Overcharge monitoring and automatic cutting-off system and method for lithium battery of communication equipment

Abstract

The invention discloses a communication equipment lithium battery overcharge monitoring automatic cut-off system and method, and relates to the technical field of lithium battery monitoring. Comprising a data acquisition module which is used for acquiring an overcharge associated data set in a lithium battery charging process and preprocessing the overcharge associated data set; the overcharge associated data set includes raw acquired data of the positive lattice oxygen escape rate. According to the invention, systematic coupling correction of a temperature influence coefficient, a multiplying power influence coefficient and a sensor drift influence coefficient is carried out on an original acquisition value of the oxygen escape rate of the positive lattice, and real-time accurate oxygen escape rate monitoring is combined, so that the interference of temperature fluctuation, charging multiplying power change and sensor aging on monitoring data can be accurately eliminated; according to the method, the oxygen escape rate monitoring value is closer to the real state, early-stage accurate identification of the overcharge risk is achieved, misjudgment or missed judgment caused by data deviation is avoided, and the lithium battery overcharge monitoring precision and reliability are improved from the source.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of lithium battery monitoring, in particular to a communication equipment lithium battery overcharge monitoring automatic cut-off system and method. BACKGROUND

[0002] A lithium battery is an energy storage device with lithium metal or lithium alloy as a positive electrode material and a non-aqueous electrolyte. Its core is composed of a positive electrode, a negative electrode, an electrolyte and a separator. With the advantages of high energy density, long cycle life and low self-discharge rate, lithium batteries have been widely used in consumer electronics, new energy vehicles, energy storage systems and other fields, and have become one of the core devices for energy storage and supply in modern society. Among them, the stability of the positive electrode material directly affects the safety performance of the lithium battery. In the charging and discharging process, the stability of the positive electrode crystal structure determines the efficiency of lithium ion deintercalation, and is closely related to whether the battery will have a thermal runaway and other safety accidents.

[0003] Overcharge is one of the most common safety hazards in the use of lithium batteries. When a lithium battery is continuously charged above the rated voltage, the positive electrode material will collapse due to excessive lithium ion deintercalation, and lattice oxygen will escape. The escaped oxygen reacts with the electrolyte to release a large amount of heat and gas. If the heat accumulates and cannot be dissipated, it may cause the battery to swell, catch fire or even explode. Therefore, overcharge monitoring is a key link to ensure the safe operation of lithium batteries. Its core is to capture the characteristic signals (such as voltage change, temperature rise, oxygen escape rate, etc.) in the overcharge process in real time, judge the battery state in time and trigger protective measures.

[0004] The existing overcharge monitoring technology relies on indirect indicators such as voltage and temperature, which has obvious limitations: first, the response is lagging, the significant change in voltage and temperature often occurs after the irreversible damage of the positive electrode material, making it difficult to achieve early warning; second, the precision is insufficient, the temperature fluctuations and charging rate changes are not considered to interfere with the monitoring signals, and the drift error of the sensor after long-term use is ignored, resulting in a large deviation between the monitoring value and the actual state; third, it lacks foresight, only based on the current state to judge the risk, and cannot predict the overcharge trend in the future. Time, the delay of protective action may cause safety accidents. These defects make it difficult for existing technology to meet the precision and advance requirements of lithium battery overcharge monitoring in high safety scenarios, so a communication equipment lithium battery overcharge monitoring automatic cut-off system and method are proposed to solve such problems. SUMMARY

[0005] Technical problems to be solved In view of the deficiencies of the prior art, the application provides a communication equipment lithium battery overcharge monitoring automatic cut-off system and method, which solves the problems raised in the background art.

[0006] Technical scheme In order to achieve the above object, the present application is realized by the following technical scheme: a communication equipment lithium battery overcharge monitoring automatic cut-off system, comprising: The data acquisition module is used for collecting overcharge associated data sets in the lithium battery charging process and pre-processing the overcharge associated data sets; the overcharge associated data sets include original collection data of the positive lattice oxygen escape rate; The data analysis module is used for performing feature extraction analysis on the pre-processed overcharge associated data sets to obtain overcharge associated characteristic values; and based on the overcharge associated characteristic values, the original collection values of the positive lattice oxygen escape rate are corrected in accuracy to obtain accurate positive lattice oxygen escape rates; The overcharge prediction module is used for taking the charging voltage change rate as an overcharge driving variable, performing trend analysis on the accurate positive lattice oxygen escape rate and the overcharge driving variable to obtain positive lattice oxygen escape rate prediction values in a future preset time; The overcharge judgment module is used for comparing the accurate positive lattice oxygen escape rate and the positive lattice oxygen escape rate prediction values with corresponding preset threshold values respectively to judge current and future overcharge risk states; The overcharge cut-off module is used for performing charging loop conduction or cut-off operation according to the overcharge risk state judgment result.

[0007] Preferably, the overcharge associated data sets further include battery temperature data, charging rate data, sensor drift calibration data and charging voltage change rate data.

[0008] Preferably, the pre-processing step of the overcharge associated data sets is as follows: For the collected overcharge associated data sets, based on the physical characteristics of the lithium battery, the effective value range corresponding to each data type is preset, and abnormal data exceeding the effective range is removed; The data processed by the above method is subjected to noise reduction processing by using a sliding average filtering method to eliminate high-frequency electromagnetic interference signals; The data processed by the above method is converted into dimensionless values based on the initial state of the new battery as a reference to complete the pre-processing.

[0009] Preferably, the step of performing feature extraction analysis based on the pre-processed overcharge associated data sets is as follows: From the battery temperature data, the real-time value and the change rate in the preset time are extracted as temperature influence features; From the charging rate data, the current value and the ratio to the rated rate are extracted as rate influence features; From the sensor drift calibration data, the cumulative drift amount and the single sampling drift value are extracted as sensor drift influence features; From the original data of the positive electrode lattice oxygen escape rate, the instantaneous value and the average value in the preset time length are extracted as the original characteristics of the oxygen escape rate; From the charging voltage rate of change data, the real-time value and the change trend of continuous multiple sampling are extracted as the voltage rate of change characteristics; The temperature influence characteristics, the rate influence characteristics, the sensor drift influence characteristics, the oxygen escape rate original characteristics and the voltage rate of change characteristics are integrated to form the overcharge correlation characteristic value.

[0010] Preferably, the step of correcting the accuracy of the original collected value of the positive electrode lattice oxygen escape rate is as follows: Based on the temperature influence characteristics in the overcharge correlation characteristic value, the temperature influence coefficient is calculated; Based on the rate influence characteristics in the overcharge correlation characteristic value, the rate influence coefficient is calculated; Based on the sensor drift influence characteristics in the overcharge correlation characteristic value, the sensor drift influence coefficient is calculated; The temperature influence coefficient, the rate influence coefficient, the sensor drift influence coefficient and the original collected value of the positive electrode lattice oxygen escape rate are coupled and operated to correct the accurate positive electrode lattice oxygen escape rate.

[0011] Preferably, the specific steps of the trend analysis of the accurate positive electrode lattice oxygen escape rate and the overcharge driving variable are as follows: The current change slope of the accurate positive electrode lattice oxygen escape rate is combined to derive the positive electrode lattice oxygen escape rate prediction value in the future preset time by linear extrapolation method.

[0012] Preferably, the step of judging the current and future overcharge risk state is as follows: The current overcharge critical threshold based on the overcharge destructive test calibration and the future overcharge warning threshold based on the oxygen escape rate growth rate are preset; If the accurate positive electrode lattice oxygen escape rate is greater than or equal to the current overcharge critical threshold, it is determined that the current overcharge risk exists; If the positive electrode lattice oxygen escape rate prediction value is greater than or equal to the future overcharge warning threshold, it is determined that the future overcharge risk exists; If neither of the above conditions is met, it is determined that there is no overcharge risk.

[0013] Preferably, the logic of executing the charging loop conduction or cut-off operation according to the overcharge risk state judgment result is as follows: If it is determined that the current overcharge risk or the future overcharge risk exists, the main loop switch and the redundant magnetic holding relay are controlled to be synchronously disconnected, and the charging loop cut-off operation is executed; If it is determined that there is no overcharge risk, the main loop switch and the redundant magnetic holding relay are maintained in the conduction state, and the normal charging is maintained.

[0014] A kind of communication equipment lithium battery overcharge monitoring automatic cut-off method, comprising the following steps: Step one: collect the overcharge associated data set in the charging process of lithium battery, and the overcharge associated data set is preprocessed;The overcharge associated data set includes the original collection data of positive lattice oxygen escape rate; Step two: the overcharge associated data set after pre-processing is analyzed by feature extraction, and the overcharge associated characteristic value is obtained;Then, based on the overcharge associated characteristic value, the original collection value of positive lattice oxygen escape rate is corrected in accuracy, and the accurate positive lattice oxygen escape rate is obtained; Step three: the accurate positive lattice oxygen escape rate and the overcharge driving variable are analyzed by trend, and the positive lattice oxygen escape rate prediction value in the future preset time is obtained; Step four: the accurate positive lattice oxygen escape rate and the positive lattice oxygen escape rate prediction value are compared with the corresponding preset threshold, and the current and future overcharge risk state is judged; Step five: according to the overcharge risk state judgment result, the charging loop is turned on or cut off.

[0015] Beneficial effects The present application has the following beneficial effects: (1) the communication equipment lithium battery overcharge monitoring automatic cut-off system and method, by the systematic coupling correction of temperature influence coefficient, rate influence coefficient and sensor drift influence coefficient to the original collection value of positive lattice oxygen escape rate, combined with real-time accurate oxygen escape rate monitoring, can accurately eliminate the interference of temperature fluctuation, charging rate change and sensor aging on monitoring data, make the oxygen escape rate monitoring value more close to the real state, realize the early accurate identification of overcharge risk, avoid the misjudgment or omission caused by data deviation, improve the accuracy and reliability of lithium battery overcharge monitoring from the source.

[0016] (2) the communication equipment lithium battery overcharge monitoring automatic cut-off system and method, by calculating the current oxygen escape rate change slope and linear extrapolation to obtain the future prediction value, combined with the double judgment logic of current overcharge critical threshold and future overcharge warning threshold, can capture the development trend of overcharge risk in advance, not only timely response to the current overcharge risk that has occurred, but also early warning to the future overcharge risk that will occur, greatly improve the time redundancy of overcharge protection, effectively avoid the irreversible damage of battery caused by monitoring lag, significantly enhance the foresight and safety of lithium battery overcharge protection.

[0017] Of course, any product implementing the present application does not necessarily need to achieve all the advantages described above at the same time. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1It is a structural diagram of a lithium battery overcharge monitoring automatic cut-off system of a communication device of the present application. Figure 2 It is a flow chart of a lithium battery overcharge monitoring automatic cut-off method of the present application. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a 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 work are within the scope of protection of the present application.

[0020] The embodiments of the present application provide a technical solution: a lithium battery overcharge monitoring automatic cut-off system of a communication device, as shown in the figure, comprising: Figure 1 A data acquisition module: used for acquiring an overcharge associated data set in the charging process of the lithium battery and pre-processing the overcharge associated data set; the overcharge associated data set comprises original acquisition data of the oxygen escape rate of the positive lattice; A data analysis module: used for performing feature extraction analysis on the pre-processed overcharge associated data set to obtain an overcharge associated feature value; and then based on the overcharge associated feature value, performing precision correction on the original acquisition value of the oxygen escape rate of the positive lattice to obtain a precise oxygen escape rate of the positive lattice; An overcharge prediction module: used for taking the charging voltage change rate as an overcharge driving variable, performing trend analysis on the precise oxygen escape rate of the positive lattice and the overcharge driving variable to obtain a positive lattice oxygen escape rate prediction value in a future preset time; An overcharge judgment module: used for comparing the precise oxygen escape rate of the positive lattice and the positive lattice oxygen escape rate prediction value with corresponding preset threshold values respectively to judge current and future overcharge risk states; An overcharge cut-off module: used for performing charging loop conduction or cut-off operation according to the overcharge risk state judgment result.

[0021] Specifically, the overcharge associated data set further comprises battery temperature data, charging rate data, sensor drift calibration data and charging voltage change rate data.

[0022] The battery temperature data is acquired by a certain brand NTC temperature sensor pasted on the positive tab surface of the lithium battery, the sensor sampling interval is set to 50 milliseconds, the analog temperature signal output by the sensor is transmitted to a certain brand 16-bit analog-to-digital conversion chip, and after the chip converts the analog signal into a digital signal, the battery temperature data in ℃ is obtained; ​The charging rate data is collected by a certain brand of Hall current sensor connected in series in the charging circuit. Specifically, the real-time charging current value is combined with the rated capacity marked by the lithium battery manufacturer to calculate the ratio. The sensor drift calibration data is collected by a calibration module of a certain brand of micro in-situ mass spectrometer. The module automatically starts calibration after completing 100 samplings. The detection value of the current sampling is compared with the standard oxygen concentration signal value built-in the module. The difference between the detection value and the standard oxygen concentration signal value is calculated as the cumulative drift. The difference between the drift value of the current sampling and the drift value of the last calibration is recorded as the single sampling drift value. The charging voltage rate of change data is collected by a certain brand of voltage sensor connected in parallel across the positive and negative electrodes of the lithium battery. The real-time charging voltage value is collected with a sampling interval of 50 milliseconds. The difference between the voltage value of the current sampling period and the voltage value of the last sampling period is calculated. The difference is divided by the sampling interval of 0.05 seconds to obtain the charging voltage rate of change data in units of V / s, i.e., the charging voltage rate of change.

[0023] The above-mentioned method is used to collect various data, which can comprehensively obtain key environmental and equipment parameters that affect the collection accuracy of the positive electrode lattice oxygen release rate: the battery temperature directly affects the sensor sensitivity, the charging rate is related to the internal airflow disturbance of the battery, the sensor drift reflects the device aging error, and the charging voltage rate of change reflects the progress of the overcharging process. The complete collection of the four types of data provides comprehensive and reliable data support for subsequent preprocessing, feature extraction, and accuracy correction, ensuring that the subsequent analysis can cover the main interference factors.

[0024] Specifically, the preprocessing steps for the overcharge-related data set are as follows: Based on the physical property manual of the lithium battery positive material (such as NCM, LCO) and 100 sets of standard charging experiment data, the effective value range corresponding to each data type is preset: the effective range of the positive electrode lattice oxygen release rate original value is 0 to 5 ppm / s, the effective range of the battery temperature is -10°C to 60°C, the effective range of the charging rate is 0.1C to 3C, the effective range of the sensor cumulative drift is -0.1 ppm / s to 0.1 ppm / s, and the effective range of the charging voltage rate of change is -0.5 V / s to 1 V / s. Data exceeding the above preset range is determined as abnormal data and is removed; After the above abnormal data removal processing, the data is processed by a sliding average filtering method with a window size of 5, i.e., for each data point, 5 data points including itself and its two adjacent data points are taken, and the arithmetic mean of the 5 data points is calculated. The average value is used as the filtered effective data. The data after the filtering processing is taken as the initial value of each data collected when the new battery is first charged as the reference value, and the ratio of each data after filtering to the corresponding reference value is calculated respectively to obtain the dimensionless overcharge correlation data set, and the pretreatment is completed.

[0025] The pretreatment adopts the above steps, which can gradually improve the data quality: the preset effective range can eliminate abnormal data, which can exclude error data caused by sudden factors such as sensor failure and electromagnetic interference, and avoid the interference of abnormal data on subsequent analysis; the moving average filtering method can effectively suppress the signal fluctuation caused by high-frequency electromagnetic interference in the charging process, and improve the data smoothness; the dimensionless processing can eliminate the influence of the dimensional difference between different data types, so that the data of different dimensions such as temperature and rate have comparability, and lay a unified dimension foundation for subsequent feature extraction and coefficient calculation.

[0026] Specifically, based on the pretreated overcharge correlation data set, the steps of feature extraction analysis are as follows: From the pretreated battery temperature data, the dimensionless data at the current time is directly read as the temperature real-time value, and all the temperature data collected in the last 1 second are selected, the difference between the last data value and the first data value in this period is calculated and divided by 1 second to obtain the temperature change rate, and the temperature real-time value and the temperature change rate are taken as the temperature influence features together; From the pretreated charging rate data, the dimensionless data at the current time is directly read as the charging rate current value, and the ratio of the current value to the dimensionless reference value (the value is 1.0) corresponding to the rated rate is calculated, and the charging rate current value and the ratio are taken as the rate influence features together; From the pretreated sensor drift calibration data, the dimensionless cumulative drift and single sampling drift value are directly read, and the cumulative drift and single sampling drift value are taken as the sensor drift influence features together; From the pretreated positive lattice oxygen escape rate original collection data, the dimensionless data at the current time is directly read as the oxygen escape rate instantaneous value, all the original data collected in the last 0.5 second are selected, and the arithmetic mean of these data is calculated, and the oxygen escape rate instantaneous value and the arithmetic mean are taken as the oxygen escape rate original features together; From the pretreated charging voltage change rate data, the dimensionless data at the current time is directly read as the voltage change rate real-time value, and the voltage change rate data of continuous 3 times is selected, the size relationship between the last data and the previous data is compared to judge the change trend of the voltage change rate, that is, the data increases for upward trend, the data decreases for downward trend, and the data remains unchanged for stable trend, and the voltage change rate real-time value and the change trend are taken as the voltage change rate features together; The temperature influence feature, the rate influence feature, the sensor drift influence feature, the oxygen evolution rate original feature and the voltage change rate feature are classified and summarized according to the data type to form the overcharge correlation feature value.

[0027] Specifically, the step of correcting the precision of the original collected value of the positive electrode lattice oxygen evolution rate is as follows: Based on the temperature influence feature in the overcharge correlation feature value, the temperature influence feature includes the real-time temperature value and the temperature change rate, the actual temperature is first back calculated through the real-time temperature value, and then the actual temperature and the temperature change rate are substituted into the formula to calculate the temperature influence coefficient; The temperature influence coefficient is obtained in the following manner: In the formula, represents the temperature influence coefficient, which is a dimensionless coefficient, and reflects the influence degree of temperature on the collection precision of the positive electrode lattice oxygen evolution rate. The greater the temperature influence coefficient is, the greater the oxygen evolution rate collection error caused by temperature factor is; The smaller the temperature influence coefficient is, the smaller the interference of temperature on the collection precision is; represents the actual temperature corresponding to the real-time temperature value, which is in ℃, and is obtained by multiplying the real-time temperature value in the temperature influence feature by 25, and represents the current actual temperature of the battery. The greater the deviation from 25℃ is, the greater the influence on is; when is higher than 25℃, will increase, and when is lower than 25℃, will decrease; 0.003 and 0.001 are fixed coefficients, which are obtained by carrying out 50 groups of comparison experiments in the temperature range of-10℃ to 60℃, linear fitting the experimental data, and obtaining a dimensionless coefficient; represents the temperature change rate in the temperature influence feature, which is in ℃ / s, and represents the speed of temperature change, the greater the temperature change rate is (the faster the temperature rises or falls), the greater the temperature influence coefficient is, the more obvious the interference of temperature on the oxygen evolution rate collection is; The rate influence coefficient is obtained in the following manner: In the formula, represents the rate influence coefficient, which is a dimensionless coefficient, and reflects the influence degree of the charging rate on the collection precision of the positive electrode lattice oxygen evolution rate. The greater the rate influence coefficient is, the smaller the interference of the charging rate on the collection precision is; The smaller the error in collecting at high rates is greater; 0.08 is a fixed coefficient, 30 sets of comparative experiments are carried out in the charge rate range of 0.1C to 3C, and a dimensionless coefficient is obtained by nonlinear fitting of experimental data; The charge rate ratio in the rate influence characteristic is represented by a dimensionless coefficient, which is obtained by dividing the current value of the charge rate by the reference value of the rated rate 1.0, and represents the proportion of the current charge rate to the rated rate. The greater the error in collecting at high rates is greater; The smaller the interference of the rate on the oxygen evolution rate collection is more significant; Based on the sensor drift influence characteristic in the overcharge correlation characteristic value, the sensor drift influence characteristic includes the cumulative drift amount and the single sampling drift value, and the sensor drift influence coefficient is calculated by substituting the two values into the formula; The sensor drift influence coefficient is obtained as follows: In the formula, The sensor drift influence coefficient is represented by a dimensionless coefficient, which reflects the influence of sensor drift on the collection accuracy of the positive electrode lattice oxygen overflow rate. The greater the sensor drift on the collection accuracy is less; The smaller the error caused by drift is greater; 0.05, 0.02 is a fixed coefficient, and a dimensionless coefficient is obtained by statistically correlating the drift amount and the detection error after 1000 cycle calibration experiments of the sensor; The cumulative drift amount in the sensor drift influence characteristic is represented by ppm / s, which represents the total drift degree of the sensor after long-term use. The greater the error in collecting at high rates is greater; The smaller the interference of the sensor aging on the oxygen evolution rate collection is more obvious; The single sampling drift value in the sensor drift influence characteristic is represented by ppm / s, which represents the drift change of the sensor at each sampling, The greater the error in collecting at high rates is greater; The smaller the interference of the single sampling drift on the collection accuracy is more significant; Based on the temperature influence coefficient, the rate influence coefficient and the sensor drift influence coefficient calculated above, and combined with the oxygen evolution rate original characteristic in the overcharge correlation characteristic value, which includes the oxygen evolution rate original collection value, the four values are substituted into the coupling formula to correct the accurate positive electrode lattice oxygen evolution rate.

[0028] The accurate positive electrode lattice oxygen evolution rate is obtained as follows: In the formula, represents the precise positive electrode lattice oxygen escape rate, specifically represents the corrected oxygen escape rate true value, and directly reflects the severity of the positive electrode lattice oxygen escape of the battery. The greater the value is, the higher the risk of overcharge is. The smaller the value is, the lower the risk of overcharge is, and the unit is ppm / s. represents the original collected value in the original characteristic of the oxygen escape rate, the unit is ppm / s, and specifically represents the uncorrected oxygen escape rate data, which is the basis for subsequent precision correction. The size of reflects the original collected oxygen escape rate. represents the temperature influence coefficient, which is a dimensionless coefficient. represents the rate influence coefficient, which is a dimensionless coefficient. represents the sensor drift influence coefficient, which is a dimensionless coefficient, and the temperature influence coefficient, the rate influence coefficient, and the sensor drift influence coefficient function to correct the interference of the corresponding factors on , so that is closer to the true value.

[0029] The precision correction adopts the above steps, which can systematically offset the errors caused by various interference factors: the calculation of the temperature influence coefficient, the rate influence coefficient, and the sensor drift influence coefficient is based on the fixed coefficients obtained by fitting a large amount of comparative experimental data, which ensures that the coefficients can truly and accurately reflect the influence of various interference factors on the oxygen escape rate collected value; the coupling operation adopts a multiplication model, which conforms to the superimposed influence law of temperature, rate, drift and other interference factors on the oxygen escape rate collected value, the calculation logic is simple and clear, the operation speed is fast, and it is suitable for the low-power operation requirements of communication equipment; the precise positive electrode lattice oxygen escape rate obtained by correction effectively improves the accuracy of the core monitoring data, and provides a high-quality data basis for subsequent overcharge prediction and risk judgment.

[0030] Specifically, the specific steps of trend analysis on the precise positive electrode lattice oxygen escape rate and the overcharge driving variable are as follows: The overcharge driving variable is determined as the charging voltage change rate, which is obtained after preprocessing the collected charging voltage data, including abnormal data elimination, sliding average filtering, and dimensionless, and the unit is V / s; The precise positive electrode lattice oxygen escape rate data collected in the last 5 sampling periods are obtained, the sampling interval is 100 milliseconds, and the current change slope is calculated by linear regression method; The current change slope is obtained as follows: In the formula, in the formula, represents the current change slope, the unit is ppm / s 2 , and reflects the change trend of the precise positive electrode lattice oxygen escape rate of the last 5 samples. The greater the positive is, the faster the oxygen evolution rate rises, and the more rapidly the overcharge risk grows. The negative is, the oxygen evolution rate shows a downward trend, and the overcharge risk decreases. represents the accurate positive lattice oxygen evolution rate of the i-th sampling, with units of ppm / s, and is specifically the true value of the corrected oxygen evolution rate of each of the last 5 samplings; i is the sampling number (1 to 5) and is a dimensionless number; the summation operation in the numerator and denominator is used to fit the slope by linear regression, reflecting the trend of the oxygen evolution rate.

[0031] The future preset time is set to 1 second, and the positive lattice oxygen evolution rate prediction value in the future preset time is derived by linear extrapolation based on the calculated current change slope and the accurate positive lattice oxygen evolution rate of the last sampling; The future positive lattice oxygen evolution rate prediction value is obtained in the following manner: In the formula, represents the future positive lattice oxygen evolution rate prediction value, with units of ppm / s, and is specifically an estimate of the oxygen evolution rate in the future 1 second, used to judge the overcharge risk in advance. The greater N is, the higher the estimated future overcharge risk is; the smaller N is, the lower the estimated future overcharge risk is; represents the accurate positive lattice oxygen evolution rate of the 5th, i.e., the last sampling, with units of ppm / s; represents the current change slope, with units of ppm / s 2 ; represents the future preset time, which is 1 second, with units of s.

[0032] Specifically, the steps of judging the current and future overcharge risk states are as follows: A current overcharge critical threshold is preset, which is calibrated by an overcharge destructive test: 20 battery samples of the same model and specification as the target lithium battery are selected, and overcharged at 0.5C rate continuously at 25°C normal temperature environment. The positive lattice oxygen evolution rate when the electrolyte of each sample shows obvious decomposition (gas production rate > 1 mL / min) is monitored, and the arithmetic mean of the rate data of the 20 samples is calculated as the current overcharge critical threshold; A future overcharge warning threshold is preset, which is derived by the formula based on the current overcharge critical threshold and the calculated oxygen evolution rate growth rate, i.e., the current change slope, with a warning advance coefficient of 0.8 seconds; The current and future overcharge risk states are judged: if the accurate positive lattice oxygen evolution rate is greater than or equal to the current overcharge critical threshold, it is determined that the current overcharge risk exists; if the future positive lattice oxygen evolution rate prediction value is greater than or equal to the future overcharge warning threshold, it is determined that the future overcharge risk exists; if neither of the above conditions is met, it is determined that no overcharge risk exists.

[0033] Specifically, according to the overcharge risk state judgment result, the logic of executing the charging loop conduction or cut-off operation is as follows: If it is determined that there is a current overcharge risk or a future overcharge risk, the overcharge judgment module outputs a high-level cut-off signal to the overcharge cut-off module, the overcharge cut-off module controls a certain brand of MOSFET drive chip to output a 1.5 ampere drive current, which is synchronously transmitted to the control end of the main loop switch and the redundant magnetic latching relay, the main loop switch is turned off within 50 nanoseconds, the redundant magnetic latching relay is de-energized to open the contact, and the charging main loop is cut off together, and the state detection unit collects the on-off feedback signal and outputs a cut-off completion signal when both are disconnected; If it is determined that there is no overcharge risk, the overcharge judgment module outputs a low-level conduction signal to the overcharge cut-off module, the overcharge cut-off module controls the MOSFET drive chip to output a 0.5 ampere maintenance current, the main loop switch remains on, and the redundant magnetic latching relay is continuously energized to maintain the contact conduction, thereby maintaining the normal conduction of the charging loop together, and the state detection unit collects the conduction feedback signal to ensure the stability of the loop.

[0034] An overcharge monitoring and automatic cut-off method for a lithium battery of a communication device, as shown in the accompanying drawings, comprises the following steps: Figure 2 As shown in the accompanying drawings, the method comprises the following steps: Step 1: Collecting overcharge associated data sets during the charging process of the lithium battery and pre-processing the overcharge associated data sets; Step 2: Extracting and analyzing the features of the pre-processed overcharge associated data sets to obtain overcharge associated feature values; and then based on the overcharge associated feature values, correcting the accuracy of the original collected values of the positive lattice oxygen escape rate to obtain the accurate positive lattice oxygen escape rate; Step 3: Taking the charging voltage change rate as the overcharge driving variable, analyzing the trend of the accurate positive lattice oxygen escape rate and the overcharge driving variable, and obtaining the prediction value of the positive lattice oxygen escape rate in the future preset time; Step 4: Comparing the accurate positive lattice oxygen escape rate and the prediction value of the positive lattice oxygen escape rate with the corresponding preset threshold values, respectively, to determine the current and future overcharge risk states; Step 5: According to the overcharge risk state judgment result, executing the charging loop conduction or cut-off operation.

[0035] It is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting; it is not intended to exclude myriad other embodiments of the present application that other presenters can develop. It is also possible, however, that only a single element can be present. It is further noted that such a term as "comprising" is intended to mean that the embodiments include the recited elements, but not excluding other elements. "Consisting essentially of when used herein in relation to a composition, means that the composition includes the recited elements, and can include additional elements, so long as the additional elements do not materially alter the basic and novel properties of the claimed composition. "Consisting of" when used herein in relation to a composition means that the composition includes the recited elements and nothing more.

[0036] The preferred embodiments of the application disclosed above are only to help explain the principles of the present application. The preferred embodiments do not describe all the details of the present application, nor limit the present application to only the specific embodiments described. It is apparent that many modifications and variations can be made to the present application based on the content of the present disclosure. The present disclosure selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present application, so that those skilled in the art can well understand and utilize the present application. The present application is limited only by the claims and their full scope and equivalents.

Claims

1. An automatic overcharge monitoring and disconnection system for lithium batteries in communication equipment, characterized in that, include: Data acquisition module: used to collect overcharge-related datasets during the lithium battery charging process and to preprocess the overcharge-related datasets; The overcharge correlation dataset includes raw data on the oxygen escape rate from the positive electrode lattice; Data analysis module: used to perform feature extraction and analysis on the preprocessed overfilling association dataset to obtain overfilling association feature values; Based on the overcharge correlation feature value, the original collected value of the positive electrode lattice oxygen escape rate is then corrected to obtain the accurate positive electrode lattice oxygen escape rate. Overcharge prediction module: Used to perform trend analysis on the precise positive electrode lattice oxygen overflow rate and overcharge driving variable with the charging voltage change rate as the overcharge driving variable, and obtain the predicted value of the positive electrode lattice oxygen escape rate within a preset time in the future. Overcharge detection module: used to compare the accurate positive lattice oxygen escape rate and the predicted positive lattice oxygen escape rate with the corresponding preset thresholds to determine the current and future overcharge risk status; Overcharge cut-off module: Used to perform charging circuit connection or disconnection operation based on the overcharge risk status judgment result.

2. The automatic overcharge monitoring and disconnection system for lithium batteries in communication equipment according to claim 1, characterized in that: The overcharge correlation dataset also includes: battery temperature data, charging rate data, sensor drift calibration data, and charging voltage change rate data.

3. The automatic overcharge monitoring and disconnection system for lithium batteries in communication equipment according to claim 1, characterized in that: The preprocessing steps for the overfilled correlated dataset are as follows: For the collected overcharge-related dataset, the effective value range corresponding to each data type is preset based on the physical characteristics of lithium batteries, and abnormal data that exceeds the effective range is removed. The data after the above processing is then subjected to noise reduction using the moving average filtering method to eliminate high-frequency electromagnetic interference signals. The data processed above are converted into dimensionless values ​​based on the initial state of the new battery to complete the preprocessing.

4. The automatic overcharge monitoring and disconnection system for lithium batteries in communication equipment according to claim 1, characterized in that: The steps for feature extraction and analysis based on the preprocessed overfilled association dataset are as follows: Extract real-time values ​​and the rate of change within a preset time period from battery temperature data as temperature influence characteristics; Extract the current value and its ratio to the rated rate from the charging rate data as the rate-affected feature; From the sensor drift calibration data, the cumulative drift amount and the drift value of a single sampling are extracted as sensor drift influence characteristics; From the raw data of oxygen escape rate in the positive electrode lattice, the instantaneous value and the average value within a preset time period are extracted as the raw features of oxygen escape rate. From the charging voltage change rate data, extract the real-time value and the change trend of multiple consecutive samplings as voltage change rate features; By integrating the characteristics of temperature influence, rate influence, sensor drift influence, original oxygen escape rate, and voltage change rate, an overcharge-related characteristic value is formed.

5. The automatic overcharge monitoring and disconnection system for lithium batteries in communication equipment according to claim 1, characterized in that: The steps for refining the accuracy of the original collected values ​​of oxygen escape rate from the positive electrode lattice are as follows: Calculate the temperature influence coefficient based on the temperature influence characteristics in the overcharge correlation eigenvalues; Calculate the ratio influence coefficient based on the ratio influence feature in the overcharge correlation feature value; Based on the sensor drift influence characteristics in the overcharge correlation feature value, the sensor drift influence coefficient is calculated; The temperature influence coefficient, rate influence coefficient, and sensor drift influence coefficient are coupled with the original collected value of the positive electrode lattice oxygen escape rate to obtain the accurate positive electrode lattice oxygen escape rate.

6. The automatic overcharge monitoring and disconnection system for a communication device lithium battery according to claim 1, characterized in that: The specific steps for trend analysis of the oxygen escape rate and overcharge driving variable in the precise cathode lattice are as follows: By combining the current slope of the precise change in the oxygen escape rate of the cathode lattice, the predicted value of the oxygen escape rate of the cathode lattice within a preset time period is derived by linear extrapolation.

7. The automatic overcharge monitoring and disconnection system for lithium batteries in communication equipment according to claim 1, characterized in that: The steps for determining the current and future overcharging risk status are as follows: The current overcharge critical threshold is preset based on the overcharge destructive test calibration, and the future overcharge warning threshold is derived based on the oxygen escape rate growth rate. If the precise measurement of the oxygen escape rate from the positive electrode lattice is greater than or equal to the current overcharge critical threshold, it is determined to be an overcharge risk. If the predicted oxygen escape rate of the positive electrode lattice is greater than or equal to the future overcharge warning threshold, it is determined to be a future overcharge risk. If neither of these conditions is met, it is determined that there is no risk of overcharging.

8. The automatic overcharge monitoring and disconnection system for lithium batteries in communication equipment according to claim 1, characterized in that: The logic for performing the charging circuit on or off operation based on the overcharge risk status judgment result is as follows: If a current or future overcharge risk is identified, the main circuit switch and redundant magnetic latching relay are simultaneously disconnected to execute a charging circuit cutoff operation. If it is determined that there is no risk of overcharging, maintain the main circuit switch and redundant magnetic latching relay in the conducting state to maintain normal charging.

9. A method for automatic overcharge monitoring and disconnection of a lithium battery in a communication device, used to implement the automatic overcharge monitoring and disconnection system for a lithium battery in a communication device as described in any one of claims 1-8, characterized in that: Includes the following steps: Step 1: Collect overcharge correlation dataset during the lithium battery charging process and preprocess the overcharge correlation dataset; the overcharge correlation dataset includes the raw collected data of the oxygen escape rate of the positive electrode lattice; Step 2: Perform feature extraction analysis on the preprocessed overcharge correlation dataset to obtain overcharge correlation feature values; then, based on the overcharge correlation feature values, perform precision correction on the original collected values ​​of positive electrode lattice oxygen escape rate to obtain the accurate positive electrode lattice oxygen escape rate. Step 3: Using the charging voltage change rate as the overcharge driving variable, perform trend analysis on the precise positive electrode lattice oxygen escape rate and the overcharge driving variable to obtain the predicted value of the positive electrode lattice oxygen escape rate within a preset time period. Step 4: Compare the precise positive lattice oxygen escape rate and the predicted positive lattice oxygen escape rate with the corresponding preset thresholds to determine the current and future overcharge risk status. Step 5: Based on the overcharge risk assessment result, perform the charging circuit connection or disconnection operation.

Images