” is identified; at the same time of performing the threshold value judgment, the trend calculation module performs sliding window processing on the continuous 10 frames of historical data, respectively calculates the single step change value Δx and the change rate Δx / Δt of each channel parameter, wherein Δt is the fixed sampling interval of 100ms, the trend abnormality judgment is made on the parameter values whose voltage change rate exceeds 50mV / s, current mutation exceeds 2000mA / s, internal resistance growth exceeds 10mΩ / min or temperature rising rate exceeds 5℃ / min, and the channel number and abnormal time of occurrence are recorded; the above two types of results are respectively stored in the abnormal flag table and the trend fluctuation table, then the abnormal integration module performs channel mapping and time correlation analysis on the two types of data, if a channel simultaneously satisfies the threshold value out-of-range and trend fluctuation conditions in the continuous three sampling periods, the state recognition controller records the channel as a strong abnormal state, and gives an abnormal level label, wherein the level is divided into three types of 1, 2 and 3, which respectively correspond to the severity of single abnormality, combined abnormality and continuous abnormality; finally, the system integrates and outputs the channel abnormality identification, abnormal occurrence time, parameter name, abnormal value and level label, and forms the abnormal state recognition result.

[0044] Step S4: obtaining battery historical operation data; based on the abnormal state recognition result and the battery historical operation data, performing state modeling analysis to obtain battery health state evaluation data;

[0045] In the embodiment of the present application, in step S4, based on the abnormal state recognition result obtained in step S3 and combined with the long-term stored battery historical operation data, the state modeling analysis is first performed by the data retrieval module to extract the raw monitoring data of single cell voltage, total voltage, charge and discharge current, battery internal resistance and temperature within the last 30 days from the local storage according to the channel number, with a 5-minute extraction period and a total data amount controlled within 8640 frames; then the extracted data is subjected to time sequence alignment and null value elimination operation, the missing fields are filled in by forward filling method, the data segments with more than 3 frames of discontinuous loss are marked as unusable and eliminated from the analysis; then the data feature extraction module calculates the average value, maximum value, minimum value, standard deviation, rising / falling rate, periodic fluctuation range and other six statistical indicators of each historical data, wherein the standard deviation is calculated by the formula σ = √(Σ(xi-μ) 2 / n), the rate index is calculated based on Δx / Δt, Δt is fixed at 600s interval, and finally the complete historical operation feature vector set is obtained; then the abnormal state recognition result generated in step S3 is compared with the above historical feature vector at channel level, the relative deviation degree of the current parameter value in its historical distribution is judged, and the relative deviation rate Δr = (xt-μ) / σ is calculated to quantify the difference between the current value and the historical steady state, if the Δr value exceeds ±2, it is considered as significant deviation; then the deviation rate value, abnormal level label and historical change trend are integrated by the health assessment control unit, the weighted summation method is used to score all feature dimensions, the score range is set to 0-100 points, and the battery health state is divided into "healthy" (≥80), "sub-healthy" (60-79) and "deterioration" (<60) three state levels according to the score interval, and the SOH estimated value is output as an auxiliary reference index, the SOH estimated value is expressed in the form of percentage of health state score value, with one decimal place; finally, the structured battery health state evaluation data containing channel number, current key parameter, health level label, SOH estimated value and reference trend is generated.

[0046] Step S5: Constructing information report based on battery health state evaluation data, and performing packaging processing through communication interface to obtain control communication data;

[0047] In the embodiment of the application, when the information report is constructed based on the battery health state evaluation data obtained in step S4 and is encapsulated by the communication interface in step S5, first, the reporting construction unit calls the channel number, health level label, SOH estimation value and abnormal parameter field in the health state evaluation data, and the contents are combined according to the fixed field order, wherein the field order is defined as: [address number] [channel number] [voltage value] [current value] [temperature value] [internal resistance value] [health score] [SOH value] [health level] [abnormal flag], all fields are encoded by using hexadecimal unsigned integer, and the field bit width is set as: the address number is 8 bits, the channel number is 8 bits, the voltage, current, temperature and internal resistance value are all 16 bits, the health score and SOH value are all 8 bits, the health level is 4 bits, and the abnormal flag is 4 bits, and the total length of the fields is 88 bits; after the combination, a structured report frame is formed, and the report frame is added with a frame header “AA55” and a frame tail “55AA” as an identifier, and then is handed over to the communication encapsulation module for interface encapsulation processing, the communication interface adopts RS485 or RS422 standard differential serial communication protocol, according to the electrical interface definition in the appendix, the data transmission line is connected to the TX+, TX- (RS485 mode) or TX+ / TX- / RX+ / RX- (RS422 mode) port, the baud rate is set to 9600 bps, the data format is 8-bit data bit, 1-bit stop bit, no parity check, all frames are calculated by CRC16 check logic to obtain a check code and are attached to the tail of the data segment, to ensure the integrity of the transmission data; before the frame is sent, the communication address manager writes the device address field, supports the address number range of 0 to 255, and sets a unique communication ID in the multi-host control system according to the network structure, and the encapsulated control communication data is dispatched by the sending controller in the next communication cycle and is output to the communication bus in turn, and is temporarily stored in the communication cache area.

[0048] Step S6: The control communication data is parsed according to the communication protocol, is sent to the upper computer, is visualized in real time, and running display data is obtained.

[0049] In the embodiment of the application, in the operation of performing communication protocol analysis on the control communication data generated in step S5 and sending to the upper computer and performing real-time visualization in step S6, first, the communication receiving controller receives the encapsulated control communication data frame through the RS485 or RS422 physical interface, the data frame starts with a frame header "AA55" and ends with a frame tail "55AA", the data format is 8-bit data bit, 1-bit stop bit, no check, the baud rate is fixed at 9600bps, after receiving, the communication protocol analyzer starts the frame synchronization mechanism, and the frame boundary is located by comparing the head and tail flags, and then the address number, channel number, voltage value, current value, temperature value, internal resistance value, health score, SOH value, health level, abnormal flag and CRC16 check code field are extracted in turn according to the fixed offset, wherein the voltage, current, temperature and internal resistance fields are parsed as 16-bit unsigned numbers, the health score and SOH value are expressed in the form of 8-bit unsigned numbers in percentage, the health level field adopts 4-bit binary value, and corresponds to four states of health, sub-health, degradation and serious respectively; after the analysis is completed, the consistency of the check field and the received data segment is checked through CRC16 check, if the check is passed, the complete field information is re-encapsulated in the JSON format and uploaded to the upper computer processing system; the data display management module embedded in the upper computer maps the received field content, automatically calls the corresponding display template according to the field type, wherein the voltage, current, temperature and internal resistance are dynamically refreshed and displayed in the form of a curve graph, the refresh period is 1000ms, the health score and SOH value are displayed in the form of a digital label, and the health level and abnormal flag are represented by red, yellow and green three-color icons; at the same time, the synchronous display output is completed in the local liquid crystal display terminal, the data mapped after the analysis is mapped to the partition area through the embedded UI control module, the channel number, voltage, current, internal resistance, temperature, SOH and health state are displayed in real time, the layout of all display items is fixed, the refresh period is consistent with that of the upper computer, and finally the complete operation display data is formed.

[0050] The application realizes intelligent monitoring and dynamic response of the whole cycle, full parameter and full channel of the battery operating state by constructing a complete control process covering data collection, preprocessing, abnormality identification, state evaluation, communication packaging and visual display, effectively overcoming the defects of slow response and weak identification of traditional manual inspection and static detection means. The method adopts a multi-parameter parallel acquisition mechanism combined with a unified timestamp integration strategy, significantly improving the synchronization and consistency of data processing, ensuring that various electrical and thermal parameters have high timeliness and high precision real-time correspondence; by introducing a dynamic threshold judgment and trend evolution analysis mechanism, the abnormal capture capability of the system for voltage abnormality, temperature rise risk, internal resistance degradation and current fluctuation is enhanced, and a health state evaluation model can be constructed based on historical operation rules to output quantifiable SOH state and risk level, realizing fault prediction and trend tracking; by structuring the diagnostic data and mapping it into a standard communication frame format, the remote communication adaptability and protocol universality of the system are improved, supporting multi-device networking deployment and real-time data uploading; at the same time, combined with the remote graphical rendering of the upper computer and the local liquid crystal synchronous display mechanism, a stable, intuitive and visual operation interface is provided to meet the centralized supervision and distributed decision-making needs in complex application environments, and the intelligent level, communication compatibility and industrial field practicability of the battery monitoring system are improved as a whole.

[0051] Preferably, step S1 comprises the following steps:

[0052] Step S11: differentially collect voltage signals of each single battery in the battery pack, and filter and amplify the voltage signals through a common-mode rejection circuit to obtain single voltage collection data;

[0053] Step S12: collect the total voltage signal across the battery pack, and stabilize the total voltage signal through a DC / DC voltage conversion component to obtain total voltage collection data;

[0054] Step S13: collect the current signal in the battery pack loop, and perform signal conditioning processing on the current signal based on a current sampling conversion component to obtain charge and discharge current collection data;

[0055] Step S14: set up an impedance test loop of the battery pack; adjust the controllable load output of the impedance test loop, and measure the dynamic response through an internal resistance detection component to obtain battery internal resistance collection data;

[0056] Step S15: record the single voltage collection data, total voltage collection data, charge and discharge current collection data and battery internal resistance collection data as initial electrical parameters;

[0057] Step S16: set multiple thermal sensors on the battery pack shell; collect multiple thermal sensor signals, and select and synchronously control the thermal signal channel to obtain initial thermodynamic parameters.

[0058] In the embodiment of the present application, firstly, in step S11, the positive and negative voltage signals of each single battery in the battery pack are accessed through the set 26-way differential sampling channel, the voltage limiting protection resistor and the TVS transient suppressor in front of the sampling interface constitute a protection network, after the signal is introduced, the common mode rejection circuit is used for primary filtering and double operational amplifier differential amplification processing, the filter cutoff frequency is set to 100 Hz, the common mode rejection ratio is greater than 90 dB, the signal anti-interference ability is enhanced and the precision is improved, the signal after differential amplification is sent to the 24-bit delta-sigma analog-to-digital converter for digital output, the sampling period is 100 ms, the sampling resolution is 0.1 mV, and the collection result constitutes the single voltage collection data; in step S12, the total voltage signal of the battery pack is collected, the signal is accessed from the dedicated VOL+ and VOL- input end, and the voltage balance and stable filtering processing are performed through the DC / DC isolation conversion component, the DC / DC module output precision is set to ±0.2%, the isolation voltage resistance is higher than 1000V, after the conditioning is completed, the signal is input to the special A / D conversion channel, and the output data is recorded as the total voltage collection data; in step S13, the battery pack output loop is connected in series with the Hall principle current sensor, the current signal is converted into a standard 0-5V analog signal, and then input to the current sampling conversion component for buffer amplification and low-pass filtering processing, the filter bandwidth is set to 500 Hz to prevent sampling error, and the charging and discharging current collection data is obtained after the signal is converted into digital signal; in step S14, the PPTC resistor array is connected in parallel at both ends of the battery pack bus to form an impedance test loop, the load is connected and released by controlling the on-off of the MOS tube controlled by the controller, the load conduction duration is 300 ms, the loading current is constant at 30A to 40A, the two voltage instantaneous values U1 and U2 before and after loading are collected respectively during the period, the battery internal resistance value is output according to the calculation formula R=(U1-U2) / I, the average value is taken as the battery internal resistance collection data after repeated measurement for three times; in step S15, the single voltage collection data, the total voltage collection data, the charging and discharging current collection data and the battery internal resistance collection data obtained in the above steps are bound with a unified timestamp, and are combined to form initial electrical parameters; in step S16, three thermistors are fixed and arranged in sequence outside the battery pack shell, the thermal response time of the thermistor element is less than 2s, the temperature measurement range is-20℃ to 80℃, the sensor output signal is introduced into the thermal signal channel switching circuit and is synchronously sampled through the multiplexer, the time consistency of each temperature data and electrical parameter is ensured by combining the sampling clock control logic, the thermoelectric signal is converted into a temperature value after linear amplification and temperature linear compensation processing, the precision is controlled within ±0.5℃, and the final collection result constitutes the initial thermodynamic parameters.

[0059] The application realizes comprehensive monitoring and dynamic evaluation of the running state of the battery pack through multi-dimensional and accurate collection of electrical and thermodynamic parameters. The differential collection and common mode rejection technology effectively improves the anti-interference ability and measurement accuracy of the single battery voltage signal, ensuring the accuracy of the voltage data; stable processing and conditioning of the total voltage and current signals ensure the reliability of the overall electrical parameters of the system, which helps to accurately grasp the charging and discharging state of the battery pack; the controllable load adjustment and dynamic response measurement of the internal resistance test loop realize real-time monitoring of the battery internal resistance, which provides a key basis for judging the health status of the battery; the layout and synchronous collection of the multi-point thermal sensor effectively reflect the thermodynamic characteristics of the battery pack, supporting fine control of temperature distribution and thermal management. Overall, this method enhances the comprehensiveness and accuracy of battery state perception, provides a solid data foundation for subsequent safety warning, performance optimization and life prediction, and significantly improves the intelligent level and application value of the battery measuring instrument.

[0060] Preferably, step S2 comprises the following steps:

[0061] Step S21: Perform sampling timestamp alignment processing on the initial electrical parameters and initial thermodynamic parameters to obtain a synchronous time series data set;

[0062] Step S22: Perform format standardization conversion on the synchronous time series data set to unify the units, accuracy and structure format of each parameter, and obtain a standardized parameter data set;

[0063] Step S23: Perform integrity verification and outlier screening on the standardized parameter data set to eliminate packet loss data, erroneous measurement values and communication noise interference, and obtain effective monitoring parameter data;

[0064] Step S24: Perform parameter aggregation and logical organization on the effective monitoring parameter data according to a preset field structure, construct a multi-dimensional monitoring matrix, and obtain a battery running state monitoring data set.

[0065] In the embodiment of the present application, step S21 first provides a unified millisecond time reference for all channels by calling the real-time clock RTC through the system clock management module, reads the original sampling time stamp of the initial electrical parameters and initial thermodynamic parameters, maps the time information corresponding to the data of each channel according to the sampling trigger sequence, classifies the data with a sampling time difference within ±5 ms into the same time slice, adds a standardized time label field in the data structure, and outputs the integration result as a synchronous time sequence data set; in step S22, the format standardization processing is completed by the data format conversion component, and each parameter is uniformly converted into a system general unit and a numerical structure according to the set rules, wherein the unit of single cell voltage is set to mV, the unit of total voltage is set to mV, the unit of current is mA, the unit of internal resistance is mΩ, and the unit of temperature is ℃, all numerical values are converted into 16-bit signed integers, voltage, current and temperature are kept to two decimal places, internal resistance is kept to one decimal place, the field order is arranged in a fixed structure, each frame of data contains a channel number, a sampling time, five types of parameter values, and a data validity identifier, and a total of 72 bytes constitute a standardized parameter data set; in step S23, the data integrity check module compares the continuous sampling frame sequence according to the frame number, and if the number is interrupted, it is marked as a packet loss data and is removed, for the frame record with abnormal data field, the threshold range is set to identify the error value, wherein the single cell voltage exceeding 1000-3000 mV, the current exceeding ±120000 mA, the temperature lower than-30 ℃ or higher than 90 ℃, and the internal resistance exceeding 500 mΩ are all regarded as error values, at the same time, the mean value smoothing method is used to identify the abnormal fluctuations exceeding 30%, and the abnormal value record is removed and output as valid monitoring parameter data; in step S24, the field aggregation controller integrates the voltage, current, temperature, internal resistance and other valid monitoring parameters according to the preset format, arranges the data of each channel in the form of column vector, each column corresponds to a parameter dimension, each row is a frame of sampling data, adds a state bit identifier and a time index, constructs a complete multi-dimensional monitoring matrix, and finally encapsulates a battery operating state monitoring data set.

[0066] The present application processes the collected electrical and thermodynamic parameters in time synchronization and format unification, significantly improves the consistency and comparability of the data, ensures that various parameters can be accurately corresponded on the same time axis, and is convenient for subsequent analysis and judgment; the standardized conversion strengthens the compatibility and standardization of the data, provides technical support for cross-module and cross-system data fusion; the integrity check and abnormal screening effectively removes invalid or error data, greatly improves the accuracy and reliability of the monitoring data; the parameter aggregation and multi-dimensional matrix construction realize the systematic and structured expression of the battery operating state, lay a solid data foundation for intelligent analysis, fault diagnosis and performance evaluation, and thus improve the data processing efficiency and decision support ability of the overall battery management system.

[0067] Preferably, step S24 comprises the following steps:

[0068] Step S241: The monomer voltage acquisition data in the effective monitoring parameter data is summarized in the order of channel number to obtain a monomer voltage matrix;

[0069] Step S242: The charge-discharge current acquisition data in the effective monitoring parameter data is classified by direction label and subjected to periodic average operation to obtain a current feature vector;

[0070] Step S243: The battery surface temperature acquisition data in the effective monitoring parameter data is subjected to position mapping and statistical analysis according to the sensor number to obtain a temperature distribution feature vector;

[0071] Step S244: The internal resistance acquisition data in the effective monitoring parameter data is subjected to incremental change analysis within a time window to extract the maximum amplitude value and growth trend, and an internal resistance change feature vector is obtained;

[0072] Step S245: The monomer voltage matrix, the current feature vector, the temperature distribution feature vector and the internal resistance change feature vector are spliced and combined according to a preset field structure and a logical order to obtain multi-dimensional monitoring matrix data;

[0073] Step S246: The multi-dimensional monitoring matrix data is added with unified time label, device number and acquisition cycle identification information to obtain a battery operation state monitoring data set.

[0074] In the embodiment of the present application, first, in step S241, all monomer voltage acquisition data is extracted from the effective monitoring parameter data by the channel mapping index mechanism, arranged in ascending order according to the channel number to construct a two-dimensional array, each column corresponds to a numbered monomer battery, and each row is the voltage value of the same sampling period, the matrix dimension is fixed as n x m, where n is the sampling frame number, m is the monomer channel number, the value range is 1 to 26, and the output is a monomer voltage matrix; in step S242, all charge and discharge current acquisition data is extracted, the positive and negative of the sampling value is distinguished by the sign judgment method, the positive value is marked as discharge, and the negative value is marked as charge, the current value of all sampling points in each sampling period is averaged, and the output result constitutes a current feature vector; in step S243, the temperature acquisition data is extracted, mapped to the battery pack structure layout table according to the thermosensitive sensor number, records the battery shell position area corresponding to each number, and calculates the temperature mean, maximum and minimum value under each number, the result is arranged in order, and a temperature distribution feature vector is formed; in step S244, the internal resistance acquisition data is extracted, the time sliding window width is set to 5 minutes, the step is 1 minute, the internal resistance change of each channel in the window is calculated, and the maximum amplitude value and change trend slope are recorded, and an internal resistance change feature vector is output; in step S245, the monomer voltage matrix, the current feature vector, the temperature distribution feature vector and the internal resistance change feature vector are spliced and combined according to the preset field structure, the field order is fixed as: voltage, current, temperature, internal resistance, and the unit of all parameter values is uniformly processed, wherein the voltage is mV, the current is mA, the temperature is ℃, and the internal resistance is mΩ, and after merging, a multi-dimensional monitoring matrix data is generated; in step S246, the above multi-dimensional monitoring matrix data is added with the timestamp field, the device number field (fixed as an unsigned integer value between 1 and 255) and the acquisition cycle number field (format YYYYMMDDHHMMSS) generated by the system, each group of data is encapsulated in JSON structure, and finally a complete battery running state monitoring data set is formed.

[0075] The present application realizes the deep analysis and structured expression of the key operation indicators of the battery pack by systematically summarizing, classifying and feature extracting the multi-dimensional effective monitoring parameters. The ordered summary of the monomer voltage matrix enhances the intuitive comparison ability between the battery monomers; the directional classification and periodical average of the current feature vector effectively captures the dynamic characteristics of the charge and discharge process; the spatial mapping and statistical analysis of the temperature distribution feature vector improves the fine monitoring of the battery thermal state; and the trend extraction of the internal resistance change feature vector provides a key reference for the battery health condition and degradation trend. The data splicing and label unification of the multi-dimensional monitoring matrix strengthen the time sequence correlation and equipment traceability of the data, facilitate the comprehensive analysis and intelligent judgment in multiple dimensions and across parameters, and significantly improve the accuracy of the battery running state evaluation and the effectiveness of the decision support.

[0076] Preferably, step S3 includes the following steps:

[0077] Step S31: performing upper and lower threshold judgment on the single cell voltage data collected in the battery operation status monitoring data set to obtain a voltage abnormality judgment result;

[0078] Step S32: Calculate the real-time temperature rise rate of the surface temperature data collected in the battery operation status monitoring data set, and judge the calculation result using a preset dynamic temperature alarm curve to obtain a temperature abnormality determination result;

[0079] Step S33: performing time series trend fitting analysis on the battery internal resistance collected data in the battery operation status monitoring data set to obtain internal resistance degradation trend data;

[0080] Step S34: Identify the fluctuation characteristics of the charge and discharge current data collected in the battery operation status monitoring data set, and compare and analyze the characteristics of the historical power curve to obtain an abnormal current fluctuation indicator;

[0081] Step S35: Based on the voltage anomaly determination result, the temperature anomaly determination result, the internal resistance degradation trend data and the current fluctuation anomaly identification, rule fusion and grade classification processing of multi-dimensional abnormal features are performed to obtain the abnormal state recognition result.

[0082] In the embodiment of the present application, step S31 calls monomer voltage acquisition data by the abnormality judgment control module, compares the upper and lower limits of each channel voltage value in each frame of data, the threshold range is fixed at 1800mV to 2500mV, if the voltage value exceeds this range, the channel number and voltage value are recorded and marked as "under-voltage" or "over-voltage", and the voltage abnormality judgment result is output; in step S32, the temperature change calculator performs temperature rise rate calculation on each frame of temperature acquisition data, the calculation method is ΔT / Δt, where ΔT is the difference between two consecutive frames of temperature, and Δt is a fixed sampling interval of 1000ms, if the rate exceeds 5℃ / min and the current temperature is higher than 45℃, it is considered that the temperature rise is abnormal, and the alarm level is judged according to the dynamic temperature alarm curve, the curve is a two-dimensional threshold relationship composed of temperature and time, and the temperature abnormality judgment result is output; in step S33, each channel resistance acquisition data is extracted, a sliding time window of 5 minutes is used to perform growth amount and slope calculation in the continuous segment, the growth trend K is obtained by using R(t) linear fitting method, the unit is mΩ / min, if K is continuously greater than 5mΩ / min and lasts for more than 3 time periods, the channel is marked as internal resistance degradation, and the internal resistance degradation trend data is output; in step S34, the current acquisition data is extracted, the standard deviation of the current in the period is calculated, and the peak fluctuation amplitude ΔImax is identified, if the standard deviation exceeds 10000mA and ΔImax exceeds 25000mA, and the deviation of the average power value in the historical record in the same period exceeds 20%, the current fluctuation abnormality identification is generated; in step S35, the output data of steps S31 to S34 are aggregated and processed by the abnormality fusion module, the abnormal parameter list of each channel in the current period is established, the number of abnormal types is counted, if a single abnormality is counted as level 1, double abnormality is counted as level 2, and three or more abnormality is counted as level 3, the level result is represented by 4-bit binary mark, and the abnormal time, parameter name, abnormal value and level mark are uniformly packaged, and the abnormal state identification result is output.

[0083] The present application realizes comprehensive and accurate monitoring of the running state of the battery through multi-dimensional abnormality judgment and trend analysis. The voltage upper and lower limit threshold judgment effectively identifies potential voltage abnormalities, ensuring safe operation of the battery; real-time temperature rise rate calculation combined with a dynamic alarm curve improves the sensitivity and early warning capability of temperature abnormalities; internal resistance time series trend fitting provides a scientific basis for battery degradation evaluation and supports dynamic tracking of the health state; current fluctuation characteristic identification and historical feature comparison enhance the detection accuracy of abnormal fluctuations and avoid missing potential faults; the fusion and level classification of multi-dimensional abnormality features realize systematic integration and hierarchical expression of abnormal information, significantly enhancing the reliability and decision support level of abnormal state identification, thereby improving the safety guarantee capability and operation stability of the battery management system.

[0084] Preferably, step S33 comprises the following steps:

[0085] Step S331: Channel identification and timestamp extraction are performed on the battery internal resistance acquisition data in the battery operating state monitoring data set, and an internal resistance time series data is constructed;

[0086] Step S332: Outlier identification and interpolation repair are performed on the internal resistance time series data, and a cleaned internal resistance data sequence is obtained;

[0087] Step S333: The internal resistance mean value and the increase rate in a continuous time period are calculated for the cleaned internal resistance data sequence, and internal resistance change interval statistical values are obtained;

[0088] Step S334: Least square fitting and polynomial modeling are performed for each channel based on the internal resistance change interval statistical values, an internal resistance time change curve is constructed, and a channel-level internal resistance fitting trend curve is obtained;

[0089] Step S335: Comparative analysis is performed on the channel-level internal resistance fitting trend curve and the preset normal change range, and a curve segment exceeding the growth rate threshold is identified, and an internal resistance degradation channel identifier is obtained;

[0090] Step S336: Channel number aggregation and degradation level classification processing are performed on the internal resistance degradation channel identifier, and internal resistance degradation trend data is obtained.

[0091] In the embodiment of the present application, first, in step S331, the internal resistance data processing module extracts the internal resistance sampling value with the identification field of "R" from the battery operating state monitoring data set, and establishes a mapping index according to the channel number, extracts the sampling time stamp corresponding thereto, and constructs a two-dimensional structure R(n, t), wherein n is the channel number, t is the sampling time, each row in the structure records the internal resistance value of a channel at different time, forming the internal resistance time series data; in step S332, the difference ΔRi between adjacent two sampling points in the internal resistance time series of each channel is detected in sequence, if ΔRi exceeds 3 times of the average value of the previous 10 frames, the point is marked as an outlier and recorded, and the linear interpolation is performed on the adjacent two effective data points R-i-1 and R-i+1 above and below the point to fill the point, the filling value is calculated as (R-i-1+R-i+1) / 2, and the cleaned internal resistance data sequence is generated; in step S333, the cleaned data of each channel is grouped according to 5 minutes as a time period, the internal resistance mean value Rm and the maximum amplitude ΔRmax of each segment are calculated respectively, the mean value is calculated by using the weighted average method, and the weight is the reciprocal of the time interval, the amplitude is obtained from the difference between the maximum value and the minimum value in the segment, and the speed K=ΔRmax / Δt of each segment is calculated simultaneously, the unit is mΩ / min, and the output is the internal resistance change interval statistical value; in step S334, the statistical value is input into the fitting calculation module, a quadratic function fitting is performed on the internal resistance time series of each channel by using the least square residual method, and the fitting trend curve of the internal resistance of each channel with time is generated after fitting; in step S335, the segment speed analysis is performed on each channel level fitting trend curve, the normal growth rate threshold is set as 5 mΩ / min, if the derivative value in any interval of the curve is greater than the threshold for more than 10 minutes, the channel is marked as a degraded channel, and the degradation start time and the internal resistance change interval value are recorded, and the output is the internal resistance degraded channel identification; in step S336, all internal resistance degraded channels are aggregated according to the number, and the number of continuous abnormal segments, the maximum slope value and the cumulative resistance increment value of each channel are counted, which correspond to three dimensions of abnormal frequency, development speed and cumulative risk respectively, and the weighted addition method L=0.4×N+0.3×Kmax+0.3×ΣΔR is used for scoring, wherein N is the number of abnormal segments, Kmax is the maximum rate, and ΣΔR is the cumulative increment, finally, the L value interval is divided into three categories of 1 level (L≤5), 2 level (5

[0092] The application realizes fine dynamic monitoring and evaluation of the change of the battery internal resistance through systematic data cleaning, statistical analysis and modeling methods. Channel identification and timestamp extraction ensure the time sequence integrity and channel accuracy of the internal resistance data; outlier processing and interpolation repair improve the data quality and continuity, and eliminate abnormal noise interference; the statistical calculation of the internal resistance change interval reveals the detailed characteristics of the internal resistance growth, providing a quantitative basis for subsequent trend analysis; the fitting curve based on the least square method and polynomial modeling accurately reflects the time variation law of the internal resistance, effectively capturing the degradation dynamics; comparison of the fitting trend and the normal range and identification of abnormal segments realize the accurate positioning of abnormal degradation channels; the final aggregation and grade classification facilitate the intuitive expression of the internal resistance degradation degree, providing scientific and operable support for battery health management and maintenance decision-making, significantly improving the accuracy and timeliness of battery performance evaluation.

[0093] Preferably, the state modeling analysis based on the abnormal state recognition result and the battery historical operation number in step S4 comprises:

[0094] The historical monitoring sequence data is obtained by performing missing value filling, outlier removal and time sequence reconstruction on the battery historical operation data.

[0095] The historical operation feature vector is obtained by extracting the single cell voltage fluctuation amplitude, internal resistance growth rate and temperature drift trend based on the historical monitoring sequence data.

[0096] The abnormal association deviation data is obtained by associating and matching the abnormal state recognition result and the historical operation feature vector, and identifying the degree of deviation of the current parameters from the historical mean value and the fluctuation threshold.

[0097] The state modeling sample data is obtained by merging the abnormal association deviation data and the preset health sample data, and constructing a labeled training sample set.

[0098] The adaptive modeling parameters are obtained by performing online learning and parameter updating on the preset embedded AI evaluation model based on the state modeling sample data.

[0099] The health state evaluation result data is obtained by predicting the current battery operation health state based on the adaptive modeling parameters.

[0100] In the embodiment of the present application, first, the data preprocessing control module extracts the historical operation data stored in the past 30 days at a 5-minute sampling interval, the extraction content includes five types of parameters such as single voltage, total voltage, internal resistance, temperature and current, and the continuity verification mechanism is called to detect the number consistency of data records, if the sequence is interrupted, it is marked as missing value and linear interpolation is performed, the interpolation method uses the mean value of the adjacent two effective data to fill; for abnormal value identification, whether it exceeds the physical threshold range is determined by using the fixed range constraint method, the voltage range is 1500 to 3000 mV, the temperature range is -20 to 80℃, the internal resistance range does not exceed 500 mΩ, and the current absolute value is not greater than 100000 mA, if it is out of limit, the frame data is rejected, and the complete time index is reconstructed according to the sampling time, and the historical monitoring sequence data is output; the feature extraction unit segments and calculates the voltage fluctuation amplitude, internal resistance growth rate and temperature drift trend of each channel according to the historical monitoring sequence after cleaning, the temperature drift trend is represented by the maximum change slope in the period, the slope value is calculated as ΔT / Δt, and the time interval Δt is fixed as 10 minutes, and the calculated results are arranged according to the channel number and output as the historical operation feature vector; the abnormal correlation analysis module matches the voltage abnormality, current fluctuation, temperature rise abnormality and internal resistance degradation labels in the abnormal state recognition result of the current period with the corresponding parameters in the historical operation feature vector one by one, respectively calculates the deviation rate relative to the historical mean value, the deviation rate calculation method is δ=(Xt-μ) / σ, wherein Xt is the current value, μ is the historical mean value, and σ is the historical standard deviation, the channel data with a deviation rate exceeding ±2 is recorded as an associated abnormal item, and the output is abnormal correlation deviation data; the sample construction module merges the abnormal correlation deviation data with the known healthy operation state sample data built in the system, unifies the format according to the field standard, constructs a training sample set containing channel number, current deviation rate, voltage amplitude, temperature rise rate, internal resistance growth rate and health level label, generates state modeling sample data; the state modeling sample data is input into the parameter learning program in the embedded evaluation processing module, the internal calculation parameters are updated and adjusted through the least square error minimization method, the new fitting parameter set is obtained after iteration, and the output is the adaptive modeling parameter; finally, the prediction calculation unit calls the above updated parameters to comprehensively score and analyze the battery operation state of the current period, the score interval is set to 0 to 100 points, wherein the score greater than 80 is defined as the healthy state, 60 to 80 is the sub-healthy state, and less than 60 is the deterioration state, and the state result is combined with the corresponding channel number, voltage, current, temperature and internal resistance information to output as the health state evaluation result data.

[0101] The application ensures the integrity and accuracy of time series data through systematic cleaning and reconstruction of historical operation data, lays a solid foundation for subsequent feature extraction, the extracted key operation features comprehensively reflect the voltage fluctuation, internal resistance change and temperature trend of the battery, and enhance the understanding of the dynamic evolution of the battery state, the association and matching of abnormal state and historical features realize the accurate quantification of the current parameter deviation degree, which is helpful for timely identifying abnormal development trend, the construction of labeled training sample set provides high-quality learning data for embedded AI model, realizes effective training and self-adaptive update of the model, online learning and parameter optimization improve the response speed and prediction accuracy of the model to the battery state change, and the final health state evaluation result provides scientific and dynamic health judgment basis for the battery management system, and significantly enhances the intelligent level of battery safety guarantee and operation and maintenance decision.

[0102] Preferably, step S5 comprises the following steps:

[0103] Step S51: field separation processing is performed on the battery health state evaluation data, SOH value, SOC value, degradation level and temperature risk level are extracted, and evaluation index data unit is obtained;

[0104] Step S52: based on the evaluation index data unit, health state features of various types are classified and counted, and matching analysis is performed in combination with a preset alarm level standard and a preset maintenance strategy library, to obtain a health state diagnosis conclusion;

[0105] Step S53: the health state diagnosis conclusion and the evaluation index data unit are subjected to format combination processing, structured information report content is constructed, and information report data is obtained;

[0106] Step S54: mapping numbering of a communication transmission field is performed based on the information report data, and information types, station number addresses, data lengths and verification modes are organized according to a communication protocol standard, to obtain communication frame structure data;

[0107] Step S55: protocol encapsulation is performed on the communication frame structure data, and control information is added to the protocol encapsulation result, to obtain control communication data frames;

[0108] Step S56: host address setting, communication baud rate configuration and channel selection identification embedding are performed on the control communication data frames, to obtain communicable control data.

[0109] In the embodiment of the present application, step S51 calls the predefined field in the battery health state evaluation data by the data analysis controller, extracts the SOH value (expressed in percentage, ranging from 0 to 100), the SOC value (expressed in capacity ratio, unit: %), the degradation level (expressed in unsigned integer, corresponding to mild, moderate and severe respectively, represented by 1 to 3), and the temperature risk level (represented by 0 to 2, corresponding to normal, warning and overheating respectively) through field separation operation, and outputs as the evaluation index data unit; in step S52, the classification analysis module aggregates and counts the SOH value, the SOC value, the degradation level and the temperature risk level of all channels according to the field content, judges whether the channels with SOH value less than 60 or temperature risk level of 2 exceed 10% of the total number of channels, if the condition is met, determines as "system level risk" according to the system preset fault level standard; meanwhile, searches the preset maintenance strategy library, extracts the recommended operation instruction according to the matching field corresponding relationship, such as when the degradation level is 3 and the SOH value is less than 50, the diagnosis conclusion is "suggested to replace the battery monomer", and finally outputs the structured health state diagnosis conclusion; in step S53, the report building module aligns and combines the diagnosis conclusion with the original evaluation index data unit in the field, and uniformly generates the structured information frame with the field order of [device number] [channel number] [SOH value] [SOC value] [degradation level] [temperature level] [diagnosis conclusion number], the total length of each frame is limited to 64 bytes, and the output information report data; in step S54, the information report data is mapped to the communication data field through the communication protocol field mapping table, the information type field is represented by 2-bit hexadecimal number, the station number address field is encoded by 8-bit unsigned integer, the data length field is automatically written according to the actual information length, the check mode adopts CRC16 cyclic redundancy check, the check value is calculated and attached to the end of the data segment, and the communication frame structure data is generated; in step S55, the communication frame structure data is input to the protocol packaging unit, the frame header "AA55" and the frame tail "55AA" are added according to the RS485 communication protocol format, and the frame number, frame type identifier and transmission control bit are written, and the complete control communication data frame is output; in step S56, the target host address is written into the address field through the host address manager, the address range is limited to 0 to 255, the communication baud rate is set to 9600bps, the channel selection identifier is converted to binary bitmap according to the battery number coding and embedded into the specified position of the data frame, and finally the communicable control data is generated.

[0110] The application realizes comprehensive quantitative expression of the health state by extracting and classifying the battery health evaluation data system, accurately obtaining key health indicators, combining preset alarm standards and maintenance strategies, enhancing the scientificity and practicality of the diagnosis conclusion, facilitating timely discovery of potential risks and guiding maintenance decisions, improving the standardization and readability of data expression through the generation of structured information reports, effectively supporting subsequent data transmission and processing, ensuring reliable transmission and compatibility of information through standardized communication protocol processing and protocol packaging, improving the communication efficiency and flexibility between systems through the perfect configuration of control communication data frames, and finally realizing accurate, efficient and safe remote transmission and control management of battery health information, significantly improving the intelligent level and operation and maintenance response capability of the battery management system.

[0111] Preferably, step S6 comprises the following steps:

[0112] Step S61: performing communication interface identification on the communicable control data to obtain an interface identification result;

[0113] Step S62: extracting a starting bit, an address field, a data length and a check bit from the control communication data frame based on the interface identification result to obtain communication protocol structure data;

[0114] Step S63: performing field decoding on the communication protocol structure data to extract an evaluation type, an alarm level, an SOH value and an SOC value to obtain state decoding data;

[0115] Step S64: classifying the information types based on the state decoding data, and marking each type as running monitoring data, health evaluation data and maintenance prompt information to obtain running data classification labels;

[0116] Step S65: mapping and fusing the running data classification labels and the state decoding data, and sending them to the corresponding host computer node to obtain host computer display data;

[0117] Step S66: performing format conversion and graphic rendering on the host computer display data, and synchronously outputting them to a local liquid crystal display component, automatically updating the monitoring indicators according to a preset refresh period to obtain running display data.

[0118] In the embodiment of the present invention, first, in step S61, the communication access identification module detects the physical port level characteristics and pin voltage status of the control communication data, determines whether the access mode is RS485 or RS422 communication interface, and outputs the interface identification flag according to the identification result, sets the configuration parameters of the data receiving channel and the driving level conversion circuit, and outputs the interface identification result; in step S62, the protocol frame parsing rule corresponding to the interface identification result is called by the parsing controller, and the start bit (fixed to "AA55"), the address field (8-bit unsigned integer), the data length field (1 6-bit unsigned integer) and the last CRC16 check bit (16 bits), the parsed field content is partitioned into structures, and the communication protocol structure data is output; in step S63, the field decoding module parses the evaluation type field (for example, 0x01 is health status information, 0x02 is fault alarm information), the alarm level field (with 0 to 3 marking levels), the SOH value field (percentage format, unit is %), and the SOC value field (power ratio format, unit is %) in sequence according to the field definition table, checks the legitimacy of the field content and packages it into a structured output, and outputs the state decoding data; in step S64, the state recognition control The control unit marks the data as operation monitoring data (flag code 0xA1), health assessment data (flag code 0xA2) or maintenance prompt information (flag code 0xA3) according to the content of the evaluation type field in the status decoding data, and matches the flag value with the channel number, timestamp, etc., and outputs the operation data classification label; in step S65, the operation data classification label and the status decoding data are fused through the host computer communication mapping manager to construct a host computer display data frame with the field order of [channel number] [data type] [SOH value] [SOC value] [alarm level] [timestamp], and sends it according to the mapping relationship table. It is sent to the corresponding module processing port in the host computer system to complete the remote data push operation; in step S66, the display control module converts the received host computer display data into Chinese labels and engineering units, and loads them into the graphics rendering engine. According to the parameter type, they are rendered as real-time line graphs (SOH, SOC), bar graphs (voltage, current) and icon marks (alarm level), and the rendered image is synchronously output to the LCD screen on the front panel of the measuring instrument. By setting the refresh cycle (1000ms), the display cache refresh is triggered, and each monitoring indicator is automatically updated, and finally the local and remote dual-channel output of the operation display data is completed.

[0119] The application realizes efficient decoding and information extraction of the communication protocol by accurate identification and structured analysis of the control communication data, ensures accurate acquisition of key evaluation indexes and alarm information, classifies and labels the data in order based on the classification identification, improves the orderliness and pertinence of data management, guarantees accurate distribution and real-time transmission of the data through the mapping fusion and directional sending function, supports effective monitoring and management of the upper computer, enhances the intuitiveness and easy understanding of data display through the format conversion and graphic rendering, realizes dynamic update and real-time display of the monitoring indexes through the automatic refreshing mechanism, significantly improves the interactive experience and response speed of the system, and strengthens the information transmission efficiency and intelligent visualization level of the overall battery management system.

[0120] Preferably, the application further provides a control system of a battery measuring instrument for executing the control method of the battery measuring instrument, the control system of the battery measuring instrument comprising:

[0121] a data acquisition module for acquiring single cell voltage, total voltage, current and internal resistance data of the battery pack to obtain initial electrical parameters, and acquiring surface temperature of the battery pack to obtain initial thermodynamic parameters;

[0122] a data processing module for synchronously integrating the initial electrical parameters and the initial thermodynamic parameters in time sequence, and unifying the format to obtain a battery operation state monitoring data set;

[0123] an abnormality detection module for performing dynamic threshold determination and trend analysis on each parameter based on the battery operation monitoring data set to obtain an abnormal state recognition result;

[0124] a health assessment module for obtaining battery historical operation data, performing state modeling analysis based on the abnormal state recognition result and the battery historical operation data to obtain battery health state assessment data;

[0125] a report generation and transmission module for constructing an information report based on the battery health state assessment data, and performing encapsulation processing through a communication interface to obtain control communication data;

[0126] a communication analysis and display module for performing communication protocol analysis on the control communication data, sending the control communication data to an upper computer, and performing real-time visualization to obtain operation display data.

[0127] Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting, the scope of the application is not limited by the above description, and therefore all changes falling within the meaning and scope of the equivalent elements of the application file are intended to be included in the application.

[0128] The foregoing is considered as illustrative only of the principles of the application. Numerous modifications and changes will readily occur to those skilled in the art, and it is intended to embrace all such modifications and changes that fall within the scope of the application. Accordingly, the application is not to be restricted in scope to the specific embodiments disclosed herein but is to be accorded the full scope that the principles and novel features request appropriately granted.

Claims

1. A control method for a battery measuring instrument, characterized in that: The following steps are involved: Step S1: Collecting the single cell voltage, total voltage, current and internal resistance data of the battery pack to obtain initial electrical parameters; collecting the surface temperature of the battery pack to obtain initial thermodynamic parameters; Step S2: Synchronously integrate the initial electrical parameters and the initial thermodynamic parameters and unify their formats to obtain a battery operation status monitoring data set; Step S3: Perform dynamic threshold determination and trend analysis on various parameters based on the battery operation monitoring data set to obtain abnormal state identification results; Step S4: Obtain historical battery operation data; perform state modeling analysis based on the abnormal state identification result and the battery historical operation data to obtain battery health status assessment data; Step S5: constructing an information report based on the battery health status assessment data, and encapsulating and processing it through the communication interface to obtain control communication data; Step S6: parse the control communication data through the communication protocol, send it to the host computer, and perform real-time visualization to obtain operation display data.

2. The control method of the battery measuring instrument according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: differentially collecting the voltage signal of each single battery in the battery pack, and filtering and amplifying the voltage signal through a common mode suppression circuit to obtain single battery voltage collection data; Step S12: collecting the total voltage signal at both ends of the battery pack, and stabilizing the total voltage signal through a DC / DC voltage conversion component to obtain total voltage collection data; Step S13: collecting the current signal in the battery pack circuit, and performing signal conditioning processing on the current signal based on the current sampling conversion component to obtain charge and discharge current collection data; Step S14: setting an impedance test loop for the battery pack; performing controllable load output adjustment on the impedance test loop, and performing dynamic response measurement through the internal resistance detection component to obtain battery internal resistance acquisition data; Step S15: Recording the single cell voltage data, the total voltage data, the charge and discharge current data, and the battery internal resistance data as initial electrical parameters; Step S16: multiple thermal sensors are set on the battery pack housing; multiple thermal sensor signals are collected, and initial thermodynamic parameters are obtained through thermal signal channel selection and synchronous control.

3. The control method of the battery measuring instrument according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: performing sampling timestamp alignment processing on the initial electrical parameters and the initial thermodynamic parameters to obtain a synchronized time series data set; Step S22: performing format standardization conversion on the synchronous time series data set, unifying the parameter units, precision and structure format, and obtaining a standardized parameter data set; Step S23: Perform integrity check and outlier screening on the standardized parameter data set to eliminate packet loss data, erroneous measurement values, and communication noise interference to obtain valid monitoring parameter data; Step S24: performing parameter aggregation and logical organization on the effective monitoring parameter data according to the preset field structure, constructing a multi-dimensional monitoring matrix, and obtaining a battery operation status monitoring data set.

4. The control method of the battery measuring instrument according to claim 3, characterized in that: Step S24 includes the following steps: Step S241: Summarize and process the cell voltage acquisition data in the valid monitoring parameter data in the order of channel numbers to obtain a cell voltage matrix; Step S242: performing direction label classification and period average calculation on the charge and discharge current collection data in the effective monitoring parameter data to obtain a current feature vector; Step S243: performing position mapping and statistical analysis on the battery surface temperature acquisition data in the effective monitoring parameter data according to the sensor number to obtain a temperature distribution feature vector; Step S244: performing incremental change analysis within a time window on the internal resistance acquisition data in the effective monitoring parameter data, extracting the maximum increment value and growth trend, and obtaining an internal resistance change feature vector; Step S245: combining the cell voltage matrix, current eigenvector, temperature distribution eigenvector, and internal resistance change eigenvector according to a preset field structure and logical order to obtain multi-dimensional monitoring matrix data; Step S246: adding a unified time tag, device number and acquisition cycle identification information to the multi-dimensional monitoring matrix data to obtain a battery operation status monitoring data set.

5. The control method of the battery measuring instrument according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: performing upper and lower threshold judgment on the single cell voltage data collected in the battery operation status monitoring data set to obtain a voltage abnormality judgment result; Step S32: Calculate the real-time temperature rise rate of the surface temperature data collected in the battery operation status monitoring data set, and judge the calculation result using a preset dynamic temperature alarm curve to obtain a temperature abnormality determination result; Step S33: performing time series trend fitting analysis on the battery internal resistance collected data in the battery operation status monitoring data set to obtain internal resistance degradation trend data; Step S34: Identify the fluctuation characteristics of the charge and discharge current data collected in the battery operation status monitoring data set, and compare and analyze the characteristics of the historical power curve to obtain an abnormal current fluctuation indicator; Step S35: Based on the voltage anomaly determination result, the temperature anomaly determination result, the internal resistance degradation trend data and the current fluctuation anomaly identification, rule fusion and grade classification processing of multi-dimensional abnormal features are performed to obtain the abnormal state recognition result.

6. The control method of the battery measuring instrument according to claim 5, characterized in that: Step S33 includes the following steps: Step S331: performing channel identification and timestamp extraction on the battery internal resistance data collected in the battery operation status monitoring data set to construct internal resistance time series data; Step S332: performing outlier identification and interpolation repair on the internal resistance time series data to obtain a cleaned internal resistance data series; Step S333: calculating the internal resistance mean and the increase rate in a continuous time period for the cleaning internal resistance data sequence to obtain the internal resistance change interval statistics; Step S334: performing least squares fitting and polynomial modeling on each channel based on the internal resistance variation interval statistics, constructing an internal resistance time variation curve, and obtaining a channel-level internal resistance fitting trend curve; Step S335: Comparing and analyzing the channel-level internal resistance fitting trend curve with a preset normal variation range, and identifying the curve segment exceeding the growth rate threshold, to obtain an internal resistance degradation channel identifier; Step S336: performing channel number aggregation and degradation level classification processing on the internal resistance degradation channel identifiers to obtain internal resistance degradation trend data.

7. The control method of the battery measuring instrument according to claim 1, characterized in that: The state modeling analysis based on the abnormal state identification result and the battery historical operation data in step S4 includes: Fill missing values, remove outliers, and reconstruct time series of historical battery operation data to obtain historical monitoring sequence data; Based on historical monitoring sequence data, the voltage fluctuation amplitude, internal resistance growth rate and temperature drift trend of each cell are extracted to obtain the historical operation characteristic vector; Perform correlation matching on the abnormal state identification results and historical operation feature vectors, and identify the degree to which the current parameters deviate from the historical mean and fluctuation threshold to obtain abnormal correlation deviation data; Merge abnormal correlation deviation data and preset healthy sample data, and construct a labeled training sample set to obtain state modeling sample data; Based on the state modeling sample data, the preset embedded AI evaluation model is trained and parameter updated online to obtain adaptive modeling parameters. The current battery health status is predicted based on the adaptive modeling parameters to obtain health status assessment result data.

8. The control method of the battery measuring instrument according to claim 1, characterized in that: Step S5 includes the following steps: Step S51: performing field separation processing on the battery health status assessment data, extracting the SOH value, SOC value, degradation level and temperature risk level, and obtaining an assessment indicator data unit; Step S52: Classify and count various health status characteristics based on the evaluation indicator data unit, and perform matching analysis in combination with the preset alarm level standard and the preset maintenance strategy library to obtain a health status diagnosis conclusion; Step S53: Formatting and combining the health status diagnosis conclusion and evaluation index data units to construct structured information report content and obtain information report data; Step S54: mapping the communication transmission fields based on the information report data, and organizing the information type, station address, data length and check method according to the communication protocol standard to obtain the communication frame structure data; Step S55: performing protocol encapsulation on the communication frame structure data, and adding control information to the protocol encapsulation result to obtain a control communication data frame; Step S56: Setting the host address, configuring the communication baud rate and embedding the channel selection identifier into the control communication data frame to obtain the communicative control data.

9. The control method of the battery measuring instrument according to claim 1, characterized in that: Step S6 includes the following steps: Step S61: performing communication interface identification on the communicable control data to obtain an interface identification result; Step S62: extracting the start bit, address field, data length and check bit of the control communication data frame based on the interface identification result to obtain communication protocol structure data; Step S63: Decode the communication protocol structure data by field, extract the assessment type, alarm level, SOH value and SOC value, and obtain status decoding data; Step S64: Classify and identify information types based on the state decoding data, and mark each type as operation monitoring data, health assessment data, and maintenance prompt information, to obtain an operation data classification label; Step S65: Map and fuse the operation data classification label and the state decoding data, and send them to the corresponding host computer node to obtain the host computer display data; Step S66: Perform format conversion and graphic rendering on the upper computer display data, and output it to the local liquid crystal display component synchronously. The monitoring indicators are automatically updated according to the preset refresh cycle to obtain the operation display data.

10. A control system for a battery measuring instrument, characterized in that: For executing the control method of the battery measuring instrument according to claim 1, the control system of the battery measuring instrument comprises: The data acquisition module is used to collect the single cell voltage, total voltage, current and internal resistance data of the battery pack to obtain the initial electrical parameters; the surface temperature of the battery pack is collected to obtain the initial thermodynamic parameters; The data processing module is used to synchronize the initial electrical parameters and initial thermodynamic parameters in a time-series manner and unify their formats to obtain a battery operation status monitoring data set; The anomaly detection module is used to perform dynamic threshold determination and trend analysis on various parameters based on the battery operation monitoring data set to obtain abnormal state identification results; The health assessment module is used to obtain historical battery operation data; based on the abnormal state identification results and the battery's historical operation data, the state modeling analysis is performed to obtain battery health status assessment data; The report generation and transmission module is used to construct information reports based on battery health status assessment data and encapsulate them through the communication interface to obtain control communication data; The communication analysis and display module is used to analyze the communication protocol of the control communication data, send it to the host computer, and perform real-time visualization to obtain operation display data.

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