Online monitoring method and system for monitoring system storage battery

Abstract

The invention relates to the technical field of intelligent detection, and discloses an online monitoring method and system for a monitoring system storage battery, and the method comprises the steps: collecting the real-time capacitance and rated capacitance of the storage battery, and determining a pulse current parameter; the method comprises the following steps: acquiring environment temperature data and load data of a storage battery to determine pulse interval time, acquiring static voltage of the storage battery to apply pulse current to the storage battery according to pulse current parameters when a monitoring condition is reached, acquiring voltage data in pulse duration to obtain a voltage steady-state value and a recovery time constant, and finally obtaining the characteristic data value of the storage battery, comparing the characteristic data value with a historical data value, judging whether to carry out early warning on the health condition of the storage battery or not, and adjusting the pulse interval time according to the characteristic data value when judging to carry out early warning on the health condition of the storage battery. According to the invention, the detection accuracy is improved, the potential problems of the storage battery can be found and dealt with in time, and the safety and reliability of the system are improved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent detection technology, and in particular to an online monitoring method and system for batteries in a monitoring system. Background Art

[0002] Tower crane monitoring systems are widely used on construction sites to ensure the safe operation and operation of tower cranes. Tower crane monitoring systems usually rely on batteries as a backup power source to ensure that the system can continue to operate when the main power supply is cut off. Since tower crane monitoring systems work for a long time in complex outdoor environments, the health of the batteries is crucial to the reliability of the system. However, the performance of batteries will gradually decline over time, resulting in reduced system power supply capacity and even the risk of power outages at critical moments.

[0003] Traditional battery monitoring methods usually rely on regular manual inspections or simple voltage detection, which often fail to capture the actual health status of the battery in real time. Manual inspections are limited by the inspection cycle, causing some potential battery failures to be overlooked during the inspection interval. Simple voltage detection is difficult to provide accurate health status assessments under the influence of factors such as load changes and temperature fluctuations, which can easily lead to misjudgments and fail to fully reflect the degree of battery aging, which can easily lead to power failure problems at critical moments.

[0004] Therefore, it is necessary to design an online monitoring method and system for monitoring system batteries to solve the problems existing in the current technology. Summary of the invention

[0005] In view of this, the present invention proposes an online monitoring method and system for batteries in a monitoring system, aiming to solve the problem that the current battery detection in tower crane monitoring systems relies heavily on manpower, is time-consuming and labor-intensive, and has low detection accuracy and poor results.

[0006] In one aspect, the present invention provides an online monitoring method for a battery in a monitoring system, comprising:

[0007] Collecting the real-time capacity and rated capacity of the battery, and determining pulse current parameters according to the rated capacity, wherein the pulse current parameters include pulse current amplitude and pulse duration;

[0008] Collecting ambient temperature data and load data of the battery, determining a pulse interval time according to the real-time capacity, rated capacity, ambient temperature data and load data, and when the ambient temperature data is consistent with the detection start temperature, the load data is less than the preset load data and the pulse interval time is reached, collecting the static voltage of the battery, applying a pulse current to the battery with the pulse current parameter, collecting voltage data within the pulse duration, obtaining a voltage steady-state value according to the voltage data, and fitting the voltage data to obtain a recovery time constant after the pulse duration;

[0009] A characteristic data value of the battery is obtained according to the voltage steady-state value and the recovery time constant, the characteristic data value is compared with the historical data value, and it is determined according to the comparison result whether to issue a warning for the health condition of the battery; when it is determined that a warning is issued for the health condition of the battery, the pulse interval time is adjusted according to the characteristic data value.

[0010] Furthermore, when the pulse current parameter is determined according to the rated capacitance, it includes:

[0011] A capacitance difference is obtained according to the rated capacitance and the real-time capacitance, wherein the capacitance difference is the difference between the rated capacitance and the real-time capacitance, and the pulse current parameter is determined according to the capacitance difference, wherein:

[0012] The pulse current amplitude is inversely proportional to the capacitance difference;

[0013] The pulse duration is proportional to the capacitance difference.

[0014] Further, when the pulse interval is determined according to the real-time capacitance, rated capacitance, yellow static temperature data and load data, the pulse interval is calculated by the following formula:

[0015]

[0016] Where tj represents the pulse interval time, C 0 Indicates real-time capacitance, C r represents the rated capacity, ΔT represents the difference between the ambient temperature and the reference temperature, T0 represents the reference temperature, L represents the load data, and tj0 represents the pulse interval time corresponding to the initial battery.

[0017] Furthermore, fitting the voltage data to obtain a recovery time constant includes:

[0018] The voltage data was fitted with an exponential recovery curve, and the fitting formula was:

[0019] V(t)=Vss+(V0-Vss)·e -t / τ ;

[0020] Among them, V(t) represents the voltage data at time t, Vss represents the steady-state value of the voltage, V0 represents the static voltage, and τ represents the recovery time constant.

[0021] Further, when the characteristic data value of the battery is obtained according to the voltage steady-state value and the recovery time constant, the characteristic data value is calculated by the following formula:

[0022]

[0023] Wherein, D represents the characteristic data value, Vss represents the voltage steady-state value, τ represents the recovery time constant, T0 represents the reference temperature, T represents the current temperature, Req represents the equivalent internal resistance, ΔV represents the instantaneous voltage drop amplitude, γ represents the environmental impact coefficient, and γ takes the value of [0.5, 2]; α and β represent weight coefficients.

[0024] Further, when determining whether to issue an early warning for the health status of the battery according to the comparison result, it includes:

[0025] When the characteristic data value is greater than 1.5 times the minimum characteristic data value in the historical data set, it is determined that an early warning is issued for the health condition of the battery;

[0026] When the characteristic data value is less than or equal to 1.5 times of the minimum characteristic data value in the historical data set, it is determined that no warning is issued for the health status of the battery.

[0027] Further, when it is determined that a warning is issued for the health condition of the battery, it also includes:

[0028] A characteristic data difference is obtained according to the characteristic data value and the minimum characteristic data value, wherein the characteristic data difference is the difference between the characteristic data value and the minimum characteristic data value, and a warning level is determined according to the characteristic data difference, wherein the warning level is proportional to the characteristic data difference.

[0029] Further, when the pulse interval is adjusted according to the characteristic data value, it includes:

[0030] The pulse interval time is adjusted by determining a time adjustment coefficient according to the characteristic data difference.

[0031] Further, when the pulse interval time is adjusted by determining the time adjustment coefficient according to the characteristic data difference, it includes:

[0032] Comparing the characteristic data difference with a first preset data difference and a second preset data difference respectively, and determining an adjustment coefficient according to the comparison result to adjust the pulse interval time, wherein the first preset data difference is smaller than the second preset data difference;

[0033] When the characteristic data difference is less than or equal to the first preset data difference, a first adjustment coefficient is determined to adjust the pulse interval time; when the characteristic data difference is greater than the first preset data difference and less than or equal to the second preset data difference, a second adjustment coefficient is determined to adjust the pulse interval time; when the characteristic data difference is greater than the second preset data difference, a third adjustment coefficient is determined to adjust the pulse interval time, the first adjustment coefficient is greater than the second adjustment coefficient, and the second adjustment coefficient is greater than the third adjustment coefficient.

[0034] Compared with the prior art, the beneficial effects of the present invention are as follows: by collecting the real-time capacity and rated capacity of the battery, the pulse current parameters (including pulse current amplitude and pulse duration) are dynamically determined, thereby improving the accuracy of detection. The influence of ambient temperature and load data is fully considered, and the pulse interval time is determined by these data to ensure that the detection is carried out under appropriate conditions, thereby avoiding misjudgment caused by external factors. The steady-state value and recovery time constant of the voltage data are fitted and analyzed to obtain the characteristic data value of the battery. By comparing with historical data, the degree of battery aging and health status can be effectively judged. Through the early warning mechanism of the detection data and the function of dynamically adjusting the pulse interval time, potential problems of the battery can be discovered and responded to in a timely manner, thereby improving the safety and reliability of the system.

[0035] On the other hand, the present application also provides an online monitoring system for a battery in a monitoring system, which is used to apply the online monitoring method for a battery in a monitoring system, comprising:

[0036] A collection unit is configured to collect the real-time capacity and rated capacity of the battery, and determine the pulse current parameters according to the rated capacity, wherein the pulse current parameters include the pulse current amplitude and the pulse duration;

[0037] a processing unit configured to collect ambient temperature data and load data of the battery, determine a pulse interval time according to the real-time capacity, the rated capacity, the ambient temperature data and the load data, and when the ambient temperature data is consistent with the detection start temperature, the load data is less than the preset load data and the pulse interval time is reached, collect the static voltage of the battery, apply a pulse current to the battery with the pulse current parameter, collect voltage data within the pulse duration, obtain a voltage steady-state value according to the voltage data, and after the pulse duration, fit the voltage data to obtain a recovery time constant;

[0038] The early warning adjustment unit is configured to obtain a characteristic data value of the battery according to the voltage steady-state value and the recovery time constant, compare the characteristic data value with the historical data value, determine whether to issue an early warning for the health status of the battery according to the comparison result, and when it is determined that an early warning is issued for the health status of the battery, adjust the pulse interval time according to the characteristic data value.

[0039] It is understandable that the above-mentioned online monitoring method and system for batteries in the monitoring system have the same beneficial effects, which will not be described in detail here. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0041] Figure 1 A flow chart of an online monitoring method for batteries in a monitoring system provided by an embodiment of the present invention;

[0042] Figure 2 This is a structural block diagram of an online monitoring system for batteries in a monitoring system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0043] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features described in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0044] Tower crane monitoring systems are widely used in construction sites to ensure the safe operation and operation of tower cranes. As an important equipment for high-altitude operations, tower cranes’ monitoring systems usually rely on batteries as a backup power source to ensure that the system can continue to operate stably when the main power supply is cut off. However, tower crane monitoring systems usually work for a long time in complex and changeable outdoor environments, and the health of batteries is crucial to the reliability and safety of the system. As the use time increases, the performance of batteries will inevitably decline gradually, resulting in a weakening of their power supply capacity, and even the risk of power outages at critical moments, which may cause serious safety accidents and economic losses.

[0045] At present, the health monitoring of batteries in tower crane monitoring systems mainly relies on regular manual inspections or simple voltage detection. Traditional monitoring methods have many shortcomings and are difficult to fully guarantee the health status of batteries. Regular manual inspections are limited by the inspection cycle, usually once a day, week or month. This intermittent detection method cannot capture the actual health status of the battery in real time. Once a battery failure or a sharp drop in performance occurs during the inspection cycle, it may not be discovered before the next inspection, causing the battery to fail at a critical moment. Simple voltage detection methods are easily affected by external factors such as ambient temperature and load changes in actual applications, and it is difficult to provide accurate and comprehensive health status assessments. For example, fluctuations in ambient temperature can significantly affect the voltage performance of the battery, while changes in load can cause instantaneous voltage fluctuations. These factors interfere with the voltage detection results, causing misjudgments in the battery health assessment, and failing to effectively reflect the actual aging degree and remaining life of the battery. Traditional methods lack a comprehensive analysis of the complex internal characteristics of the battery, resulting in an insufficient understanding and evaluation of the battery aging process, making it difficult to provide early warning of potential failure risks.

[0046] As the requirements for real-time performance and reliability of tower crane monitoring systems continue to increase, the inadequacies of traditional monitoring methods cannot meet the strict safety requirements of modern engineering. Once the battery fails at a critical moment, it will not only cause the monitoring system to be interrupted and affect the normal operation of the tower crane, but may also cause personnel safety accidents, resulting in huge economic losses and reputation damage. Therefore, it is necessary to design an online monitoring method and system for monitoring system batteries to solve the problems existing in current technology.

[0047] In some embodiments of the present application, see Figure 1 As shown, an online monitoring method for a battery in a monitoring system includes:

[0048] S100: collecting the real-time capacity and rated capacity of the battery, and determining pulse current parameters according to the rated capacity, the pulse current parameters including pulse current amplitude and pulse duration.

[0049] S200: Collect ambient temperature data and battery load data, determine the pulse interval time according to the real-time capacity, rated capacity, ambient temperature data and load data, and when the ambient temperature data is consistent with the detection start temperature, the load data is less than the preset load data and the pulse interval time is reached, collect the static voltage of the battery, apply a pulse current to the battery with a pulse current parameter, collect voltage data within the pulse duration, obtain the voltage steady-state value according to the voltage data, and after the pulse duration, fit the voltage data to obtain the recovery time constant.

[0050] S300: Obtain characteristic data values ​​of the battery according to the voltage steady-state value and the recovery time constant, compare the characteristic data values ​​with the historical data values, determine whether to issue a warning for the health status of the battery according to the comparison result, and when determining whether to issue a warning for the health status of the battery, adjust the pulse interval time according to the characteristic data values.

[0051] Specifically, the real-time capacity and rated capacity of the battery are collected in S100. The real-time capacity reflects the electric energy available for use by the battery under current conditions, while the rated capacity is the maximum energy storage capacity that the battery should have under ideal conditions. By comparing these two parameters, the health status of the battery is preliminarily judged. Based on the rated capacity, the pulse current parameters are obtained, including the amplitude and pulse duration of the pulse current. The pulse current is designed to obtain more accurate battery performance data without damaging the battery. The ambient temperature data and the load data of the battery are further collected in S200. The ambient temperature is an important external factor affecting the performance of the battery. The temperature fluctuation will directly affect the voltage and capacity performance of the battery. The load data reflects the current power output load borne by the battery. The change in load will cause the stress change inside the battery, which will affect its health status. By combining the real-time capacity, rated capacity, ambient temperature data and load data, the pulse interval time, that is, the time interval between each pulse detection, is determined. When the ambient temperature is consistent with the preset detection start temperature, which can be determined according to the temperature value with the highest frequency of occurrence under the current use environment, and the load data is lower than the preset load value and reaches the pulse interval time, the static voltage of the battery is collected. The static voltage is the voltage measured when the battery is lightly loaded, which can reflect the internal state of the battery. According to the pulse current parameters calculated previously, a pulse current is applied to the battery, and voltage data is collected during the pulse duration. By analyzing these voltage data, a voltage steady-state value is obtained, which is the stable voltage value of the battery after the pulse current is applied. The voltage data is fitted to obtain a recovery time constant, which reflects the time required for the battery to recover to a stable state after the pulse. In S300, the characteristic data value of the battery is calculated by the voltage steady-state value and the recovery time constant. This characteristic data value combines the voltage stability and recovery ability of the battery, and can more comprehensively reflect the health status of the battery. The current characteristic data value is compared with the historical data value to determine whether the battery is in a degraded state or is about to fail. When the comparison result shows that the health status of the battery is not good, an early warning signal is issued. At the same time, the pulse interval is dynamically adjusted according to the current characteristic data value to optimize the accuracy and frequency of subsequent monitoring.

[0052] It is understandable that through the dynamic adjustment of pulse current parameters, combined with comprehensive analysis of multiple parameters such as ambient temperature and load data, real-time and accurate monitoring of the health status of the battery is achieved. Compared with traditional monitoring methods, it can not only more accurately assess the aging degree and remaining life of the battery, but also timely warn of potential failure risks, avoiding problems such as interruption of the monitoring system due to battery failure, and improving the reliability and safety of the tower crane monitoring system.

[0053] In some embodiments of the present application, when the pulse current parameter is determined according to the rated capacitance, it includes: obtaining a capacitance difference according to the rated capacitance and the real-time capacitance, the capacitance difference is the difference between the rated capacitance and the real-time capacitance, and determining the pulse current parameter according to the capacitance difference, wherein,

[0054] The pulse current amplitude is inversely proportional to the capacitance difference.

[0055] The pulse duration is proportional to the capacitance difference.

[0056] Specifically, the real-time capacity indicates the total capacity that can be achieved when the battery is fully charged. It can be measured by charging and discharging. By collecting the rated capacity and real-time capacity of the battery, the difference between the two, i.e., the capacity difference, is calculated. The capacity difference reflects the gap between the actual energy storage capacity of the battery and its theoretical maximum energy storage capacity, and reveals the degree of battery degradation to a certain extent. When the capacity difference is large, a smaller pulse current amplitude is selected for testing to avoid applying excessive current pressure to the already degraded battery and prevent further damage to the battery performance. On the contrary, when the capacity difference is small, it indicates that the battery is in a good state, and a larger pulse current amplitude is applied to obtain more detailed voltage response data. When the capacity difference is large, the pulse duration will be extended, giving the battery more time to respond to the pulse current, thereby obtaining more stable voltage data, which is convenient for subsequent analysis and processing. On the contrary, when the capacity difference is small, the pulse duration can be shortened accordingly, because the battery can respond to current changes more quickly in a good state. For example: Assume that the rated capacity of the battery of a tower crane monitoring system is 100Ah, and the current real-time measured capacity is 70Ah. The calculation shows that the capacitance difference is 30Ah. Based on the capacitance difference of 30Ah, a smaller pulse current amplitude, such as 1A, is selected to avoid additional stress on the battery that has been significantly degraded. Due to the large capacitance difference, the pulse duration is set to 5 seconds to ensure that sufficient voltage data can be obtained for steady-state analysis and recovery time constant fitting under long-term current application. If the real-time capacitance is 95Ah and the capacitance difference is only 5Ah, a larger pulse current amplitude of 3A is selected and the pulse duration is set to 2 seconds, because the battery is in a better state at this time and can respond quickly and stably to larger currents.

[0057] It can be understood that by dynamically adjusting the pulse current parameters according to the capacitance difference, the detection accuracy and battery safety are effectively balanced, avoiding further damage to the battery due to unreasonable current application. At the same time, according to the actual status of different batteries, the pulse current amplitude and duration are flexibly adjusted to make the detection results more accurate and reliable, which helps to make a more comprehensive and real-time assessment of the health of the battery.

[0058] In some embodiments of the present application, when the pulse interval time is determined according to the real-time capacitance, the rated capacitance, the yellow static temperature data and the load data, the pulse interval time is calculated by the following formula:

[0059]

[0060] Where tj represents the pulse interval time, C 0 Indicates real-time capacitance, C r represents the rated capacity, ΔT represents the difference between the ambient temperature and the reference temperature, T0 represents the reference temperature, L represents the load data, and tj0 represents the pulse interval time corresponding to the initial battery.

[0061] It is understandable that the initial pulse interval is a reference value, which is set according to the initial state of the battery and represents the pulse interval under ideal conditions. Assume that the initial pulse interval of the battery of a tower crane monitoring system is 720 hours. The current real-time capacity is 80Ah, the rated capacity is 100Ah, the ambient temperature difference is 5°C, the reference temperature is 25°C, and the load data is 0.5. The final calculated pulse interval tj = 0.72478 × 1.004 × 1.05 × 720 ≈ 550 hours.

[0062] It is understandable that by comprehensively considering the battery capacity, ambient temperature and load conditions, the pulse interval time is dynamically adjusted, which effectively improves the accuracy and real-time performance of battery health status monitoring. Adapting to changes in external conditions ensures that the detection system can provide the optimal pulse application time under different working conditions, avoiding detection errors or battery damage caused by fixed pulse interval time, thereby extending the battery life and ensuring the safe operation of the tower crane monitoring system.

[0063] In some embodiments of the present application, fitting the voltage data to obtain the recovery time constant includes:

[0064] The voltage data is fitted with an exponential recovery curve, and the fitting formula is:

[0065] V(t)=Vss+(V0-Vss)·e -t / τ ;

[0066] Among them, V(t) represents the voltage data at time t, Vss represents the steady-state value of the voltage, V0 represents the static voltage, and τ represents the recovery time constant.

[0067] It can be understood that, for example, during a certain online monitoring process, after the pulse current is applied to the battery of the tower crane monitoring system, its voltage data at different time points are recorded as follows: at t = 0 seconds, V(0) = = 12.5 volts; at t = 10 seconds, V(10) = 12.2 volts; at t = 30 seconds, V(30) = 12.1 volts; at t = 60 seconds, V(60) = 12.05 volts; at t = 120 seconds, V(120) = 12.02 volts; exponential fitting of these data can obtain a voltage steady-state value Vss = = 12.0 volts, a static voltage V0 = = 12.5 volts, and a recovery time constant τ of about 40 seconds. This means that after about 40 seconds, the voltage will recover to a value close to its steady-state value. This embodiment effectively captures the dynamic characteristics of the battery under actual working conditions, helps the system identify potential battery aging or failure conditions earlier, and thus improves the overall safety and reliability of the tower crane monitoring system.

[0068] In some embodiments of the present application, when obtaining the characteristic data value of the battery according to the voltage steady-state value and the recovery time constant, the characteristic data value is calculated by the following formula:

[0069]

[0070] Wherein, D represents the characteristic data value, Vss represents the voltage steady-state value, τ represents the recovery time constant, T0 represents the reference temperature, T represents the current temperature, Req represents the equivalent internal resistance, ΔV represents the instantaneous voltage drop amplitude, γ represents the environmental impact coefficient, and γ takes the value of [0.5, 2]; α and β represent weight coefficients.

[0071] In some embodiments of the present application, when determining whether to issue a warning for the health status of the battery according to the comparison result, it includes: when the characteristic data value is greater than 1.5 times the minimum characteristic data value in the historical data set, it is determined that a warning is issued for the health status of the battery. When the characteristic data value is less than or equal to 1.5 times the minimum characteristic data value in the historical data set, it is determined that no warning is issued for the health status of the battery.

[0072] In some embodiments of the present application, when determining to issue a warning for the health status of a battery, it also includes: obtaining a characteristic data difference based on the characteristic data value and the minimum characteristic data value, the characteristic data difference being the difference between the characteristic data value and the minimum characteristic data value, determining a warning level based on the characteristic data difference, and the warning level is proportional to the characteristic data difference.

[0073] It is understandable that the steady-state voltage value Vss of the battery is 12V, the recovery time constant is 40 seconds, the reference temperature T0 is 25°C, the current temperature T is 5°C, the equivalent internal resistance Req is 0.04Ω, the instantaneous voltage drop amplitude ΔV is 0.6V, and the environmental impact coefficient γ is 1.5. The weight coefficient α is preferably 0.07, and β is preferably 30 for calculation: D≈16.8+7.2≈24. The comprehensive calculation method based on multiple factors can more keenly capture the slight changes in the battery, and improve the early warning accuracy and response speed of the monitoring system. Through historical data comparison, it is judged in real time whether a health warning is needed. And when dealing with risks of different degrees, the hierarchical early warning mechanism makes the processing more flexible. The larger the difference in characteristic data, the higher the degree of battery aging, and the higher the early warning level, the higher the degree of battery risk.

[0074] In some embodiments of the present application, when the pulse interval time is adjusted according to the characteristic data value, it includes: determining a time adjustment coefficient according to the characteristic data difference value to adjust the pulse interval time.

[0075] Specifically, the characteristic data difference is compared with the first preset data difference and the second preset data difference respectively, and the adjustment coefficient is determined to adjust the pulse interval time according to the comparison result, and the first preset data difference is less than the second preset data difference. When the characteristic data difference is less than or equal to the first preset data difference, the first adjustment coefficient is determined to adjust the pulse interval time. When the characteristic data difference is greater than the first preset data difference and less than or equal to the second preset data difference, the second adjustment coefficient is determined to adjust the pulse interval time. When the characteristic data difference is greater than the second preset data difference, the third adjustment coefficient is determined to adjust the pulse interval time. The first adjustment coefficient is greater than the second adjustment coefficient, and the second adjustment coefficient is greater than the third adjustment coefficient.

[0076] It can be understood that, assuming that the current characteristic data value D is 42, the minimum characteristic data value Dmin in the historical data set is 20. Therefore, the characteristic data difference is 22. The preset first and second data differences are 10 and 20, respectively. The adjustment coefficients are k1==0.9, k2=0.7, and k3=0.6, respectively. 22>20, that is, the third adjustment coefficient 0.6 is selected to adjust the pulse interval time of 550 hours, and the adjusted pulse interval time is 330 hours.

[0077] It is understandable that different adjustment coefficients are applied according to different feature data difference ranges so that the pulse interval time can accurately match the current battery health status. Effectively respond to different degrees of battery aging and environmental changes to ensure the accuracy and timeliness of monitoring data. By setting multiple adjustment coefficients, the battery health status can be quickly responded to according to the actual test results, so as to adjust the monitoring strategy in real time and reduce the potential risks caused by battery performance degradation.

[0078] In the above embodiment, the real-time capacity and rated capacity of the battery are collected to dynamically determine the pulse current parameters (including pulse current amplitude and pulse duration), thereby improving the accuracy of detection. The influence of ambient temperature and load data is fully considered, and the pulse interval time is determined by these data to ensure that the detection is carried out under appropriate conditions, thereby avoiding misjudgment caused by external factors. The steady-state value and recovery time constant of the voltage data are fitted and analyzed to obtain the characteristic data value of the battery. By comparing with historical data, the degree of battery aging and health status can be effectively judged. Through the early warning mechanism of the detection data and the function of dynamically adjusting the pulse interval time, potential problems of the battery can be discovered and responded to in a timely manner, thereby improving the safety and reliability of the system.

[0079] In another preferred embodiment based on the above embodiment, refer to Figure 2 As shown, this embodiment provides an online monitoring system for batteries in a monitoring system, which is used to apply the online monitoring method for batteries in a monitoring system, including:

[0080] The collecting unit is configured to collect the real-time capacity and the rated capacity of the battery, and determine the pulse current parameters according to the rated capacity, wherein the pulse current parameters include the pulse current amplitude and the pulse duration;

[0081] The processing unit is configured to collect ambient temperature data and load data of the battery, determine the pulse interval time according to the real-time capacity, the rated capacity, the ambient temperature data and the load data, and when the ambient temperature data is consistent with the detection start temperature, the load data is less than the preset load data and the pulse interval time is reached, collect the static voltage of the battery, apply a pulse current to the battery with a pulse current parameter, collect voltage data within the pulse duration, obtain a voltage steady-state value according to the voltage data, and after the pulse duration, fit the voltage data to obtain a recovery time constant;

[0082] The early warning adjustment unit is configured to obtain a characteristic data value of the battery based on a voltage steady-state value and a recovery time constant, compare the characteristic data value with a historical data value, determine whether to issue an early warning for the health status of the battery based on the comparison result, and when determining whether to issue an early warning for the health status of the battery, adjust the pulse interval time based on the characteristic data value.

[0083] It is understandable that by collecting the real-time capacity and rated capacity of the battery and dynamically determining the pulse current parameters (including pulse current amplitude and pulse duration), the accuracy of detection is improved. The influence of ambient temperature and load data is fully considered, and the pulse interval time is determined by these data to ensure that the detection is carried out under appropriate conditions, thereby avoiding misjudgment caused by external factors. The steady-state value and recovery time constant of the voltage data are fitted and analyzed to obtain the characteristic data value of the battery. By comparing with historical data, the degree of battery aging and health status can be effectively judged. Through the early warning mechanism of the detection data and the function of dynamically adjusting the pulse interval time, potential battery problems can be discovered and responded to in a timely manner, improving the safety and reliability of the system.

[0084] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0085] The present application is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0086] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0087] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0088] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. An online monitoring method for a battery in a monitoring system, characterized in that: include: Collecting the real-time capacity and rated capacity of the battery, and determining pulse current parameters according to the rated capacity, wherein the pulse current parameters include pulse current amplitude and pulse duration; Collecting ambient temperature data and load data of the battery, determining a pulse interval time according to the real-time capacity, rated capacity, ambient temperature data and load data, and when the ambient temperature data is consistent with the detection start temperature, the load data is less than the preset load data and the pulse interval time is reached, collecting the static voltage of the battery, applying a pulse current to the battery with the pulse current parameter, collecting voltage data within the pulse duration, obtaining a voltage steady-state value according to the voltage data, and fitting the voltage data to obtain a recovery time constant after the pulse duration; A characteristic data value of the battery is obtained according to the voltage steady-state value and the recovery time constant, the characteristic data value is compared with the historical data value, and it is determined according to the comparison result whether to issue a warning for the health condition of the battery; when it is determined that a warning is issued for the health condition of the battery, the pulse interval time is adjusted according to the characteristic data value.

2. The online monitoring method for batteries in a monitoring system according to claim 1, characterized in that: When the pulse current parameters are determined according to the rated capacitance, it includes: A capacitance difference is obtained according to the rated capacitance and the real-time capacitance, wherein the capacitance difference is the difference between the rated capacitance and the real-time capacitance, and the pulse current parameter is determined according to the capacitance difference, wherein: The pulse current amplitude is inversely proportional to the capacitance difference; The pulse duration is proportional to the capacitance difference.

3. The online monitoring method for batteries in a monitoring system according to claim 1, characterized in that: When the pulse interval is determined according to the real-time capacitance, rated capacitance, yellow static temperature data and load data, the pulse interval is calculated by the following formula: Where tj represents the pulse interval time, C0 represents the real-time capacitance, C r represents the rated capacity, ΔT represents the difference between the ambient temperature and the reference temperature, T0 represents the reference temperature, L represents the load data, and tj0 represents the pulse interval time corresponding to the initial battery.

4. The online monitoring method for batteries in a monitoring system according to claim 1, characterized in that: When fitting the voltage data to obtain the recovery time constant, it includes: The voltage data was fitted with an exponential recovery curve, and the fitting formula was: V(t)=Vss+(V0-Vss)·e -t / τ ; Among them, V(t) represents the voltage data at time t, Vss represents the steady-state value of the voltage, V0 represents the static voltage, and τ represents the recovery time constant.

5. The online monitoring method for batteries in a monitoring system according to claim 1, characterized in that: When the characteristic data value of the battery is obtained according to the voltage steady-state value and the recovery time constant, the characteristic data value is calculated by the following formula: Wherein, D represents the characteristic data value, Vss represents the voltage steady-state value, τ represents the recovery time constant, T0 represents the reference temperature, T represents the current temperature, Req represents the equivalent internal resistance, ΔV represents the instantaneous voltage drop amplitude, γ represents the environmental impact coefficient, and γ takes the value of [0.5, 2]; α and β represent weight coefficients.

6. The online monitoring method for batteries in a monitoring system according to claim 1, characterized in that: When determining whether to issue an early warning for the health condition of the battery according to the comparison result, the method includes: When the characteristic data value is greater than 1.5 times the minimum characteristic data value in the historical data set, it is determined that an early warning is issued for the health condition of the battery; When the characteristic data value is less than or equal to 1.5 times of the minimum characteristic data value in the historical data set, it is determined that no warning is issued for the health status of the battery.

7. The online monitoring method for batteries in a monitoring system according to claim 6, characterized in that: When determining to give an early warning for the health condition of the battery, the method further includes: A characteristic data difference is obtained according to the characteristic data value and the minimum characteristic data value, wherein the characteristic data difference is the difference between the characteristic data value and the minimum characteristic data value, and a warning level is determined according to the characteristic data difference, wherein the warning level is proportional to the characteristic data difference.

8. The online monitoring method for storage batteries in a monitoring system according to claim 7, characterized in that: When the pulse interval time is adjusted according to the characteristic data value, it includes: The pulse interval time is adjusted by determining a time adjustment coefficient according to the characteristic data difference.

9. The online monitoring method for storage batteries in a monitoring system according to claim 8, characterized in that: When the time adjustment coefficient is determined according to the characteristic data difference to adjust the pulse interval time, it includes: Comparing the characteristic data difference with a first preset data difference and a second preset data difference respectively, and determining an adjustment coefficient according to the comparison result to adjust the pulse interval time, wherein the first preset data difference is smaller than the second preset data difference; When the characteristic data difference is less than or equal to the first preset data difference, a first adjustment coefficient is determined to adjust the pulse interval time; when the characteristic data difference is greater than the first preset data difference and less than or equal to the second preset data difference, a second adjustment coefficient is determined to adjust the pulse interval time; when the characteristic data difference is greater than the second preset data difference, a third adjustment coefficient is determined to adjust the pulse interval time, the first adjustment coefficient is greater than the second adjustment coefficient, and the second adjustment coefficient is greater than the third adjustment coefficient.

10. An online monitoring system for a storage battery in a monitoring system, used for applying the online monitoring method for a storage battery in a monitoring system as claimed in any one of claims 1 to 9, characterized in that: include: A collection unit is configured to collect the real-time capacity and rated capacity of the battery, and determine the pulse current parameters according to the rated capacity, wherein the pulse current parameters include the pulse current amplitude and the pulse duration; a processing unit configured to collect ambient temperature data and load data of the battery, determine a pulse interval time according to the real-time capacity, the rated capacity, the ambient temperature data and the load data, and when the ambient temperature data is consistent with the detection start temperature, the load data is less than the preset load data and the pulse interval time is reached, collect the static voltage of the battery, apply a pulse current to the battery with the pulse current parameter, collect voltage data within the pulse duration, obtain a voltage steady-state value according to the voltage data, and after the pulse duration, fit the voltage data to obtain a recovery time constant; The early warning adjustment unit is configured to obtain a characteristic data value of the battery according to the voltage steady-state value and the recovery time constant, compare the characteristic data value with the historical data value, determine whether to issue an early warning for the health status of the battery according to the comparison result, and when it is determined that an early warning is issued for the health status of the battery, adjust the pulse interval time according to the characteristic data value.

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