Intelligent detection method and system based on lithium battery equipment

Abstract

The invention discloses an intelligent detection method and system based on lithium battery equipment, and relates to the technical field of intelligent detection. According to the intelligent detection method based on the lithium battery equipment, operation test data of the equipment in high-load, low-load, charging and discharging states are acquired and preprocessed, various data are comprehensively analyzed to obtain health state indexes, the indexes are comprehensively analyzed, and the comprehensive health state indexes of the equipment are obtained through calculation; and comparing with a preset evaluation interval. And when the index exceeds a preset interval, the system automatically marks that the equipment operates abnormally, and triggers an alarm. According to the invention, the operation data of multiple scenes are comprehensively analyzed in combination with the working parameters of the equipment; therefore, the health condition of the equipment can be evaluated more comprehensively, performance degradation of the battery or the motor under different working conditions can be found in time through multi-scene and multi-parameter analysis, and higher equipment reliability and longer service life are ensured.

Description

Technical Field

[0001] The present invention relates to the field of intelligent detection technology, and in particular to an intelligent detection method and system based on lithium battery equipment. Background Art

[0002] With the continuous development of lithium battery technology, lithium batteries have been widely used in various devices, such as electric vehicles, energy storage systems, smart devices, etc. Lithium batteries have become an important choice for modern energy storage due to their high energy density, long cycle life and light weight. However, with the increase of usage time and changes in the working environment, the performance of lithium batteries will gradually decline, which may lead to a decrease in battery capacity, reduced charging and discharging efficiency, and even battery overheating, expansion or failure and other safety issues. Therefore, real-time monitoring and analysis of the health status of lithium battery equipment has become the key to ensure its efficient and safe operation.

[0003] The limitations of the existing technology include at least the following problems. Currently, most health monitoring methods for lithium battery equipment can usually only evaluate the performance of the equipment under a single load state, ignoring the dynamic performance of the equipment under multiple working conditions, such as high load, low load, charging and discharging states. This limitation makes it difficult to accurately evaluate the health status of batteries and motors in actual use, especially the performance fluctuations that may occur when the equipment undergoes different loads and charging and discharging cycles. Due to the lack of comprehensive monitoring in multiple scenarios and multiple states, the existing technology is difficult to detect potential failures or performance degradation trends in advance, resulting in untimely equipment maintenance and fault warnings, increasing equipment downtime and maintenance costs, and reducing equipment utilization efficiency and economic benefits. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention provides an intelligent detection method and system based on lithium battery equipment, which solves the problem in the prior art that it is difficult to comprehensively evaluate the health status of lithium battery equipment under various working conditions, thereby affecting the maintenance of the equipment and delaying early warning.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: an intelligent detection method based on lithium battery equipment, comprising the following steps: acquiring and preprocessing operation test data of lithium battery equipment, the operation test data including high-load operation test data, low-load operation test data, charging operation test data, and discharging operation test data; performing comprehensive analysis on the preprocessed operation test data of lithium battery equipment to obtain an operation health status set of lithium battery equipment, the operation health status set including a high-load operation health index, a low-load operation health index, a charging operation health efficiency index, and a discharging operation health efficiency index; performing comprehensive analysis on the operation health status set of lithium battery equipment to obtain a comprehensive health status index of the lithium battery equipment, and performing judgment analysis with a preset comprehensive health assessment interval; when the comprehensive health status index of the lithium battery equipment is outside the preset comprehensive health assessment interval, marking the lithium battery equipment as an operation abnormality, and sending an operation abnormality alarm.

[0006] Furthermore, the specific formula for calculating the comprehensive health status index of lithium battery equipment is as follows: ;in, It is the comprehensive health status index of lithium battery equipment. It is the high load operation health index of lithium battery equipment. The high load operation impact coefficient stored in the database. It is the low-load operation health index of lithium battery equipment. is the low load operation impact coefficient stored in the database. It is the charging and operating health efficiency index of lithium battery equipment. is the charging operation influence coefficient stored in the database, It is the discharge operation health efficiency index of lithium battery equipment. is the discharge operation influence coefficient stored in the database, is a natural constant.

[0007] Furthermore, the high-load operation test data includes a high-load battery output current value, a high-load battery operating voltage value, a high-load battery internal resistance value, a high-load battery operating temperature value, a high-load motor output power, and a high-load motor speed value; the low-load operation test data includes a low-load battery output current value, a low-load battery operating voltage value, a low-load battery internal resistance value, a low-load battery operating temperature value, and a low-load motor output power; the charging operation test data includes a battery charging efficiency value, a battery charging temperature value, a battery charging current value, and a battery charging voltage value; and the discharging operation test data includes a battery discharge temperature value, a battery discharge current value, and a battery discharge voltage value.

[0008] Furthermore, the specific steps for obtaining the high-load operation health index of the lithium battery equipment are as follows: obtain the high-load operation parameter data of the lithium battery equipment, the high-load operation parameter data including the high-load battery maximum output current value, the battery operation nominal voltage value, the battery initial internal resistance value, the battery operation maximum temperature value, the high-load motor maximum output power, and the high-load motor maximum speed value; conduct a comprehensive analysis of the high-load operation parameter data of the lithium battery equipment in combination with the high-load operation test data of the lithium battery equipment to obtain the high-load operation health index of the lithium battery equipment.

[0009] Furthermore, the specific formula for calculating the high-load operation health index of lithium battery equipment is as follows: ;in, It is the high load operation health index of lithium battery equipment. The high-load battery output current value for lithium-ion devices. The maximum output current value of the high-load battery of the lithium battery device. is the high load current influence coefficient stored in the database, It is the high load battery operating voltage value of lithium battery equipment. It is the nominal voltage value of the battery of lithium battery equipment. is the high load voltage influence coefficient stored in the database, is the high-load battery internal resistance of lithium battery equipment, is the initial internal resistance of the battery of the lithium battery device, is the high load internal resistance influence coefficient stored in the database, It is the high load battery operating temperature value of lithium battery equipment. The maximum temperature that the battery of lithium battery equipment can withstand. is the high load temperature influence coefficient stored in the database, Output power for high-load motors of lithium battery equipment. The maximum output power of the high-load motor of the lithium battery equipment. is the high-load motor output power influence coefficient stored in the database, is the high-load motor speed value of the lithium battery device, The maximum speed of the high-load motor of the lithium battery device. It is the high load motor speed influence coefficient stored in the database.

[0010] Furthermore, the specific steps for obtaining the low-load operation health index of the lithium battery device are as follows: obtain the low-load operation parameter data of the lithium battery device, the low-load operation parameter data including the battery output nominal current value, the ambient temperature value in the set area, and the low-load motor maximum output power; read the battery operation nominal voltage value and the battery initial internal resistance value of the lithium battery device, and conduct a comprehensive analysis based on the low-load operation test data and the low-load operation parameter data of the lithium battery device to obtain the low-load operation health index of the lithium battery device.

[0011] Furthermore, the specific steps for obtaining the charging operation health efficiency index of the lithium battery equipment are as follows: obtaining the charging operation parameter data of the lithium battery equipment, the charging operation parameter data including the battery charging parameter temperature value, the battery maximum charging current value, and the battery charging nominal voltage value; comprehensively analyzing the charging operation parameter data of the lithium battery equipment in combination with the charging operation test data of the lithium battery equipment to obtain the charging operation health efficiency index of the lithium battery equipment.

[0012] Furthermore, the specific formula for calculating the charging operation health efficiency index of lithium battery equipment is as follows: ;in, It is the charging and operating health efficiency index of lithium battery equipment. is the battery charging efficiency value of the lithium battery device, is a natural constant, It is the battery charging temperature value of lithium battery equipment. Set the temperature value for charging the battery of lithium battery equipment. is the charging temperature influence coefficient stored in the database, It is the battery charging current value of the lithium battery device. It is the maximum charging current value of the battery of the lithium battery device. is the charging current influence coefficient stored in the database, It is the battery charging voltage value of the lithium battery device. The nominal voltage value for charging the battery of lithium battery equipment. It is the charging voltage influence coefficient stored in the database.

[0013] Furthermore, the specific steps for obtaining the discharge operation health efficiency index of the lithium battery equipment are as follows: obtain the discharge operation parameter data of the lithium battery equipment, the discharge operation parameter data including the battery discharge parameter temperature value, the battery discharge nominal current value, and the battery discharge nominal voltage value; conduct a comprehensive analysis on the discharge operation parameter data of the lithium battery equipment in combination with the discharge operation test data of the lithium battery equipment to obtain the discharge operation health efficiency index of the lithium battery equipment.

[0014] An intelligent detection system based on lithium battery equipment includes: an operation test data acquisition module, which is used to acquire and pre-process the operation test data of the lithium battery equipment, wherein the operation test data includes high-load operation test data, low-load operation test data, charging operation test data, and discharging operation test data; an operation test data analysis module, which is used to comprehensively analyze the pre-processed operation test data of the lithium battery equipment to obtain an operation health status set of the lithium battery equipment, wherein the operation health status set includes a high-load operation health index, a low-load operation health index, a charging operation health efficiency index, and a discharging operation health efficiency index; a comprehensive health analysis module, which is used to comprehensively analyze the operation health status set of the lithium battery equipment to obtain a comprehensive health status index of the lithium battery equipment; and a comprehensive health judgment module, which is used to judge and analyze the comprehensive health status index of the lithium battery equipment with a preset comprehensive health assessment interval, and when the comprehensive health status index of the lithium battery equipment is outside the preset comprehensive health assessment interval, the lithium battery equipment is marked as abnormal operation and an abnormal operation alarm is sent.

[0015] The present invention has the following beneficial effects: (1) This intelligent detection method based on lithium battery equipment can provide a more accurate health status assessment than traditional methods by comprehensively analyzing the operating data of multiple scenarios, such as high load, low load, charging and discharging status. In the existing technology, many detection methods usually only focus on a certain working state, such as battery performance under charging or high load conditions, which easily leads to potential problems of the equipment in other working states being ignored. By collecting test data under multiple load conditions and combining the equipment's operating parameters, such as current, voltage, internal resistance, temperature, etc., a more comprehensive assessment of the equipment's health status can be made. This multi-scenario, multi-parameter analysis can promptly detect the performance degradation of the battery or motor under different working conditions, ensuring higher equipment reliability and longer service life.

[0016] (2) This intelligent detection method based on lithium battery equipment can provide an in-depth understanding of the performance of the equipment under different working conditions through comprehensive analysis of operating data and health status index based on multiple scenarios, thereby providing data support for equipment optimization. For example, the charging efficiency, discharging efficiency, and the impact of load changes on battery health can all be accurately measured and fed back through the intelligent system. Since the charging and discharging processes have a great impact on battery life and efficiency, real-time monitoring of these parameters can significantly improve the battery charging efficiency and discharging efficiency, thereby reducing energy consumption and improving the overall operating efficiency of the equipment.

[0017] (3) This intelligent detection method based on lithium battery equipment can automatically trigger an abnormal operation alarm when the comprehensive health status index of the equipment exceeds the preset health assessment range, thereby solving the lag problem of equipment health monitoring in the existing technology. The existing technology usually relies on manual regular inspections and single parameter monitoring, which makes it difficult to timely detect potential abnormal conditions of the equipment, especially when the battery temperature is too high or the load changes greatly during charging and discharging. This method can detect equipment health changes in advance and issue alarms in time by collecting and analyzing data from different operating states, such as high load, low load, charging, and discharging in real time, thereby avoiding the equipment being in a faulty state for a long time and reducing equipment downtime and maintenance costs.

[0018] (4) The intelligent detection system based on lithium battery equipment can effectively improve the equipment failure prediction and prevention capabilities through multi-dimensional data analysis of lithium battery equipment under different working conditions, including high load, low load, charging and discharging conditions. By running the test data acquisition module and the test data analysis module, it can collect and pre-process the key data of the equipment under various working modes in real time, and analyze the health status under different working conditions through the comprehensive health analysis module to form a comprehensive comprehensive health status index. When the index is abnormal, the system can automatically determine whether the equipment is in an abnormal state according to the preset health assessment interval, and send an alarm through the comprehensive health judgment module. This real-time and accurate fault prediction and early warning mechanism can detect potential faults in advance and reduce equipment downtime, thereby effectively extending the service life of the equipment and avoiding high repair costs. In addition, through continuous health monitoring, equipment operators can implement targeted maintenance and repair measures based on the system's early warning information to minimize the impact of equipment failures on production and operations.

[0019] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 This is a flow chart of an intelligent detection method based on lithium battery equipment in the present invention.

[0021] Figure 2 The present invention is a flowchart of the specific steps of obtaining the low-load operation health index of a lithium battery device in an intelligent detection method based on a lithium battery device.

[0022] Figure 3 This is a block diagram of an intelligent detection system based on lithium battery equipment in the present invention. DETAILED DESCRIPTION

[0023] See also Figure 1The embodiment of the present invention provides a technical solution: an intelligent detection method based on lithium battery equipment, comprising the following steps: acquiring and preprocessing operation test data of the lithium battery equipment, the operation test data including high-load operation test data, low-load operation test data, charging operation test data, and discharging operation test data; performing comprehensive analysis on the preprocessed operation test data of the lithium battery equipment to obtain an operation health status set of the lithium battery equipment, the operation health status set including a high-load operation health index, a low-load operation health index, a charging operation health efficiency index, and a discharging operation health efficiency index; performing comprehensive analysis on the operation health status set of the lithium battery equipment to obtain a comprehensive health status index of the lithium battery equipment, and performing judgment analysis with a preset comprehensive health assessment interval; when the comprehensive health status index of the lithium battery equipment is outside the preset comprehensive health assessment interval, the lithium battery equipment is marked as operating abnormally, and an operation abnormality alarm is sent to relevant staff.

[0024] Among them, the specific formula for calculating the comprehensive health status index of lithium battery equipment is as follows: ;in, It is the comprehensive health status index of lithium battery equipment. It is the high load operation health index of lithium battery equipment. The high load operation impact coefficient stored in the database. It is the low-load operation health index of lithium battery equipment. is the low load operation impact coefficient stored in the database. It is the charging and operating health efficiency index of lithium battery equipment. is the charging operation influence coefficient stored in the database, It is the discharge operation health efficiency index of lithium battery equipment. is the discharge operation influence coefficient stored in the database, is a natural constant and in this embodiment has a value of 2.71.

[0025] It should be explained that the high load operation impact coefficient stored in the database , Low load operation influence coefficient , Charging operation influence coefficient , Discharge operation influence coefficient The specific acquisition steps are as follows: under different working conditions of the equipment, the influence coefficient is calculated through real-time data collection and storage. For the high-load operation influence coefficient, when the equipment is in a high-load state, the data acquisition system collects battery current, voltage, temperature, power and other data, which are stored in the database. Subsequently, the influence of each parameter on battery health is analyzed through regression analysis or statistical modeling methods, and finally the coefficient of variation of battery health under high-load conditions is obtained, which reflects the impact of temperature increase, internal resistance increase and other problems on battery health; for the low-load operation influence coefficient, when the equipment is in a low-load state, the acquisition system also collects battery current, voltage, power output and temperature data, and stores them in the database. Through time series analysis The data acquisition system processes the stored data and calculates the stability coefficient of battery health under low load conditions, reflecting the changes in equipment health under low load. For the charging operation influence coefficient, when the equipment is charging, the data acquisition system records the battery current, charging voltage, charging time, battery temperature and other data in real time. These data are stored in the database, and the influence coefficient is calculated through charging efficiency and temperature change analysis to evaluate the impact of the charging process on battery health, especially the changes in temperature and charging efficiency. For the discharge operation influence coefficient, when the equipment is discharging, the acquisition system records the discharge current, voltage, power and temperature and other data, and calculates the impact of the discharge process on battery health through regression analysis and other methods, especially the impact of discharge efficiency and temperature changes on equipment health.

[0026] The high-load operation test data includes high-load battery output current value, high-load battery operation voltage value, high-load battery internal resistance value, high-load battery operation temperature value, high-load motor output power, and high-load motor speed value. The low-load operation test data includes low-load battery output current value, low-load battery operation voltage value, low-load battery internal resistance value, low-load battery operation temperature value, and low-load motor output power. The charging operation test data includes battery charging efficiency value, battery charging temperature value, battery charging current value, and battery charging voltage value. The discharge operation test data includes the battery discharge temperature value, the battery discharge current value, and the battery discharge voltage value.

[0027] Among them, the high-load battery output current value, that is, the current value actually output by the battery under high-load conditions, represents the current intensity provided by the battery to the device, and can be measured and obtained by a current sensor (such as a Hall effect sensor).

[0028] The high-load battery operating voltage value, that is, the output voltage of the battery under high load, indicates the working voltage of the battery under high load, and can be measured and obtained by a voltage sensor.

[0029] The high-load battery internal resistance value, that is, the internal resistance of the battery under high-load conditions, the increase in internal resistance may indicate a decline in battery health, which can be measured using an internal resistance meter.

[0030] The high-load battery operating temperature value, that is, the operating temperature of the battery under high load. Temperature changes can affect the performance and life of the battery, and can be measured and obtained through a temperature sensor (such as a thermocouple or RTD sensor).

[0031] High-load motor output power, that is, the output power of the motor under high load, indicates the working efficiency of the motor and can be measured by a power sensor (such as a power meter).

[0032] The high-load motor speed value, that is, the speed of the motor under high load, is usually measured in revolutions per minute (RPM), reflecting the working speed of the motor. It can be measured and obtained through a speed sensor (such as a photoelectric encoder or a magnetic sensor).

[0033] The low-load battery output current value, that is, under the low-load state, the current output by the battery is small, indicating the working state of the battery under low load, can be measured and obtained by a current sensor.

[0034] The low-load battery operating voltage value, that is, the output voltage value of the battery under low load, is usually relatively stable, reflecting the working voltage of the battery, and can be measured and obtained by a voltage sensor.

[0035] The low-load battery internal resistance value, that is, the internal resistance value of the battery under low load. Under low load conditions, the internal resistance usually changes little and can be measured using an internal resistance tester.

[0036] The low-load battery operating temperature value, that is, the operating temperature of the battery under low-load conditions. The battery temperature changes little under low load conditions and can be measured and obtained through a temperature sensor.

[0037] Low-load motor output power, that is, the output power of the motor under low load, is usually small and reflects the working efficiency of the motor. It can be measured and obtained by a power sensor.

[0038] The battery charging efficiency value, that is, the efficiency of the battery charging process, is usually the ratio of the electric energy obtained by the battery from the external power source to the electric energy actually stored in the battery, which can be calculated by a power meter and a current and voltage sensor.

[0039] The battery charging temperature value, that is, the temperature change of the battery during the charging process. Too high charging temperature may affect the battery life. It can be measured and obtained through a temperature sensor.

[0040] The battery charging current value, that is, the current value flowing into the battery during the charging process, indicates the charging rate and can be measured and obtained by a current sensor.

[0041] The battery charging voltage value, that is, the voltage of the battery during the charging process, is usually higher than the nominal voltage of the battery and can be measured and obtained by a voltage sensor.

[0042] The battery discharge temperature value, that is, the change in battery temperature during the discharge process. The increase in temperature may affect the efficiency and life of the battery, and can be measured and obtained through a temperature sensor.

[0043] The battery discharge current value, that is, the current flowing out of the battery during the discharge process, reflects the battery discharge rate and can be measured and obtained by a current sensor.

[0044] The battery discharge voltage value, that is, the voltage output by the battery during the discharge process, usually decreases with the degree of battery discharge and can be measured and obtained by a voltage sensor.

[0045] Specifically, the specific steps for obtaining the high-load operation health index of lithium battery equipment are as follows: obtain the high-load operation parameter data of the lithium battery equipment, the high-load operation parameter data include the high-load battery maximum output current value, the battery operation nominal voltage value, the battery initial internal resistance value, the battery operation maximum temperature value, the high-load motor maximum output power, and the high-load motor maximum speed value; the high-load operation parameter data of the lithium battery equipment is combined with the high-load operation test data of the lithium battery equipment for comprehensive analysis to obtain the high-load operation health index of the lithium battery equipment.

[0046] Among them, the high-load battery maximum output current value indicates the maximum current value that the battery can safely output under a high-load state, reflecting the current carrying capacity of the battery under high load, and can be extracted from the instruction manual provided by the battery manufacturer.

[0047] The nominal operating voltage of a battery indicates the standard voltage value of the battery under normal working conditions. It is usually the rated voltage of the battery. This value is the typical voltage of the battery when it is working normally and can be extracted from the instruction manual provided by the battery manufacturer.

[0048] The initial internal resistance of the battery refers to the internal resistance of the battery when it is used for the first time. The internal resistance is the obstacle to the flow of current caused by the internal materials of the battery, which affects the efficiency and performance of the battery. It can be measured by an internal resistance tester or specialized battery testing equipment and recorded when the battery is tested for the first time.

[0049] The maximum operating temperature value of the battery indicates the maximum operating temperature that the battery can safely withstand. Exceeding this temperature may cause battery damage or reduced performance. It can be extracted from the instruction manual provided by the battery manufacturer.

[0050] The high-load motor maximum output power indicates the maximum power output that the motor can provide when under high load, indicating the performance limit of the motor under high load, and can be extracted from the instruction manual provided by the battery manufacturer.

[0051] The maximum speed value of a high-load motor indicates the maximum speed that the motor can reach under high load, reflects the working ability of the motor under extreme load conditions, and can be extracted from the instruction manual provided by the battery manufacturer.

[0052] The specific formula for calculating the high-load operation health index of lithium battery equipment is as follows: ;in, It is the high load operation health index of lithium battery equipment. The high-load battery output current value for lithium-ion devices. The maximum output current value of the high-load battery of the lithium battery device. is the high load current influence coefficient stored in the database, It is the high load battery operating voltage value of lithium battery equipment. It is the nominal voltage value of the battery of lithium battery equipment. is the high load voltage influence coefficient stored in the database, is the high-load battery internal resistance of lithium battery equipment, is the initial internal resistance of the battery of the lithium battery device, is the high load internal resistance influence coefficient stored in the database, It is the high load battery operating temperature value of lithium battery equipment. The maximum temperature that the battery of lithium battery equipment can withstand. is the high load temperature influence coefficient stored in the database, Output power for high-load motors of lithium battery equipment. The maximum output power of the high-load motor of the lithium battery equipment. is the high-load motor output power influence coefficient stored in the database, is the high-load motor speed value of the lithium battery device, The maximum speed of the high-load motor of the lithium battery device. It is the high load motor speed influence coefficient stored in the database.

[0053] It should be explained that the high load current influence coefficient stored in the database , High load voltage influence coefficient , High load internal resistance influence coefficient , High load temperature influence coefficient , High load motor output power influence coefficient , High load motor speed influence coefficient The specific acquisition steps are as follows: collect real-time data such as high-load current, battery voltage, battery internal resistance, battery temperature, motor power output, and motor speed of lithium battery equipment. These data are stored in the database in real time through the sensor system. Each data point reflects the key operating parameters of the equipment under high load conditions. By analyzing these stored data, using regression analysis, statistical modeling and other methods, the influence coefficients related to each operating parameter are obtained. These influence coefficients reflect the specific impact of factors such as battery current, voltage, internal resistance, temperature, motor power output and motor speed under high load on the health status of the equipment.

[0054] In this implementation scheme, by comprehensively collecting and analyzing the key operating data of lithium battery equipment under high-load working conditions and combining the influence coefficients in the database, the health status of the equipment can be accurately evaluated. By collecting data such as the output current, voltage, internal resistance, temperature of the battery, and the power output and speed of the motor, the performance of the equipment under high load can be fully reflected. Then, the influence coefficients related to various parameters are calculated through regression analysis, statistical modeling and other methods. These influence coefficients can help monitor the working conditions of batteries and motors in real time, predict health changes of equipment, avoid being in abnormal conditions for a long time, and thus identify potential faults in advance. Through intelligent analysis, the downtime of equipment can be reduced, maintenance plans can be optimized, the service life of equipment can be extended, and long-term maintenance costs can be reduced. In addition, accurate health assessments help improve the operating efficiency of equipment and ensure its stable operation under high-load environments.

[0055] Specifically, Figure 2 As shown, the specific steps for obtaining the low-load operation health index of the lithium battery device are as follows: obtain the low-load operation parameter data of the lithium battery device, the low-load operation parameter data include the battery output nominal current value, the ambient temperature value in the set area, and the low-load motor maximum output power; read the battery operation nominal voltage value and the battery initial internal resistance value of the lithium battery device, and conduct a comprehensive analysis based on the low-load operation test data and low-load operation parameter data of the lithium battery device to obtain the low-load operation health index of the lithium battery device.

[0056] Among them, the nominal current value of the battery output indicates the standard current value output by the battery under low load conditions. This value is the expected current output of the battery under normal working conditions and can be extracted from the instruction manual provided by the battery manufacturer.

[0057] The ambient temperature value in the set area indicates the ambient temperature of the battery operating area, which affects the performance and health of the battery, especially under low load conditions. It can be measured by an ambient temperature sensor (such as a temperature probe or thermocouple).

[0058] The maximum output power of a low-load motor refers to the maximum power output that the motor can provide under low-load conditions. Usually, the motor operates under lighter load conditions when the load is lower.

[0059] Among them, the specific formula for calculating the low-load operation health index of lithium battery equipment is as follows: ;in, It is the low-load operation health index of lithium battery equipment. The low-load battery output current value for lithium battery equipment. The nominal current value of the battery output of the lithium battery device. is the low load current impact coefficient stored in the database, It is the low load battery operating voltage value of lithium battery equipment. It is the nominal voltage value of the battery of lithium battery equipment. is the low load voltage impact coefficient stored in the database, The low load battery internal resistance of lithium battery equipment. is the initial internal resistance of the battery of the lithium battery device, is the low load internal resistance influence coefficient stored in the database, is a natural constant, and in this embodiment, its value is 2.71. It is the ambient temperature value in the setting area of ​​the lithium battery equipment. It is the low load battery operating temperature value of lithium battery equipment. is the low load temperature influence coefficient stored in the database, Output power for low-load motors of lithium battery equipment. The maximum output power of the low-load motor of the lithium battery device. It is the low load motor output power influence coefficient stored in the database.

[0060] It should be explained that the low load current influence coefficient stored in the database , Low load voltage influence coefficient , Low load internal resistance influence coefficient , Low load temperature influence coefficient , Low load motor output power influence coefficient The specific acquisition steps are as follows: first, the battery current, battery voltage, battery internal resistance, battery temperature and motor power output parameters under low load conditions are collected through the data acquisition system of the equipment. All real-time collected data will be stored in the database of the equipment through the sensor system. Each data point reflects the key operating data of the equipment under low load conditions, and is transmitted to the database for storage through data to form a data set. Then, regression analysis and statistical modeling methods are used to analyze the relationship between each data point and the health status of the equipment under low load conditions based on the relevant data stored in the database. Through the distribution analysis of these stored data, the influence coefficients related to the health status of the equipment are obtained, such as the specific influence of parameters such as low load current, low load voltage, internal resistance, temperature and motor power on the health status of the equipment. Finally, the influence coefficients under low load working conditions are calculated to more accurately evaluate the health status of the equipment under low load conditions.

[0061] In this implementation scheme, the health status of the equipment is accurately evaluated by comprehensively analyzing various key parameters of the equipment under low-load working conditions. By collecting data such as battery current, voltage, internal resistance, temperature, and motor power, combined with the low-load operation parameter data of the equipment, this method can fully reflect the performance of the battery and motor under low-load conditions. Through the influence coefficients stored in the database, combined with regression analysis and statistical modeling, the system can accurately evaluate the impact of each parameter on the health of the equipment, ensuring comprehensive monitoring of equipment performance, especially under low-load conditions. Slight changes in parameters such as battery temperature, internal resistance, current, and voltage may affect the long-term operation of the equipment. Real-time monitoring and analysis of these parameters can detect equipment health problems at an early stage, prevent the equipment from being in a potential fault state for a long time, reduce fault downtime and maintenance costs, and at the same time, accurate health assessments help optimize equipment operating efficiency, extend service life, and improve overall economic benefits.

[0062] Specifically, the specific steps to obtain the charging operation health efficiency index of the lithium battery equipment are as follows: obtain the charging operation parameter data of the lithium battery equipment, the charging operation parameter data including the battery charging parameter temperature value, the battery maximum charging current value, and the battery charging nominal voltage value; conduct a comprehensive analysis on the charging operation parameter data of the lithium battery equipment in combination with the charging operation test data of the lithium battery equipment to obtain the charging operation health efficiency index of the lithium battery equipment.

[0063] Among them, the battery charging parameter temperature value indicates the optimal operating temperature of the battery during charging. Usually, the charging efficiency and safety of the battery will be affected by the temperature, which can be extracted from the instruction manual provided by the battery manufacturer.

[0064] The maximum charging current value of the battery indicates the maximum current value that the battery can safely withstand during the charging process. Exceeding this value may cause damage to the battery. It can be extracted from the instruction manual provided by the battery manufacturer.

[0065] The nominal voltage value of battery charging indicates the standard voltage value used by the battery during the charging process, which is usually consistent with the nominal voltage of the battery and can be extracted from the instruction manual provided by the battery manufacturer.

[0066] The specific formula for calculating the charging operation health efficiency index of lithium battery equipment is as follows: ;in, It is the charging and operating health efficiency index of lithium battery equipment. is the battery charging efficiency value of the lithium battery device, is a natural constant, and in this embodiment, its value is 2.71. It is the battery charging temperature value of lithium battery equipment. Set the temperature value for charging the battery of lithium battery equipment. is the charging temperature influence coefficient stored in the database, It is the battery charging current value of the lithium battery device. It is the maximum charging current value of the battery of the lithium battery device. is the charging current influence coefficient stored in the database, It is the battery charging voltage value of the lithium battery device. The nominal voltage value for charging the battery of lithium battery equipment. It is the charging voltage influence coefficient stored in the database.

[0067] It needs to be explained that the charging temperature influence coefficient stored in the database , Charging current influence coefficient , Charging voltage influence coefficient The specific acquisition steps are as follows: first, when the device is charging, the data acquisition system obtains the battery's charging current, charging voltage, battery temperature and other parameters in real time. These data are monitored in real time by sensors and transmitted to the database for storage. The charging current influence coefficient is obtained by recording the current data of the battery during charging and analyzing the impact of the current on the charging process; the charging voltage influence coefficient is obtained by monitoring the voltage data of the battery during charging, reflecting the impact of voltage changes on charging efficiency and battery health; the charging temperature influence coefficient is obtained by collecting the battery temperature data during the charging process through a temperature sensor, and storing it in the database, and analyzing the impact on battery health through temperature changes. All these data will be stored in the database and calculated through regression analysis, statistical modeling and other methods to finally obtain the charging temperature influence coefficient. , Charging current influence coefficient , Charging voltage influence coefficient , providing a basis for the health assessment of the charging process.

[0068] In this implementation scheme, by comprehensively analyzing multiple key parameters in the charging operation process, an accurate assessment of the charging efficiency and health status of the lithium battery equipment is achieved. By collecting the battery's charging current, charging voltage and temperature data, and combining the charging operation parameter data, the method can accurately reflect the impact of the charging process on the battery. By using the influence coefficients stored in the database and through regression analysis and statistical modeling, the specific impact of factors such as charging temperature, charging current and charging voltage on the health of the equipment can be calculated. By real-time monitoring of these parameters, abnormal conditions can be discovered in time during the charging process to avoid battery damage due to excessive temperature or excessive current during the charging process. At the same time, accurate charging efficiency evaluation can optimize the charging process, extend battery life, and reduce maintenance costs. This method can effectively improve the safety, reliability and efficiency of the charging process, and provide guarantee for the long-term stable operation of the equipment.

[0069] Specifically, the specific steps for obtaining the discharge operation health efficiency index of the lithium battery equipment are as follows: obtain the discharge operation parameter data of the lithium battery equipment, the discharge operation parameter data including the battery discharge parameter temperature value, the battery discharge nominal current value, and the battery discharge nominal voltage value; conduct a comprehensive analysis on the discharge operation parameter data of the lithium battery equipment in combination with the discharge operation test data of the lithium battery equipment to obtain the discharge operation health efficiency index of the lithium battery equipment.

[0070] Among them, the battery discharge parameter temperature value indicates the temperature range that the battery can safely withstand during the discharge process. Too high temperature may affect the discharge efficiency and battery life. It can be extracted from the instruction manual provided by the battery manufacturer.

[0071] The nominal current value of battery discharge indicates the nominal standard current value of the battery during the discharge process, reflects the working current of the battery during normal discharge, and can be extracted from the instruction manual provided by the battery manufacturer.

[0072] The nominal voltage value of battery discharge refers to the nominal voltage value output by the battery during discharge, which is usually lower than the voltage during charging and can be extracted from the instruction manual provided by the battery manufacturer.

[0073] The specific formula for calculating the discharge operation health efficiency index of lithium battery equipment is as follows: ;in, It is the discharge operation health efficiency index of lithium battery equipment. is a natural constant, and in this embodiment, its value is 2.71. is the battery discharge temperature value of the lithium battery device, Determine the temperature value for battery discharge of lithium battery equipment. is the discharge temperature influence coefficient stored in the database, is the battery discharge current value of the lithium battery device, It is the nominal current value of the battery discharge of lithium battery equipment. is the discharge current influence coefficient stored in the database, is the battery discharge voltage value of the lithium battery device, It is the nominal discharge voltage value of the battery of the lithium battery device. It is the discharge voltage influence coefficient stored in the database.

[0074] It should be explained that the discharge temperature influence coefficient stored in the database , Discharge current influence coefficient , Discharge voltage influence coefficient The specific acquisition steps are as follows: first, when the device is discharging, the data acquisition system collects key data in real time through temperature sensors, current sensors and voltage sensors, including current data, voltage data and battery temperature data during battery discharge. These data are stored in the database through the data transmission system. The discharge temperature influence coefficient is calculated by recording the temperature data during battery discharge, and the influence of temperature on discharge efficiency is analyzed; the discharge current influence coefficient reflects the influence of current changes on battery health by monitoring the battery current data during discharge; the discharge voltage influence coefficient is calculated by collecting the voltage data during battery discharge, and the influence of voltage changes on discharge efficiency and battery health is evaluated. All these data are stored in the database, and the corresponding influence coefficients are obtained through regression analysis, statistical modeling and other methods, so as to evaluate the specific influence of different parameters of the device on battery health during discharge.

[0075] Among them, the specific implementation example of calculating the discharge operation health efficiency index of lithium battery equipment is as follows, and the following parameters are available: The battery discharge temperature of lithium battery equipment is approximately: 28.983℃.

[0076] The battery discharge parameter temperature value of lithium battery equipment is approximately: 30.000℃.

[0077] The discharge temperature influence coefficient stored in the database is approximately: 0.159.

[0078] The battery discharge current value of lithium battery equipment is approximately: 3.472A.

[0079] The nominal current value of the battery discharge of lithium battery equipment is approximately: 3.500A.

[0080] The discharge current influence coefficient stored in the database is approximately: 1.004.

[0081] The battery discharge voltage value of lithium battery equipment is approximately: 11.398V.

[0082] The nominal discharge voltage of lithium battery equipment is approximately: 12.000V.

[0083] The discharge voltage influence coefficient stored in the database is approximately: 1.015.

[0084] Natural constant: 2.71.

[0085] Substituting the above data into the specific formula for calculating the discharge operation health efficiency index of lithium battery equipment, we get: The discharge operation health efficiency index of lithium battery equipment = (1 / (1+2.71^(-0.159×(28.983-30.000))))×((3.472 / 3.500)^1.004)×((11.398 / 12.000)^1.015)≈0.433.

[0086] In this implementation scheme, the discharge efficiency and health status of the lithium battery equipment are accurately evaluated through real-time monitoring and comprehensive analysis of key parameters (such as discharge temperature, current, voltage, etc.) during the discharge operation process. By combining the discharge operation parameter data, such as the battery's nominal discharge current, voltage and temperature, the system can reflect the discharge performance of the equipment under different working conditions. By using the influence coefficients stored in the database and through regression analysis and statistical modeling methods, the specific effects of temperature, current and voltage on battery health can be calculated, and potential problems of the battery during the discharge process can be discovered in time. This method ensures the efficiency and safety of the charging process, and avoids performance degradation or damage of the battery due to excessive discharge or excessive temperature. At the same time, through accurate health assessment, the occurrence of equipment failures can be reduced, the equipment service life can be increased, and maintenance costs can be reduced, thereby improving the working efficiency and economic benefits of the entire lithium battery equipment.

[0087] See also Figure 3The embodiment of the present invention provides a technical solution: an intelligent detection system based on lithium battery equipment, including: an operation test data acquisition module, used to acquire and pre-process the operation test data of the lithium battery equipment, the operation test data including high-load operation test data, low-load operation test data, charging operation test data, and discharging operation test data; an operation test data analysis module, used to comprehensively analyze the pre-processed operation test data of the lithium battery equipment to obtain an operation health status set of the lithium battery equipment, the operation health status set including a high-load operation health index, a low-load operation health index, a charging operation health efficiency index, and a discharging operation health efficiency index; a comprehensive health analysis module, used to comprehensively analyze the operation health status set of the lithium battery equipment to obtain a comprehensive health status index of the lithium battery equipment; a comprehensive health judgment module, used to judge and analyze the comprehensive health status index of the lithium battery equipment with a preset comprehensive health assessment interval, and when the comprehensive health status index of the lithium battery equipment is outside the preset comprehensive health assessment interval, the lithium battery equipment is marked as abnormal operation, and an abnormal operation alarm is sent to relevant staff.

[0088] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0089] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. An intelligent detection method based on lithium battery equipment, characterized in that: The following steps are involved: Acquire and preprocess operation test data of the lithium battery device, wherein the operation test data includes high-load operation test data, low-load operation test data, charging operation test data, and discharging operation test data; Comprehensively analyzing the pre-processed operation test data of the lithium battery equipment to obtain an operation health status set of the lithium battery equipment, wherein the operation health status set includes a high-load operation health index, a low-load operation health index, a charging operation health efficiency index, and a discharging operation health efficiency index; Comprehensively analyze the operating health status set of lithium battery equipment to obtain the comprehensive health status index of lithium battery equipment, and make judgment and analysis with the preset comprehensive health assessment interval; When the comprehensive health status index of the lithium battery device is outside the preset comprehensive health assessment range, the lithium battery device is marked as operating abnormally and an operating abnormality alarm is sent.

2. The intelligent detection method based on lithium battery equipment according to claim 1 is characterized in that: The specific formula for calculating the comprehensive health status index of lithium battery equipment is as follows: ； in, , , , , They are the comprehensive health status index of lithium battery equipment, high-load operation health index, low-load operation health index, charging operation health efficiency index, and discharging operation health efficiency index. , , , The high load operation influence coefficient, low load operation influence coefficient, charging operation influence coefficient, and discharging operation influence coefficient stored in the database are in turn. is a natural constant.

3. The intelligent detection method based on lithium battery equipment according to claim 1 is characterized in that: The high-load operation test data includes a high-load battery output current value, a high-load battery operation voltage value, a high-load battery internal resistance value, a high-load battery operation temperature value, a high-load motor output power, and a high-load motor speed value; the low-load operation test data includes a low-load battery output current value, a low-load battery operation voltage value, a low-load battery internal resistance value, a low-load battery operation temperature value, and a low-load motor output power; the charging operation test data includes a battery charging efficiency value, a battery charging temperature value, a battery charging current value, and a battery charging voltage value; and the discharging operation test data includes a battery discharge temperature value, a battery discharge current value, and a battery discharge voltage value.

4. The intelligent detection method based on lithium battery equipment according to claim 3 is characterized in that: The specific steps to obtain the high-load operation health index of lithium battery equipment are as follows: Obtain high-load operation parameter data of the lithium battery device, wherein the high-load operation parameter data includes a high-load battery maximum output current value, a battery operating nominal voltage value, a battery initial internal resistance value, a battery operating maximum withstand temperature value, a high-load motor maximum output power, and a high-load motor maximum speed value; The high-load operation parameter data of the lithium battery equipment is combined with the high-load operation test data of the lithium battery equipment for comprehensive analysis to obtain the high-load operation health index of the lithium battery equipment.

5. The intelligent detection method based on lithium battery equipment according to claim 4 is characterized in that: The specific formula for calculating the high-load operation health index of lithium battery equipment is as follows: ； in, , , , , , , , , , , , , They are the high-load operation health index of lithium battery equipment, high-load battery output current value, high-load battery maximum output current value, high-load battery operation voltage value, battery operation nominal voltage value, high-load battery internal resistance value, battery initial internal resistance value, high-load battery operation temperature value, battery operation maximum temperature value, high-load motor output power, high-load motor maximum output power, high-load motor speed value, high-load motor maximum speed value, , , , , , They are the high-load current influence coefficient, high-load voltage influence coefficient, high-load internal resistance influence coefficient, high-load temperature influence coefficient, high-load motor output power influence coefficient, and high-load motor speed influence coefficient stored in the database, respectively.

6. The intelligent detection method based on lithium battery equipment according to claim 4 is characterized in that: The specific steps to obtain the low-load operation health index of lithium battery equipment are as follows: Obtain low-load operation parameter data of the lithium battery device, wherein the low-load operation parameter data includes the nominal current value of the battery output, the ambient temperature value in the set area, and the maximum output power of the low-load motor; The nominal operating voltage value and initial internal resistance value of the lithium battery device are read, and a comprehensive analysis is performed on the low-load operating test data and low-load operating parameter data of the lithium battery device to obtain the low-load operating health index of the lithium battery device.

7. The intelligent detection method based on lithium battery equipment according to claim 3 is characterized in that: The specific steps to obtain the charging operation health efficiency index of lithium battery equipment are as follows: Obtaining charging operation parameter data of the lithium battery device, wherein the charging operation parameter data includes a battery charging parameter temperature value, a battery maximum charging current value, and a battery charging nominal voltage value; The charging operation parameter data of the lithium battery equipment is combined with the charging operation test data of the lithium battery equipment for comprehensive analysis to obtain the charging operation health efficiency index of the lithium battery equipment.

8. The intelligent detection method based on lithium battery equipment according to claim 7 is characterized in that: The specific formula for calculating the charging operation health efficiency index of lithium battery equipment is as follows: ； in, , , , , , , , They are the charging operation health efficiency index of lithium battery equipment, battery charging efficiency value, battery charging temperature value, battery charging parameter temperature value, battery charging current value, battery maximum charging current value, battery charging voltage value, and battery charging nominal voltage value. is a natural constant, , , They are the charging temperature influence coefficient, charging current influence coefficient, and charging voltage influence coefficient stored in the database respectively.

9. The intelligent detection method based on lithium battery equipment according to claim 3 is characterized in that: The specific steps to obtain the discharge operation health efficiency index of lithium battery equipment are as follows: Obtaining discharge operation parameter data of the lithium battery device, wherein the discharge operation parameter data includes a battery discharge parameter temperature value, a battery discharge nominal current value, and a battery discharge nominal voltage value; The discharge operation parameter data of the lithium battery equipment is combined with the discharge operation test data of the lithium battery equipment for comprehensive analysis to obtain the discharge operation health efficiency index of the lithium battery equipment.

10. An intelligent detection system based on lithium battery equipment, using the intelligent detection method based on lithium battery equipment according to any one of claims 1 to 9, characterized in that: include: An operation test data acquisition module is used to acquire and pre-process the operation test data of the lithium battery device, wherein the operation test data includes high-load operation test data, low-load operation test data, charging operation test data, and discharging operation test data; An operation test data analysis module is used to comprehensively analyze the pre-processed operation test data of the lithium battery device to obtain an operation health status set of the lithium battery device, wherein the operation health status set includes a high-load operation health index, a low-load operation health index, a charging operation health efficiency index, and a discharging operation health efficiency index; The comprehensive health analysis module is used to comprehensively analyze the operating health status set of the lithium battery equipment to obtain the comprehensive health status index of the lithium battery equipment; The comprehensive health judgment module is used to judge and analyze the comprehensive health status index of the lithium battery device and the preset comprehensive health assessment interval. When the comprehensive health status index of the lithium battery device is outside the preset comprehensive health assessment interval, the lithium battery device is marked as operating abnormally and an operating abnormality alarm is sent.

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