Method and system for detecting lithium battery charger based on dynamic working condition

Abstract

This invention discloses a detection method and system for lithium battery chargers based on dynamic operating conditions. The invention relates to the technical field of lithium battery charger detection methods. The dynamic operating conditions are determined based on the testing scenario and model of the lithium battery charger. Multiple charging data points are collected during the dynamic testing process. A state characteristic curve is determined based on these multiple charging data points and the dynamic operating conditions, ensuring the accuracy of the state characteristic curve. Therefore, a combination of change regions is determined by comparing multiple state characteristic curves. This combination contains multiple change regions within the same time range. The actual output voltage is determined based on these multiple change regions. Anomalies are identified based on the multiple actual output voltages, the voltage of a preset charging stage, and the dynamic operating conditions. The anomaly factors are traced back to these anomalies, ensuring the accuracy of anomaly detection and controlling the anomalies.

Description

Technical Field

[0001] This invention relates to the technical field of lithium battery chargers, and more particularly to a detection method and system for lithium battery chargers based on dynamic operating conditions. Background Technology

[0002] With the development of technology, lithium battery chargers are set up in indoor or outdoor scenarios to charge external electric devices. In the existing technology, lithium battery chargers perform charging detection at a preset voltage and corresponding static detection in a static environment. However, they cannot introduce dynamic detection of multiple operating conditions, which affects the targeted analysis of lithium battery chargers under rated operating conditions, overvoltage operating conditions, and undervoltage operating conditions, and reduces the accuracy of the state characteristic curve of lithium battery chargers. Summary of the Invention

[0003] The purpose of this invention is to overcome the shortcomings of the prior art. This invention provides a detection method and system for lithium battery chargers based on dynamic operating conditions.

[0004] This invention provides a testing method for a lithium battery charger based on dynamic operating conditions, comprising: collecting multiple test parameters of the lithium battery charger during dynamic testing; determining the testing scenario of the lithium battery charger based on the multiple test parameters, and determining the dynamic operating conditions according to the testing scenario and the model of the lithium battery charger; the dynamic operating conditions include rated operating conditions, overvoltage operating conditions, and undervoltage operating conditions; collecting multiple charging data of the lithium battery charger during dynamic testing, and determining a state characteristic curve based on the multiple charging data and the dynamic operating conditions of the lithium battery charger; collecting multiple state characteristic curves under different dynamic operating conditions, and determining a combination of change regions based on the comparison of the multiple state characteristic curves, wherein the combination of change regions contains multiple change regions within the same time range;

[0005] The actual output voltage is determined based on multiple variation regions, and abnormal nodes are identified based on multiple actual output voltages, the voltage of the preset charging stage, and dynamic operating conditions. Abnormal factors are then traced back to these abnormal nodes.

[0006] This invention provides a testing system for a lithium battery charger based on dynamic operating conditions. The testing system is applied to the aforementioned testing method for lithium battery chargers based on dynamic operating conditions. The testing system includes:

[0007] The test parameter module is used to collect multiple test parameters of the lithium battery charger during the dynamic testing process.

[0008] The dynamic operating condition module is used to determine the testing scenario of the lithium battery charger based on multiple test parameters of the lithium battery charger, and to determine the dynamic operating condition according to the testing scenario and the model of the lithium battery charger; the dynamic operating condition includes rated operating condition, overvoltage operating condition and undervoltage operating condition.

[0009] The state characteristic curve module is used to collect multiple charging data of the lithium battery charger during dynamic testing, and determine the state characteristic curve based on the multiple charging data and the dynamic operating conditions of the lithium battery charger.

[0010] The variable region combination module is used to collect multiple state characteristic curves under different dynamic working conditions, and determine the variable region combination based on the comparison of multiple state characteristic curves. The variable region combination contains multiple variable regions within the same time range.

[0011] The anomaly tracing module is used to determine the actual output voltage based on multiple changing regions, and to identify the abnormal node based on the multiple actual output voltages, the voltage of the preset charging stage, and the dynamic operating conditions, and to trace the abnormal factors based on the anomaly node.

[0012] Compared with the prior art, the beneficial effects of the present invention are:

[0013] In this embodiment of the invention, the method of this embodiment collects multiple charging data of the lithium battery charger during dynamic testing, and determines the state characteristic curve based on the multiple charging data and the dynamic operating conditions of the lithium battery charger. The dynamic operating conditions are introduced, which takes into account the overall consideration of multiple charging data and the dynamic operating conditions of the lithium battery charger, ensuring the accuracy of the state characteristic curve, and realizing targeted analysis of rated operating conditions, overvoltage operating conditions and undervoltage operating conditions.

[0014] Therefore, a combination of change regions is determined by comparing multiple state characteristic curves. This combination contains multiple change regions within the same time range. The actual output voltage is determined based on these multiple change regions. Abnormal nodes are identified based on these actual output voltages, the voltage of the preset charging stage, and dynamic operating conditions. Abnormal factors are traced back to these abnormal nodes. This method introduces a combination of change regions and further analyzes multiple state characteristic curves. It takes into account multiple actual output voltages, the voltage of the preset charging stage, and dynamic operating conditions, ensuring the accuracy of abnormal node detection and controlling abnormal factors. Attached Figure Description

[0015] Figure 1 This is a flowchart illustrating the detection method for a lithium battery charger based on dynamic operating conditions in an embodiment of the present invention.

[0016] Figure 2 This is a schematic diagram of the structural composition of the detection system for a lithium battery charger based on dynamic operating conditions in an embodiment of the present invention. Detailed Implementation

[0017] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0018] Please see Figure 1 and Figure 2 A testing method for lithium battery chargers based on dynamic operating conditions is provided, applicable to the testing scenarios of lithium battery chargers. This testing method for lithium battery chargers based on dynamic operating conditions includes:

[0019] Step S11: During the dynamic testing of the lithium battery charger, collect multiple test parameters of the lithium battery charger;

[0020] Step S12: Determine the testing scenario of the lithium battery charger based on multiple test parameters of the lithium battery charger, and determine the dynamic operating conditions according to the testing scenario and the model of the lithium battery charger; the dynamic operating conditions include rated operating conditions, overvoltage operating conditions and undervoltage operating conditions.

[0021] Step S13: Collect multiple charging data of the lithium battery charger during the dynamic test process, and determine the state characteristic curve based on the multiple charging data and the dynamic operating conditions of the lithium battery charger.

[0022] Step S14: Collect multiple state characteristic curves under different dynamic working conditions, and determine the combination of change regions based on the comparison of multiple state characteristic curves. The combination of change regions contains multiple change regions within the same time range.

[0023] Step S15: Determine the actual output voltage based on multiple changing regions, and determine the abnormal node based on the multiple actual output voltages, the voltage of the preset charging stage, and the dynamic operating conditions, and identify the abnormal factors based on the tracing of the abnormal node.

[0024] In step S11, during the dynamic testing of the lithium battery charger, multiple test parameters of the lithium battery charger are collected.

[0025] In the specific implementation of this invention, the specific steps are as follows:

[0026] S111: Collect past usage records of the lithium battery charger, determine the past operating parameters of the lithium battery charger based on the past usage records, and determine the current status of the lithium battery charger based on the past operating parameters and the model of the lithium battery charger.

[0027] S112: Collect the test tasks of the lithium battery charger, and determine the corresponding test content based on the identification of the test tasks of the lithium battery charger. Based on the corresponding test content and the current status of the lithium battery charger, determine multiple test parameters of the lithium battery charger, including test location, current test parameters, voltage test parameters, and test sequence.

[0028] In the embodiments of this application, historical usage records of the lithium battery charger are collected, including the number of charging cycles, charging time, charging current, charging voltage, and fault records. Based on these historical usage records, key operating parameters are extracted to evaluate the historical performance and health status of the lithium battery charger. At this point, the average charging current reflects the load capacity and efficiency of the lithium battery charger; the highest / lowest charging voltage reflects the voltage regulation capability and stability of the lithium battery charger; the number of charging cycles reflects the lifespan and aging degree of the lithium battery charger; and the fault type and frequency reveal defects or vulnerable components in the lithium battery charger.

[0029] Based on the historical performance and model characteristics of the lithium battery charger, its current status is assessed, including its health, performance level, and potential problems. Optionally, a lithium battery charger of model XYZ1234 is assumed to require performance testing. Historical usage records were read from the charger's built-in memory, revealing that it underwent 20 charges in the past year without any recorded malfunctions. Key operating parameters were extracted and compared with the performance specifications of the XYZ1234 model. The average charging current and maximum charging voltage were found to be within specifications, but the number of charging cycles was approaching the manufacturer's recommended maximum number of charging cycles (assumed to be 300). Based on historical operating parameters and model characteristics, the current status of the lithium battery charger is assessed. Its overall performance is considered good, but performance is gradually declining due to approaching its lifespan limit. Therefore, subsequent tests will focus on indicators such as efficiency, temperature rise, and charging stability to ensure its safety and reliability during continued use.

[0030] Furthermore, the test tasks of the lithium battery charger are collected, and the corresponding test content is determined based on the identification of the test tasks. Based on the corresponding test content and the current state of the lithium battery charger, multiple test parameters of the lithium battery charger are determined. These multiple test parameters include test location, current test parameters, voltage test parameters, and test sequence. This comprehensive consideration of the corresponding test content and the current state of the lithium battery charger ensures the accuracy of the multiple test parameters of the lithium battery charger.

[0031] At this point, the test tasks for the lithium battery charger are obtained from the test plan or test requirements document; based on the identification of the test tasks, specific test items or test points are determined; optionally, the test content includes functional testing, performance testing, safety testing, or reliability testing.

[0032] Functional testing: Verify whether the various functions of the lithium battery charger are working properly, such as charging start-up, charging end-up, over-temperature protection, and short-circuit protection;

[0033] Performance testing: Evaluate the performance indicators of lithium battery chargers, such as efficiency, power factor, temperature rise, and charging speed.

[0034] Safety testing: Check the behavior of the lithium battery charger under abnormal conditions, such as excessively high / low input voltage, reverse battery connection, battery overheating, etc.

[0035] Reliability testing: Simulates long-term operation or use under harsh environmental conditions to evaluate the durability and stability of lithium battery chargers.

[0036] Based on the test content and the current state of the lithium battery charger, determine the specific test parameters, i.e., how to test. At this point, the test location specifies the connection position of the sensor or measuring device during the test, such as the input terminal, output terminal, battery interface, etc. Current test parameters include the range, accuracy, and sampling rate of the test current, used to evaluate the current regulation capability and stability of the lithium battery charger. Voltage test parameters include the range, accuracy, and sampling rate of the test voltage, used to evaluate the voltage output accuracy and stability of the lithium battery charger. The test sequence determines the execution order of each test to ensure the comprehensiveness and effectiveness of the test, while avoiding unnecessary damage to the lithium battery charger.

[0037] In step S12, the testing scenario of the lithium battery charger is determined based on multiple test parameters of the lithium battery charger, and the dynamic operating conditions are determined according to the testing scenario and the model of the lithium battery charger; the dynamic operating conditions include rated operating conditions, overvoltage operating conditions and undervoltage operating conditions.

[0038] In the specific implementation of this invention, the specific steps are as follows:

[0039] S121: Collect multiple test parameters of the lithium battery charger, determine multiple test combinations based on the synthesis of multiple test parameters of the lithium battery charger, determine the corresponding test features based on the identification of multiple test combinations, and collect multiple test features.

[0040] S122: Collect the form and surrounding environment of the lithium battery charger, and determine the test scenario of the lithium battery charger based on multiple test characteristics, the form and surrounding environment of the lithium battery charger. The test scenario of the lithium battery charger includes voltage detection, current detection and stability detection.

[0041] S123: Dynamic operating conditions are determined based on the detection scenario of the lithium battery charger, the current operating status of the lithium battery charger, and the matching of the lithium battery charger model. These dynamic operating conditions include rated operating conditions, overvoltage operating conditions, and undervoltage operating conditions.

[0042] In the embodiments of this application, various test parameters of the lithium battery charger are collected. These test parameters can comprehensively reflect the performance and characteristics of the lithium battery charger. At this time, basic parameters such as input voltage range, output voltage, output current, and charging power are obtained from the technical specifications of the lithium battery charger. Historical test data, including charging efficiency, temperature rise, and power factor under different conditions, are obtained from previous test records or databases. Based on the test requirements, additional parameters that need to be measured are determined, such as charging time, battery temperature, and internal resistance of the lithium battery charger.

[0043] Based on the collected test parameters, different test combinations are synthesized to simulate various situations encountered by lithium battery chargers in actual use. At this point, the usage scenarios and user needs of the lithium battery charger are analyzed to determine key test points, such as normal charging, fast charging, trickle charging, over-temperature protection, and short-circuit protection. According to the different test points, the test parameters are combined to form multiple test schemes. For example, input voltage, output current, and charging time are combined to test the charging speed of the lithium battery charger under different input voltages and output currents. This ensures that each test combination covers the key functions and performance characteristics of the lithium battery charger.

[0044] For each test combination, identify and determine the test characteristics that can reflect the performance and features of the lithium battery charger, and then perform actual measurements or observations; at this time, functional characteristics such as the response time of charging start and stop, the trigger temperature of over-temperature protection, and the action time of short-circuit protection are considered.

[0045] Performance characteristics: such as charging efficiency, power factor, temperature rise, charging speed, and voltage stability after the battery is fully charged;

[0046] Safety features include input overvoltage protection, battery reverse connection protection, and output overcurrent protection.

[0047] At this point, a programmable power supply is used to test the lithium battery charger; the parameters of the test equipment and instruments are set according to the requirements of the test combination and test characteristics; actual tests are conducted, test data is recorded, and the behavior and performance of the lithium battery charger are observed.

[0048] Specifically, the following test parameters were collected: Input voltage range: 0-240V AC (suitable for different regions and power supply conditions); Output voltage: assumed to be 54V DC based on the lithium battery specifications of the electric forklift (or the actual required voltage); Output current: maximum 200A (or the maximum allowable charging current of the electric forklift's lithium battery; 200A is used as an example here, the actual value may vary); Historical test data: historical charging efficiency, temperature rise, and other data of the electric forklift lithium battery charger under specific input voltage (e.g., 230V AC) and output current (e.g., maximum allowable current). The following test combination was determined:

[0049] Combination 1: Normal charging test

[0050] Input voltage: 220V AC (or local standard voltage); Output current: set according to the recommended charging current of the electric forklift lithium battery, assumed to be 100A (actual value adjusted according to battery status and charging strategy); Test objective: to evaluate the charging speed and efficiency of the lithium battery charger under normal charging conditions, as well as its impact on the charging effect and battery health of the electric forklift lithium battery.

[0051] Combination 2: Fast Charging Test

[0052] Input voltage: 240V AC (maximum input voltage, used to test fast charging capability); Output current: set according to the fast charging strategy, but not exceeding the maximum allowable value of the lithium battery charger and battery, assumed to be 200A (or actual fast charging current); Test objective: to evaluate the charging speed, temperature rise, and potential impact on the lithium battery of the electric forklift under fast charging conditions (such as battery temperature, lifespan, etc.).

[0053] Combination 3: Over-temperature protection test

[0054] Test environment: The lithium battery charger is placed in a simulated high-temperature environment (e.g., 50°C or higher, depending on the specifications of the lithium battery charger); Monitoring indicators: the trigger temperature and trigger time of the over-temperature protection function, and the status of the lithium battery charger after recovery; Test objective: To verify the effectiveness of the over-temperature protection function of the lithium battery charger in a high-temperature environment, and to ensure the safety and stability of the charging process.

[0055] The following test features were identified and collected:

[0056] Test characteristics of combination 1: Charging speed: the time required for the electric forklift lithium battery to go from 0% to 100% charge; Charging efficiency: the ratio of energy output to the electric forklift lithium battery to energy input to the lithium battery charger, used to evaluate energy conversion efficiency.

[0057] Test characteristics of combination 2: Charging speed: The time required for the lithium battery of the electric forklift to go from a low charge (e.g., 20%) to a high charge (e.g., 80%) under fast charging conditions to evaluate fast charging performance; Lithium battery charger temperature rise: The difference between the highest temperature of the lithium battery charger and room temperature during fast charging to evaluate the heat dissipation performance of the lithium battery charger.

[0058] Test characteristics of combination 3: Over-temperature protection trigger temperature: the temperature at which the lithium battery charger begins to perform over-temperature protection when the internal temperature of the lithium battery charger reaches a certain set value, used to evaluate the sensitivity of the protection mechanism; Recovery time: the time required for the lithium battery charger to cool down and return to normal operation after the over-temperature protection action is triggered, used to evaluate the recovery capability of the protection mechanism.

[0059] The following test data was obtained through actual testing:

[0060] Combination 1: Charging speed is 4 hours (hypothetical value, actual time varies depending on the capacity of the electric forklift's lithium battery and the performance of the lithium battery charger); charging efficiency is 88% (hypothetical value, actual time is affected by various factors).

[0061] Combination 2: Charging speed is 1.2 hours (hypothetical value, used to evaluate charging speed in fast charging mode); lithium battery charger temperature rise is 8°C (hypothetical value, used to evaluate thermal management performance during fast charging).

[0062] Combination 3: Over-temperature protection trigger temperature is 75℃ (hypothetical value, used to evaluate the protection capability of lithium battery charger in high-temperature environment); recovery time is 3 minutes (hypothetical value, used to evaluate the rapid recovery capability of lithium battery charger after protection mechanism).

[0063] Furthermore, the form and surrounding environment of the lithium battery charger are collected. Based on multiple test features, the form and surrounding environment of the lithium battery charger are used to determine the test scenario of the lithium battery charger. The test scenario of the lithium battery charger includes voltage detection, current detection and stability detection. It takes into account multiple test features, the form and surrounding environment of the lithium battery charger as a whole, so as to ensure the accuracy of the test scenario of the lithium battery charger.

[0064] At this point, it is necessary to understand the physical form of the lithium battery charger (such as size, shape, interface type, etc.) and its surrounding environment (such as temperature, humidity, electromagnetic interference, etc.); at this point, the size, weight, interface type and appearance characteristics of the lithium battery charger are obtained by direct observation or by using measuring tools; environmental sensors are used to measure the temperature and humidity conditions exposed to the lithium battery charger, assess potential sources of electromagnetic interference (such as other electronic devices, radio waves, etc.), and consider the geographical location of the lithium battery charger installation or use (such as outdoor scenes in shopping malls, outdoor scenes in parking lots, etc.).

[0065] Based on the form factor and surrounding environment of the lithium battery charger, and combined with several previously determined test characteristics, a series of test scenarios were set to ensure that the lithium battery charger can operate normally under various practical usage conditions. These included: a voltage testing scenario: considering the input voltage fluctuations faced by the lithium battery charger (such as unstable mains voltage, use of power sockets from different countries, etc.), test scenarios were set under different input voltages to evaluate the voltage regulation capability and output stability of the lithium battery charger; a current testing scenario: based on the output current specifications of the lithium battery charger, current testing scenarios were set under different load conditions, including normal load, overload, and short circuit, to verify the current limiting and protection functions of the lithium battery charger; and a stability testing scenario: considering the form factor and surrounding environment of the lithium battery charger, long-term operational stability testing scenarios were set, taking into account the impact of temperature and humidity changes on the performance of the lithium battery charger, as well as its operational stability under electromagnetic interference.

[0066] Specifically, the XYZ123 lithium battery charger features a compact design, equipped with an interface suitable for connecting electric forklift batteries, and is designed for outdoor charging scenarios; its dimensions are 550mm×400mm×700mm, and its weight is 55kg; the XYZ123 lithium battery charger has an output interface that matches the electric forklift battery.

[0067] The temperature range of the XYZ123 lithium battery charger is 0°C to 50°C (considering the extreme temperature conditions of electric forklifts working outdoors); relative humidity is not more than 90% (to adapt to variable outdoor climate conditions); the XYZ123 lithium battery charger is exposed to electromagnetic interference from the electric forklift's own motor, controller, and nearby wireless communication equipment.

[0068] Voltage testing scenarios: Input voltage range testing: Within the input voltage range of 110V to 240V AC (considering the differences in grid voltage in different regions), test the output voltage stability of the lithium battery charger to ensure a stable charging voltage for the electric forklift battery; Voltage fluctuation testing: Simulate grid voltage fluctuations (such as a fluctuation range of ±15%, considering the use of electric forklifts in areas with unstable power) to evaluate the voltage regulation capability of the lithium battery charger and prevent voltage surges from damaging the battery.

[0069] Current detection scenarios: Normal load detection: Based on the charging requirements of the electric forklift battery, set the rated output current (e.g., 50A or higher) to test the stability and efficiency of the lithium battery charger's output current, ensuring efficient charging; Overload protection detection: Simulate the output current exceeding the rated value (e.g., 1.5 times the rated current) to verify the overload protection function of the lithium battery charger, preventing equipment damage or safety hazards caused by excessive current; Short circuit protection detection: Simulate a short circuit at the output terminal to evaluate the short circuit protection response time and recovery capability of the lithium battery charger, ensuring rapid circuit disconnection in emergencies to protect the battery and lithium battery charger.

[0070] Stability testing scenarios: Temperature stability testing: Within a temperature range of 0°C to 50°C, test the performance and efficiency changes of the lithium battery charger to ensure normal operation in high or low temperature environments, meeting the charging needs of electric forklifts in different seasons; Humidity stability testing: Under conditions where the relative humidity does not exceed 90%, evaluate the insulation performance and internal circuit stability of the lithium battery charger to prevent short circuits or performance degradation caused by moisture; Electromagnetic compatibility testing: In an environment with electromagnetic interference from the electric forklift motor, controller, and nearby wireless communication equipment, test the output stability of the lithium battery charger and the degree of interference to other devices to ensure the compatibility of the lithium battery charger with other systems of the electric forklift and avoid mutual interference leading to malfunctions.

[0071] Therefore, dynamic operating conditions are determined based on the testing scenario of the lithium battery charger, the current operating status of the lithium battery charger, and the matching of the lithium battery charger model. These dynamic operating conditions include rated operating conditions, overvoltage operating conditions, and undervoltage operating conditions. This approach takes into account the overall consideration of the testing scenario of the lithium battery charger, the current operating status of the lithium battery charger, and the matching of the lithium battery charger model, ensuring the accuracy of the dynamic operating conditions.

[0072] At this point, the detection scenarios of the lithium battery charger (such as voltage detection, current detection, stability detection, etc.) are combined with its current actual operating state (such as standby, charging, fault protection, etc.) to simulate the dynamic behavior of the lithium battery charger in actual use. Then, the detection scenarios defined in S122 are reviewed to understand the specific performance requirements of each scenario for the lithium battery charger. Sensors or internal state monitoring functions are used to acquire the current operating state of the lithium battery charger in real time. Based on the model characteristics of the lithium battery charger and the requirements of the detection scenarios, it is determined which detection scenarios are applicable to the current operating state. For example, if the lithium battery charger is charging, then current detection and voltage stability detection should be performed.

[0073] Considering that different models of lithium battery chargers have different design characteristics and performance parameters, the specific dynamic operating conditions (such as rated operating conditions, overvoltage operating conditions, and undervoltage operating conditions) are determined based on their model information. At this point, the technical specifications or database of the lithium battery charger are consulted to understand its key parameters such as rated voltage, rated current, overvoltage protection threshold, and undervoltage protection threshold. Based on the model characteristics and the requirements of the testing scenario, specific dynamic operating conditions are defined. For example, the rated operating condition tests the performance and efficiency of the lithium battery charger under standard input voltage and load conditions; the overvoltage operating condition tests the overvoltage protection capability of the lithium battery charger when the input voltage exceeds the rated voltage by a certain percentage; and the undervoltage operating condition tests the undervoltage protection capability and performance of the lithium battery charger when the input voltage is lower than the rated voltage by a certain percentage.

[0074] Based on the dynamic operating conditions determined in the previous steps, set the test conditions and execute the corresponding tests to evaluate the performance and stability of the lithium battery charger under different operating conditions. At this time, use test equipment such as programmable power supplies, electronic loads, oscilloscopes, and thermometers to set test conditions such as input voltage, output current, and ambient temperature according to the requirements of the dynamic operating conditions. Start the test equipment, monitor key parameters such as the output voltage, output current, and temperature of the lithium battery charger, and record the test data. Evaluate the performance and stability of the lithium battery charger under dynamic operating conditions based on the test data, such as charging efficiency, power factor, temperature rise, and protection action time.

[0075] Specifically, suppose there is a lithium battery charger, model XYZ1234, specifically designed for electric forklifts. Its rated voltage is 220V AC (suitable for the mains voltage commonly used by electric forklifts), and its rated output current is a high current suitable for fast charging of electric forklift batteries (e.g., 30A). It also features overvoltage and undervoltage protection to ensure safety during power fluctuations. Currently, this lithium battery charger is charging an electric forklift battery, so it is decided to conduct current and voltage stability tests to verify its performance in a real-world charging scenario. According to the XYZ1234's technical specifications, its overvoltage protection threshold is 242V AC (110% of the rated voltage, considering the use of electric forklifts in outdoor environments with unstable power), and its undervoltage protection threshold is 198V AC (90% of the rated voltage, ensuring that the lithium battery charger or battery will not be damaged under low voltage conditions).

[0076] Based on this information, the following dynamic operating conditions for charging electric forklifts are defined:

[0077] Rated operating conditions: Input voltage is 220V AC, output current is 30A (or the specific charging current required by the electric forklift battery), to test the performance and efficiency of the lithium battery charger and ensure that it can meet the fast charging needs of the electric forklift under normal charging conditions.

[0078] Overvoltage condition: The input voltage is 242V AC, simulating a sudden increase in the mains voltage. The overvoltage protection capability of the lithium battery charger is tested, and the trigger time of the protection and the output voltage after the protection action are recorded to ensure that it can respond quickly and protect the battery and lithium battery charger from damage when the power fluctuates.

[0079] Undervoltage condition: The input voltage is 198V AC, simulating the situation of reduced grid voltage. The undervoltage protection capability of the lithium battery charger is tested, and the trigger time of protection and the output voltage and current after the protection action are recorded to prevent battery damage or performance degradation of the lithium battery charger caused by continued charging under low voltage conditions.

[0080] To perform these tests, a programmable power supply was used to precisely set the input voltage, an electronic load was used to simulate the charging needs of the electric forklift battery and set the output current, and an oscilloscope and thermometer were used to monitor key parameters such as the output voltage, current waveform, and temperature changes of the lithium battery charger in real time. Under rated operating conditions, the output voltage stability, charging efficiency, and temperature rise of the lithium battery charger were recorded to ensure its performance during normal charging. Under overvoltage and undervoltage conditions, special attention was paid to the timing of the lithium battery charger's protection trigger and the output voltage and current after the protection action to verify its protection capability and response speed under abnormal voltage conditions. Through these tests, the performance and stability of the XYZ1234 lithium battery charger under different operating conditions were comprehensively evaluated, ensuring that it can meet the charging needs of electric forklifts, user safety standards, and the requirements of complex outdoor environments in actual use.

[0081] In some embodiments of this application, a dynamic operating condition matching table is collected, as shown in Table 1:

[0082] Table 1 Dynamic Operating Condition Matching Table

[0083]

[0084] The dynamic operating condition matching table lists the detection scenarios (such as voltage detection, current detection, stability detection, etc.), the current operating status of the lithium battery charger (such as charging, standby, long-term operation, etc.), and the lithium battery charger model (such as XYZ1234). For each combination, the corresponding dynamic operating condition is determined. For example, when the lithium battery charger XYZ1234 is charging and performing voltage detection, it is matched as "rated operating condition"; if overvoltage protection detection is performed, it is matched as "overvoltage operating condition".

[0085] In step S13, multiple charging data of the lithium battery charger are collected during the dynamic test process, and the state characteristic curve is determined based on the multiple charging data and the dynamic operating conditions of the lithium battery charger.

[0086] In the specific implementation of this invention, the specific steps are as follows:

[0087] S131: Collect the dynamic operating conditions of the lithium battery charger, determine the corresponding dynamic test method based on the dynamic operating conditions of the lithium battery charger and the matching of the preset operating condition matching table, and trigger the dynamic test of the lithium battery charger along the dynamic test method.

[0088] S132: In the dynamic test of the lithium battery charger, multiple charging data of the lithium battery charger are collected, and a first state curve is determined based on the multiple charging data and the corresponding test time. A second state curve is determined based on the multiple charging data and the dynamic working condition of the lithium battery charger.

[0089] S133: In a lithium battery charger, the state characteristic curve of the lithium battery charger is determined based on the synthesis of the first state curve and the second state curve. At this time, the rated operating condition, overvoltage operating condition and undervoltage operating condition are provided with corresponding state characteristic curves.

[0090] In the embodiments of this application, the dynamic operating conditions of the lithium battery charger are collected, and the corresponding dynamic test method is determined based on the matching of the dynamic operating conditions of the lithium battery charger and the preset operating condition matching table. The dynamic test of the lithium battery charger is triggered along the dynamic test method, which takes into account the overall consideration of the matching of the dynamic operating conditions of the lithium battery charger and the preset operating condition matching table, and ensures the accuracy of the corresponding dynamic test method.

[0091] At this point, the current operating status of the lithium battery charger is acquired in real time, especially its dynamic operating condition information, such as voltage, current, temperature, and whether it is under specific operating conditions such as rated, overvoltage, or undervoltage. The collected dynamic operating condition information is compared with a preset operating condition matching table to determine the most suitable dynamic test method for the current operating condition. According to the dynamic test method, the corresponding test process is initiated to conduct real-time testing on the lithium battery charger. At this time, the test equipment (such as programmable power supply, electronic load, oscilloscope, etc.) is configured to meet the requirements of the dynamic test method. The test equipment is started to begin testing the lithium battery charger. During the test, the key parameters of the lithium battery charger are continuously monitored and the test data is recorded.

[0092] Specifically, a preset operating condition matching table for the pre-lithium battery charger is collected, as shown in Table 2:

[0093] Table 2. Preset Operating Condition Matching Table for Pre-Lithium Battery Chargers

[0094]

[0095] The current dynamic operating condition of the lithium battery charger is collected as "overvoltage condition," meaning the input voltage exceeds the rated voltage by 10%. The voltage sensor monitors in real time that the input voltage of the lithium battery charger has exceeded the rated voltage range, thus identifying it as an "overvoltage condition." The "overvoltage condition" is compared with a preset operating condition matching table to find the corresponding dynamic test method: "simulating an input voltage exceeding the rated voltage by 10% to test the overvoltage protection mechanism." A programmable power supply is configured to simulate the condition of the input voltage exceeding the rated voltage by 10%, and the test equipment is started to begin the overvoltage protection test on the XYZ lithium battery charger. During the test, key parameters such as the output voltage, current, and trigger and recovery times of the overvoltage protection mechanism of the lithium battery charger are monitored, and the test data is recorded.

[0096] Furthermore, in the dynamic testing of the lithium battery charger, multiple charging data points of the lithium battery charger are collected. A first state curve is determined based on the multiple charging data points and the corresponding test time. A second state curve is determined based on the multiple charging data points and the dynamic operating conditions of the lithium battery charger. This comprehensive consideration of multiple charging data points and the dynamic operating conditions of the lithium battery charger ensures the accuracy of the second state curve.

[0097] During dynamic testing, key charging data of the lithium battery charger, such as voltage, current, temperature, and charging time, are collected in real time or periodically. Using the collected charging data and corresponding test times, a first-state curve of the lithium battery charger is plotted, reflecting the performance change trend of the charger throughout the test. The charging data (such as voltage and current) is then used as the vertical axis, and the test time as the horizontal axis. Data visualization tools (such as charting software) are used to plot the curve. The shape, trend, and key points of the curve are analyzed to evaluate the performance of the lithium battery charger.

[0098] By combining charging data and the dynamic operating conditions of the lithium battery charger (such as rated operating conditions, overvoltage operating conditions, and undervoltage operating conditions), a second state curve is plotted. This curve focuses more on showing the specific performance of the lithium battery charger under different operating conditions. At this time, in addition to considering charging data and time, operating condition information is also considered as an additional dimension. For each operating condition, a state curve is plotted separately. By comparing the state curves under different operating conditions, the performance differences of the lithium battery charger under different conditions can be analyzed.

[0099] Specifically, suppose a dynamic test of a lithium battery charger is underway, including rated operating conditions, overvoltage conditions, and undervoltage conditions. During the test, the voltage, current, and temperature data of the lithium battery charger are collected in real time at a sampling rate of 10 times per second. Simultaneously, the test time corresponding to each data point is recorded. With test time as the x-axis and voltage and current as the y-axis, voltage-time curves and current-time curves are plotted respectively, serving as the first state curves of the lithium battery charger. Through these two curves, the voltage and current change trends of the lithium battery charger during the test process are observed, as well as whether any abnormal fluctuations exist.

[0100] Based on the collected data and the dynamic operating conditions of the lithium battery charger, state curves were plotted under rated, overvoltage, and undervoltage conditions. For example, under rated conditions, rated voltage-time and rated current-time curves were plotted; under overvoltage conditions, voltage-time curves before overvoltage protection triggering and recovery current-time curves after overvoltage protection triggering were plotted; under undervoltage conditions, voltage-time curves before undervoltage response and current-time curves after undervoltage recovery were plotted. By comparing these curves, the performance differences of the lithium battery charger under different operating conditions were discovered, such as the triggering speed of the overvoltage protection mechanism, the sensitivity of the undervoltage response, and the recovery capability. Through this analysis, a more comprehensive understanding of the performance of the lithium battery charger under different operating conditions is obtained, providing strong support for product optimization and improvement.

[0101] Therefore, in a lithium battery charger, the state characteristic curve of the lithium battery charger is determined based on the synthesis of the first state curve and the second state curve. At this time, the rated operating condition, overvoltage operating condition and undervoltage operating condition are provided with corresponding state characteristic curves, which is compatible with the overall consideration of the synthesis of the first state curve and the second state curve, ensuring the accuracy of the state characteristic curve of the lithium battery charger. At the same time, dynamic operating conditions are introduced, which is compatible with the overall consideration of multiple charging data and the dynamic operating conditions in which the lithium battery charger is located, ensuring the accuracy of the state characteristic curve, and realizing targeted analysis of rated operating condition, overvoltage operating condition and undervoltage operating condition.

[0102] At this point, the first state curve (reflecting the performance change trend of the lithium battery charger throughout the test process) and the second state curve (showing the specific performance of the lithium battery charger under different operating conditions) are combined to form a more comprehensive state characteristic curve. First, ensure that the time axes of the first and second state curves are aligned, that is, they reflect data within the same time period. Then, as needed, choose to superimpose, average, or combine the values ​​of the two curves to form a composite curve. Considering that the second state curve contains data from multiple operating conditions, the curves under each operating condition need to be processed separately before the overall composite is performed.

[0103] Based on the synthesized curves, the state characteristic curves of the lithium battery charger are determined. These curves comprehensively reflect the performance of the lithium battery charger under different operating conditions. The synthesized curves are then analyzed to identify key characteristic points, such as peak values, valley values, and inflection points in the trend. Based on these characteristic points, key performance indicators of the lithium battery charger are extracted, such as charging efficiency, stability, and the response speed of the protection mechanism. These performance indicators are then compared with the design specifications and industry standards of the lithium battery charger to assess whether its performance meets the requirements.

[0104] For different operating conditions of lithium battery chargers, corresponding state characteristic curves are set to analyze their performance under various conditions in a more detailed manner. Then, for each operating condition, the above process is repeated to synthesize and determine the state characteristic curves respectively. Ensure that the state characteristic curves under each operating condition reflect the specific performance under that operating condition. Organize and archive these state characteristic curves for subsequent analysis and comparison.

[0105] Specifically, assuming a lithium battery charger is being tested, and its first-state curves and second-state curves have been obtained under rated, overvoltage, and undervoltage conditions; for the rated condition, the voltage-time curve (first-state curve) and the efficiency-time curve at rated current (part of the second-state curve) are synthesized to form the composite curve for the rated condition; similarly, corresponding composite curves are synthesized for the overvoltage and undervoltage conditions; for the composite curve of the rated condition, it is observed that the voltage fluctuates stably within a certain range, and the efficiency curve also remains at a relatively stable level. This indicates that the lithium battery charger has good stability and efficiency under rated operating conditions. For the composite curves of overvoltage and undervoltage conditions, the focus was on indicators such as the triggering speed and recovery capability of the protection mechanism. Corresponding state characteristic curves were set. Corresponding state characteristic curves were set for rated operating conditions, overvoltage conditions, and undervoltage conditions, and these curves were organized and archived. By comparing these curves, the performance differences of the lithium battery charger under different operating conditions were found, such as the rapid triggering and recovery capability of the protection mechanism under overvoltage conditions, and the sensitivity and response speed of the lithium battery charger to voltage fluctuations under undervoltage conditions.

[0106] In step S14, multiple state characteristic curves under different dynamic working conditions are collected, and the combination of change regions is determined by comparing the multiple state characteristic curves. The combination of change regions contains multiple change regions within the same time range.

[0107] In the specific implementation of this invention, the specific steps are as follows:

[0108] S141: Real-time monitoring of the dynamic test of the lithium battery charger, collecting the corresponding state characteristic curves of the lithium battery charger under rated operating conditions, overvoltage operating conditions and undervoltage operating conditions; transmitting the corresponding state characteristic curves of rated operating conditions, overvoltage operating conditions and undervoltage operating conditions to the same curve frame, and comparing the corresponding state characteristic curves of rated operating conditions, overvoltage operating conditions and undervoltage operating conditions in the curve frame.

[0109] S142: Real-time monitoring of rated operating conditions, overvoltage operating conditions and undervoltage operating conditions, with corresponding characteristic curves for each condition, and determination of multiple curve change segments based on the comparison of the corresponding characteristic curves for rated operating conditions, overvoltage operating conditions and undervoltage operating conditions.

[0110] S143: Determine the corresponding change region based on multiple curve change segments and corresponding time nodes, collect multiple change regions, and determine the change region combination based on the combination of change regions. The change region combination contains multiple change regions within the same time range.

[0111] In the embodiments of this application, the dynamic testing of the lithium battery charger is monitored in real time, and the corresponding state characteristic curves of the lithium battery charger under rated operating conditions, overvoltage operating conditions, and undervoltage operating conditions are collected. The corresponding state characteristic curves under rated operating conditions, overvoltage operating conditions, and undervoltage operating conditions are transmitted to the same curve frame, and the corresponding state characteristic curves under rated operating conditions, overvoltage operating conditions, and undervoltage operating conditions are compared in the curve frame. The comparison of the corresponding state characteristic curves under rated operating conditions, overvoltage operating conditions, and undervoltage operating conditions is introduced in the curve frame.

[0112] During the dynamic testing of the lithium battery charger, its operating status is monitored in real time to ensure that performance data under different operating conditions can be obtained immediately. State characteristic curves are collected for each operating condition (rated condition, overvoltage condition, undervoltage condition) for subsequent analysis and comparison. Under each condition, tests are conducted according to preset test conditions and procedures. Performance parameters of the lithium battery charger, such as voltage and current changes over time, are recorded in real time. Data visualization tools (such as charting software) are used to plot the collected data as state characteristic curves, which should clearly reflect the performance change trend of the lithium battery charger under different operating conditions.

[0113] Integrate the state characteristic curves under different operating conditions into the same curve frame for intuitive comparison and analysis. At this time, select an appropriate data visualization tool to ensure that it can support the overlay display of multiple curves. Import the state characteristic curves under rated operating conditions, overvoltage operating conditions, and undervoltage operating conditions into the same curve frame, ensuring that the time axis is aligned. Adjust the color, line type, and other attributes of the curves to distinguish the curves under different operating conditions. Carefully observe the shape, trend, and key points of the curves to analyze the performance differences and potential problems of lithium battery chargers under different operating conditions.

[0114] Specifically, suppose a dynamic test of a lithium battery charger is underway. During the test, the voltage and current data of the lithium battery charger are monitored in real time, and its state characteristic curves under rated operating conditions, overvoltage operating conditions, and undervoltage operating conditions are collected respectively. Under rated operating conditions, the voltage and current curves of the lithium battery charger remain stable, with voltage fluctuations within ±5% and current fluctuations within ±3%. This indicates that the lithium battery charger has good stability and performance under normal operating conditions.

[0115] Under overvoltage conditions, the voltage curve of the lithium battery charger rises rapidly and exceeds the rated voltage value, but then the overvoltage protection mechanism is triggered, and the voltage drops rapidly to the safe range; the current curve fluctuates slightly during the voltage rise, but quickly returns to stability after the overvoltage protection is triggered. This shows that the lithium battery charger has an effective overvoltage protection mechanism that can protect the battery and the lithium battery charger itself from damage under overvoltage conditions.

[0116] Under under-voltage conditions, the voltage curve of the lithium battery charger is lower than the rated voltage, and the current curve fluctuates. However, as the charging process progresses, the voltage gradually rises and approaches the rated voltage, and the current gradually stabilizes. This indicates that the lithium battery charger can still maintain a certain charging capacity under under-voltage conditions, but its performance is affected to some extent. By integrating the state characteristic curves of these three operating conditions into the same curve framework for comparison, the performance differences and potential problems of the lithium battery charger under different operating conditions can be clearly seen. For example, under over-voltage conditions, the voltage and current of the lithium battery charger fluctuate greatly, but it can quickly recover and stabilize after triggering the over-voltage protection mechanism. Under under-voltage conditions, the performance of the lithium battery charger is affected to some extent, requiring further optimization and improvement.

[0117] Furthermore, the system provides real-time monitoring of the corresponding characteristic curves for rated operating conditions, overvoltage operating conditions, and undervoltage operating conditions. Based on the comparison of these characteristic curves, multiple curve change segments are determined. This system incorporates the overall consideration of comparing the corresponding characteristic curves for rated operating conditions, overvoltage operating conditions, and undervoltage operating conditions, ensuring the accuracy of multiple curve change segments.

[0118] At this point, during the real-time monitoring of the dynamic testing of the lithium battery charger, the state characteristic curves under rated operating conditions, overvoltage operating conditions, and undervoltage operating conditions are compared to identify the performance differences and trends of the lithium battery charger under different operating conditions. It is ensured that the state characteristic curves under all operating conditions have been imported into the same curve framework and that the time axis is aligned. Data visualization tools (such as charting software) are used to display and update the curves in real time to observe the changes in key performance parameters such as voltage and current of the lithium battery charger under different operating conditions. The curves under different operating conditions are compared, paying attention to the shape, trend, fluctuation range, and any abnormal points or inflection points.

[0119] Based on the comparison results of the state characteristic curves, multiple curve change segments are identified. These curve change segments reflect the key change points or ranges of the lithium battery charger's performance under different operating conditions. At this time, the fluctuations and trend changes of the curves are carefully observed to identify significant change points or ranges of performance parameters such as voltage and current. For each significant change point or range, its start and end time points, as well as the corresponding performance parameter change range, are determined. These change segments are marked and recorded for subsequent analysis and optimization. It is important to distinguish between normal performance changes (such as lithium battery charger startup, charging completion, etc.) and abnormal performance changes (such as overvoltage protection triggering, abnormal current fluctuations, etc.).

[0120] Specifically, suppose we are conducting dynamic testing on a lithium battery charger designed specifically for electric forklifts, and we are monitoring its state-of-the-art characteristics under rated, overvoltage, and undervoltage conditions in real time. When comparing these curves, we identified the following key curve variation segments:

[0121] Stable charging range under rated operating conditions:

[0122] Time range: 0-10 minutes (or adjusted according to specific test conditions); Voltage variation: stabilizes near the rated voltage value suitable for the electric forklift, with fluctuations less than the preset safety threshold (e.g., ±5%, but the specific value varies depending on the design of the electric forklift battery and lithium battery charger); Current variation: based on the charging requirements of the electric forklift battery and the design of the lithium battery charger, the current is gradually adjusted to a stable charging current value, with fluctuations also remaining within the preset safety range (e.g., ±3%); Note: Under rated operating conditions, the lithium battery charger exhibits good stability and performance, meeting the fast and safe charging needs of the electric forklift.

[0123] Protection triggering stage under overvoltage conditions:

[0124] Time Range: Assume overvoltage protection is triggered after 15 minutes of testing (the exact time varies depending on test conditions); Voltage Change: The voltage rapidly rises to the overvoltage protection threshold (e.g., 110% or higher of the rated voltage, the specific value depends on the lithium battery charger design), and then rapidly drops to a safe range to avoid damage to the battery and lithium battery charger; Current Change: The current will fluctuate during the voltage rise, but should quickly stabilize after the overvoltage protection is triggered; Note: The lithium battery charger has an effective overvoltage protection mechanism that can protect the battery and the lithium battery charger itself from damage under power fluctuations or abnormal conditions, ensuring the charging safety of the electric forklift.

[0125] Performance recovery phase under undervoltage conditions:

[0126] Time range: Assuming charging begins after 30 minutes of testing (the exact time depends on the test settings), the voltage approaches the rated voltage at 40 minutes. Voltage variation: Starting below the rated voltage, the voltage gradually increases until it approaches or reaches the rated voltage suitable for the electric forklift. Current variation: In the initial stage of charging, the current fluctuates significantly due to battery status or the lithium battery charger's adjustment strategy, but it gradually stabilizes as charging progresses. Note: Although the performance of the lithium battery charger is somewhat affected under under-voltage conditions, it still maintains a certain charging capacity. By optimizing the charging algorithm, improving the adaptability of the lithium battery charger to low-voltage environments, or adopting other improvement measures, its performance under under-voltage conditions can be further enhanced to meet the charging needs of electric forklifts.

[0127] Therefore, the corresponding change regions are determined based on multiple curve change segments and corresponding time nodes. Multiple change regions are collected, and a combination of change regions is determined based on the combination of change regions. The combination of change regions contains multiple change regions within the same time range, which takes into account the overall consideration of multiple curve change segments and corresponding time nodes, and ensures the accuracy of the corresponding change regions.

[0128] At this point, based on the multiple curve change segments and their corresponding time nodes identified in step S142, the change region corresponding to each change segment is determined. These change regions reflect the specific changes in the performance of the lithium battery charger over different time periods. Then, the curve change segments identified in step S142 are reviewed, including their start and end time nodes. The start and end points of these change segments are marked on the time axis to define the time range of each change region. Note the overlap between change regions, i.e., the situation where multiple performance parameters change within the same time range.

[0129] Collect all identified areas of change for subsequent combination and analysis; at the same time, record information about each area of ​​change (such as time range, performance parameter changes, etc.); ensure that the collected areas of change are complete and accurate, including those that overlap or are related to each other; use data tables, graphical interfaces or other data management tools to organize and store the information of these areas of change.

[0130] Multiple collected change regions are combined to form a comprehensive view containing changes in multiple performance parameters within the same time frame. This helps identify the overall trend and potential problems of lithium battery charger performance under different operating conditions. At this point, the temporal overlap and performance parameter correlation between change regions are analyzed to determine which regions should be reasonably combined together. Change region combinations are created, with each combination containing one or more performance parameter changes occurring within the same time frame. It is important to maintain the logic and clarity of the combinations for subsequent analysis and interpretation. Data visualization tools (such as charts, heatmaps, etc.) are used to display the change region combinations to more intuitively understand the changes in lithium battery charger performance.

[0131] Specifically, assuming a dynamic test of a lithium battery charger is underway, and the following curve variation segments have been identified according to step S142: stable charging segment under rated operating conditions (time range: 0-10 minutes); protection trigger segment under overvoltage conditions (time range: 15-20 minutes); performance recovery segment under undervoltage conditions (time range: 30-40 minutes); now, according to step S143, determine and collect the variation regions corresponding to these variation segments, and form a combination of variation regions; Variation region 1: time range 0-10 minutes, corresponding to the stable charging segment under rated operating conditions; Variation region 2: time range 15-20 minutes, corresponding to the protection trigger segment under overvoltage conditions; Variation region 3: time range 30-40 minutes, corresponding to the performance recovery segment under undervoltage conditions.

[0132] Information on the three variation regions mentioned above has been collected and recorded. Since the three variation regions do not overlap in time, they are considered as independent combinations of variation regions. However, in practical applications, if there is time overlap or performance parameter correlation, these variation regions are combined to form a more comprehensive view. For example, if it is found that the lithium battery charger still exhibits abnormal voltage or current fluctuations for a period of time (e.g., 20-25 minutes) after the overvoltage protection is triggered, this time period is combined with the protection triggering period under overvoltage conditions (15-20 minutes) to form a combination of variation regions that includes the overvoltage protection trigger and its subsequent effects. In this specific example, since the three variation regions do not overlap in time and are independent of each other, they are considered as three independent combinations of variation regions. However, please note that in practical applications, the correlation and combination between variation regions are more complex and diverse.

[0133] In some embodiments of this application, a table of performance parameter changes for the pre-lithium battery charger is collected, as shown in Table 3:

[0134] Table 3 Performance Parameter Changes

[0135]

[0136] This performance parameter variation table lists different variation area numbers, their time ranges, and corresponding performance parameter variation descriptions. These variation areas are determined based on the curve variation segments identified in the previous steps.

[0137] In step S15, the actual output voltage is determined based on multiple changing regions, and abnormal nodes are determined based on multiple actual output voltages, the voltage of the preset charging stage, and dynamic operating conditions. Abnormal factors are identified by tracing the abnormal nodes.

[0138] In the specific implementation of this invention, the specific steps are as follows:

[0139] S151: Collect multiple change areas, determine multiple voltage data based on the identification of multiple change areas, determine multiple actual output voltages based on the filtering of multiple voltage data, and the multiple actual output voltages are at different time nodes.

[0140] S152: Collect past usage records of the lithium battery charger to determine multiple charging events, determine the voltage of the preset charging stage based on the detection of multiple charging events, determine the first abnormality coefficient based on multiple actual output voltages and the voltage of the preset charging stage, and determine the second abnormality coefficient based on multiple actual output voltages and dynamic operating conditions.

[0141] S153: Determine the corresponding abnormal node based on the first abnormal coefficient, the second abnormal coefficient, and the abnormal node mapping relationship, and trigger the tracing of the abnormal node. Determine the detection event of the lithium battery charger at the corresponding time node based on the tracing of the abnormal node, and determine the corresponding abnormal factor based on the identification of the detection event.

[0142] In the embodiments of this application, data is collected from multiple variation regions identified in the previous steps (such as S143). These variation regions reflect the performance changes of the lithium battery charger under different operating conditions or time periods. At this time, the variation regions identified in the previous steps are reviewed and confirmed to ensure that their time range and performance parameter changes are clear. Data acquisition tools or devices are used to collect relevant data such as voltage and current from the variation regions. These data come from the built-in sensors of the lithium battery charger or external measuring devices. It is ensured that the collected data has sufficient time resolution and accuracy for subsequent analysis and processing.

[0143] Identify voltage-related data points from the collected data. At this point, preprocess the collected data, such as removing noise and smoothing the data, to improve data quality. Based on the data timestamp and voltage value, extract voltage-related data points from the data. These data points correspond to the output voltage values ​​of the lithium battery charger under different operating conditions. Record the time node of each voltage data point so that it can be matched with the actual output voltage later.

[0144] Representative actual output voltage values ​​are selected from the extracted voltage data. Simultaneously, the trends and stability of the voltage data are analyzed to determine which data points accurately reflect the output voltage of the lithium battery charger. Based on actual needs, the average, peak, valley, or specific time point values ​​of the voltage data are selected as the actual output voltage. It is ensured that the selected actual output voltage values ​​are representative and accurately reflect the performance status of the lithium battery charger at different time points. The time point corresponding to each actual output voltage value is recorded for subsequent analysis and comparison.

[0145] Specifically, suppose a performance test of a lithium battery charger is underway, and the following two variation zones have been determined based on previous steps: Variation Zone 1: time range 0-10 minutes, corresponding to the startup and initial charging phase of the lithium battery charger; Variation Zone 2: time range 15-25 minutes, corresponding to the constant voltage charging phase of the lithium battery charger; Now, data acquisition and processing are performed according to step S151: voltage data is acquired from variation zone 1 and variation zone 2; assuming a high-precision voltage measurement device is used, and sampling is performed at 1-second intervals; voltage-related data points are extracted from the acquired data; for example, in variation zone 1, all voltage data points between 0 seconds and 10 minutes (i.e., 0 seconds to 600 seconds) are extracted; Similarly, corresponding voltage data points were extracted in variation region 2. The extracted voltage data were filtered to determine the actual output voltage value. Assuming that the voltage fluctuated during the startup phase in variation region 1 but stabilized after 30 seconds, the average voltage after 30 seconds was selected as the actual output voltage value V1 for this phase. Similarly, in variation region 2, the voltage remained stable throughout the constant voltage charging phase, so the average voltage for this phase was selected as the actual output voltage value V2. Finally, two actual output voltage values, V1 and V2, were obtained, corresponding to different time points (i.e., the midpoint or representative time point of variation region 1 and variation region 2). These actual output voltage values ​​were used for subsequent performance analysis and comparison.

[0146] Furthermore, the data storage system of the lithium battery charger is accessed, which typically records past usage data. Information related to charging events is extracted, such as charging start and end times, charging current, charging voltage, and ambient temperature. The integrity and accuracy of the data are ensured for subsequent analysis. Based on the lithium battery charger's design specifications or past experience, voltage values ​​for different charging stages are preset for each charging event. At this point, the charging curve or charging protocol of the lithium battery charger is analyzed to understand the voltage characteristics of different charging stages (such as constant current charging and constant voltage charging). Based on this information, voltage values ​​for the corresponding charging stages are preset for each charging event; these preset values ​​are typically based on the lithium battery charger's rated output voltage and charging strategy.

[0147] The difference between the actual output voltage and the preset charging stage voltage is compared and quantified to assess the performance anomaly of the lithium battery charger. For each charging event, the actual measured output voltage is compared with the preset charging stage voltage. The difference value is calculated and standardized or normalized as needed to obtain a first anomaly coefficient. The first anomaly coefficient reflects the degree of deviation between the lithium battery charger output voltage and the preset value.

[0148] To further quantify performance anomalies of lithium battery chargers, the impact of dynamic operating conditions on the output voltage of the charger is considered. This involves analyzing dynamic operating conditions during charging events, such as changes in ambient temperature and charging current. Based on these dynamic changes, the comparison benchmark between the actual output voltage and the preset voltage is adjusted or corrected. A second anomaly coefficient is calculated, which comprehensively considers the effects of the actual output voltage, the preset voltage, and the dynamic operating conditions. This second anomaly coefficient provides a more comprehensive performance evaluation, taking into account the adaptability of the lithium battery charger under different operating conditions.

[0149] Specifically, suppose there is a lithium battery charger specifically designed for electric forklifts, whose charging process includes two stages: constant current charging and constant voltage charging. Usage records of this lithium battery charger charging electric forklifts over the past week were collected, and the following two charging events were identified: Charging Event 1: Occurred on Monday, with an ambient temperature of 25°C. During the constant current charging stage, the lithium battery charger's output current was set to a specific value suitable for the electric forklift, and the preset charging voltage was a voltage suitable for the electric forklift's battery, such as 48V (this value may be adjusted depending on the battery type and specifications). When the battery approached full charge, the lithium battery charger switched to the constant voltage charging stage, at which point the voltage remained constant at 48V, while the current gradually decreased. Charging Event 2: Occurred on Thursday, with the ambient temperature rising to 30°C. During the constant current charging stage, the lithium battery charger still output the corresponding current, but the actual measured charging voltage was 47.5V, slightly lower than the preset value. After entering the constant voltage charging stage, the actual measured charging voltage was 47.8V.

[0150] Now, proceed with the calculations and evaluation according to the analysis steps:

[0151] Extract detailed information for charging events 1 and 2 from the data records, including charging time, ambient temperature, charging current, preset voltage, and actual measured voltage value. Confirm the preset voltage values ​​for the constant current charging and constant voltage charging stages in charging events 1 and 2, which are 48V and 48V respectively in this example. For charging event 1, there is no deviation since the actual measured voltage matches the preset voltage. For charging event 2, calculate the voltage deviation percentages for the constant current charging and constant voltage charging stages. The voltage deviation for the constant current charging stage is |(47.5V - 48V)| / 48V ≈ 1.04%, and the voltage deviation for the constant voltage charging stage is |(47.8V - 48V)| / 48V ≈ 0.42%.

[0152] Since changes in ambient temperature affect the output voltage of lithium battery chargers, preset values ​​need to be adjusted or the reasonableness of voltage deviations needs to be evaluated based on the impact of temperature on voltage. Assuming that the lithium battery charger is designed with a certain temperature compensation mechanism, in this scenario, it is assumed that for every 1°C increase, the output voltage of the lithium battery charger will decrease by 0.05V (this value will vary depending on the specific design of the lithium battery charger). Therefore, at 30°C, the preset constant current charging and constant voltage charging stage voltages should be adjusted to 46.5V and 46.5V respectively. However, since the actual temperature compensation capability of the lithium battery charger is unknown, the voltage deviation will continue to be evaluated based on the original preset values.

[0153] Although hypothetical adjustments were made for the effects of temperature, the actual analysis focused primarily on the actual performance of the lithium battery charger under given conditions. The voltage deviation in charging event 2 indicates that the output voltage of the lithium battery charger decreased slightly under high-temperature conditions. This decrease is normal, but it also suggests that the lithium battery charger needs better temperature management or calibration. For heavy equipment such as electric forklifts, ensuring the stability and accuracy of the charging process is crucial. Therefore, it is recommended to regularly check and calibrate the lithium battery charger to adapt to different environmental conditions.

[0154] Therefore, based on the mapping relationship between the first anomaly coefficient, the second anomaly coefficient, and the anomaly node, the corresponding anomaly node is determined, and the tracing of the anomaly node is triggered. Based on the tracing of the anomaly node, the detection event of the lithium battery charger at the corresponding time node is determined. Based on the identification of the detection event, the corresponding anomaly factor is determined. This approach takes into account the overall consideration of the mapping relationship between the first anomaly coefficient, the second anomaly coefficient, and the anomaly node, ensuring the accuracy of the corresponding anomaly node. At the same time, a combination of change regions is introduced, and multiple state characteristic curves are further analyzed. This approach takes into account the overall consideration of multiple actual output voltages, preset charging stage voltages, and dynamic operating conditions, ensuring the detection accuracy of the anomaly node and controlling the anomaly factor.

[0155] At this point, based on the first anomaly coefficient, the second anomaly coefficient, and the preset anomaly node mapping relationship, abnormal nodes or components in the lithium battery charger are identified. The anomaly node mapping relationship is a predefined rule or model that associates the anomaly coefficient with a specific node or component of the lithium battery charger. The first and second anomaly coefficients are analyzed to determine if they exceed a preset threshold or range. Based on the magnitude and combination of the anomaly coefficients, the corresponding anomaly node is searched in the anomaly node mapping relationship. Anomaly nodes include the power management module, battery management system, and heat dissipation system of the lithium battery charger.

[0156] Detailed tracing and analysis are performed on the identified abnormal nodes to determine the specific cause of the problem. At this point, the tracing mechanism is activated to collect historical data, log information, configuration parameters, etc., related to the abnormal nodes. This data is analyzed to identify when and under what conditions the abnormal nodes encountered problems. If necessary, a physical inspection is performed on the abnormal nodes to observe their appearance, connection status, temperature, etc. Comparison is made with the design specifications of the lithium battery charger and the documents provided by the manufacturer to verify whether the performance of the abnormal nodes meets the requirements.

[0157] Based on the tracing results, specific detection events of the lithium battery charger at the corresponding time points were identified, and these events led to the occurrence of anomaly coefficients. At the same time, the data and logs collected during the tracing process were analyzed to identify specific events related to the anomaly nodes. These events included overcharging, over-discharging, short circuits, and overheating, which negatively affected the performance of the lithium battery charger. The specific time points of these events were determined in order to conduct correlation analysis with the anomaly coefficients.

[0158] Based on the identification results of the detected events, the specific factors causing the abnormal performance of the lithium battery charger are determined; the correlation between the detected events and the design, manufacturing process, and usage environment of the lithium battery charger is analyzed; external factors, such as power grid fluctuations, battery aging, and changes in ambient temperature, are considered; and all information is combined to determine the main factors causing the abnormal performance of the lithium battery charger. These main factors guide subsequent remedial measures and improvement suggestions.

[0159] Specifically, suppose there is a lithium battery charger. In the previous steps, the first abnormality coefficient and the second abnormality coefficient were determined, and the abnormal node was identified as the power management module of the lithium battery charger according to the abnormal node mapping relationship. The first abnormality coefficient and the second abnormality coefficient both exceeded the preset threshold, indicating that the lithium battery charger has a performance abnormality in the constant voltage charging stage. According to the abnormal node mapping relationship, the abnormal node is identified as the power management module of the lithium battery charger.

[0160] Historical data, log information, and configuration parameters of the power management module were collected. Data analysis revealed that multiple over-temperature warnings were recorded in the power management module's logs near the time points when the abnormal coefficients occurred. Physical inspection of the power management module revealed a significant decrease in the speed of its cooling fan, leading to poor heat dissipation. Based on the tracing results, it was determined that the detection event of the lithium battery charger at the time points when the abnormal coefficients occurred was the overheating of the power management module. The overheating event caused a decrease in the performance of the power management module, thereby affecting the output voltage stability of the lithium battery charger.

[0161] Analysis of the overheating incident revealed that the reduced speed of the cooling fan was due to the accumulation of dust and debris inside the fan. After cleaning the dust and debris from inside the fan, the speed of the cooling fan returned to normal, and the overheating problem of the power management module was resolved. Therefore, the main factor causing the abnormal performance of the lithium battery charger was determined to be the blockage and dust accumulation of the cooling fan.

[0162] In some embodiments of this application, an abnormal node matching table is collected, as shown in Table 4:

[0163] Table 4. Abnormal Node Matching Table

[0164]

[0165] Assuming the first anomaly coefficient is 0.1 (high, exceeding the threshold of 0.05) and the second anomaly coefficient is 0.03 (normal, below the threshold of 0.05), the anomaly node is identified as the "power management module" according to the matching table. A tracing mechanism is initiated to collect historical data and log files from the power management module. It is discovered that the power management module frequently issued overload warnings during recent charging processes. Based on the tracing results, it is determined that an overload event occurred in the power management module at the time the anomaly coefficient appeared. Analysis of the cause of the overload event reveals that it was due to unstable input voltage. Therefore, the anomaly factor is "unstable input voltage."

[0166] In another embodiment of this application, a three-dimensional mapping table of lithium battery SOC (State of Charge), charging current, and voltage is established. The values ​​in this three-dimensional mapping table are actual measured data of the lithium battery. The three-dimensional mapping table of lithium battery SOC, charging current, and voltage is shown in Table 5.

[0167] Table 5: Three-Dimensional Mapping Table of Lithium Battery SOC, Charging Current, and Voltage

[0168]

[0169] Multi-stage testing process:

[0170] Step 1 (Rated Input Voltage Test):

[0171] (1) Configuration parameters: rated AC voltage, frequency, minimum output voltage of lithium battery charger, maximum output voltage of lithium battery charger, rated output current of lithium battery charger, rated capacity of simulated battery, configure the bidirectional programmable DC power supply to battery simulation mode, and initialize the port voltage to the lithium battery voltage when SOC=0.

[0172] (2) The host computer sends a simulated BMS (Battery Management System) command;

[0173] (3) After receiving the charging permission instruction from the host computer, the lithium battery charger begins to output voltage and current as required;

[0174] (4) The bidirectional programmable DC power supply calculates the charging capacity in real time and sends it to the host computer;

[0175] (5) The host computer calculates the current SOC based on the charging capacity, calculates the voltage value of the programmable power supply, and issues instructions to adjust its voltage value in real time.

[0176] (6) The host computer calculates and determines the error and records the data in real time.

[0177] Step 2 (115% input voltage test): After standing for 5 minutes, repeat Step 1 to verify the overvoltage adaptability of the power grid.

[0178] Step 3 (85% input voltage test): After standing for 5 minutes, repeat step 1 to verify the undervoltage adaptability of the power grid.

[0179] Please see Figure 2 , Figure 2 This is a schematic diagram of the structural composition of a detection system for a lithium battery charger based on dynamic operating conditions according to an embodiment of the present invention; the detection system for the lithium battery charger based on dynamic operating conditions includes:

[0180] Test parameter module 21 is used to collect multiple test parameters of the lithium battery charger during the dynamic testing process of the lithium battery charger.

[0181] The dynamic operating condition module 22 is used to determine the testing scenario of the lithium battery charger based on multiple test parameters of the lithium battery charger, and to determine the dynamic operating condition according to the testing scenario and the model of the lithium battery charger; the dynamic operating condition includes rated operating condition, overvoltage operating condition and undervoltage operating condition.

[0182] The state characteristic curve module 23 is used to collect multiple charging data of the lithium battery charger during dynamic testing, and determine the state characteristic curve based on the multiple charging data and the dynamic operating conditions of the lithium battery charger.

[0183] The variable region combination module 24 is used to collect multiple state characteristic curves under different dynamic working conditions, and determine the variable region combination based on the comparison of multiple state characteristic curves. The variable region combination contains multiple variable regions within the same time range.

[0184] The anomaly tracing module 25 is used to determine the actual output voltage based on multiple changing regions, and to determine the abnormal node based on the multiple actual output voltages, the voltage of the preset charging stage and the dynamic operating conditions, and to trace the abnormal factors based on the anomaly node.

[0185] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

Claims

1. A testing method for lithium battery chargers based on dynamic operating conditions, characterized in that, include: During the dynamic testing of the lithium battery charger, multiple test parameters of the lithium battery charger are collected. The testing scenarios for lithium battery chargers are determined based on multiple test parameters, and the dynamic operating conditions are determined according to the testing scenarios and models of lithium battery chargers; the dynamic operating conditions include rated operating conditions, overvoltage operating conditions, and undervoltage operating conditions. Collect multiple charging data points of the lithium battery charger during dynamic testing, and determine the state characteristic curve based on the multiple charging data points and the dynamic operating conditions of the lithium battery charger. Multiple state characteristic curves under different dynamic working conditions are collected, and the combination of change regions is determined by comparing the multiple state characteristic curves. The combination of change regions contains multiple change regions within the same time range. The actual output voltage is determined based on multiple variation regions, and abnormal nodes are identified based on multiple actual output voltages, the voltage of the preset charging stage, and dynamic operating conditions. Abnormal factors are then identified by tracing the abnormal nodes.

2. The detection method for a lithium battery charger based on dynamic operating conditions according to claim 1, characterized in that, During the dynamic testing of the lithium battery charger, multiple test parameters of the lithium battery charger are collected, including: Collect past usage records of the lithium battery charger, determine the past operating parameters of the lithium battery charger based on the past usage records, and determine the current status of the lithium battery charger based on the past operating parameters and the model of the lithium battery charger. The test tasks of the lithium battery charger are collected, and the corresponding test content is determined based on the identification of the test tasks of the lithium battery charger. Based on the corresponding test content and the current state of the lithium battery charger, multiple test parameters of the lithium battery charger are determined. These multiple test parameters include test location, current test parameters, voltage test parameters, and test sequence.

3. The detection method for a lithium battery charger based on dynamic operating conditions according to claim 1, characterized in that, The test scenarios for lithium battery chargers are determined based on multiple test parameters of the lithium battery charger, and the dynamic operating conditions are determined according to the test scenarios and the model of the lithium battery charger. Dynamic operating conditions include rated operating conditions, overvoltage operating conditions, and undervoltage operating conditions, including: Multiple test parameters of the lithium battery charger are collected, multiple test combinations are determined based on the synthesis of the multiple test parameters of the lithium battery charger, and the corresponding test features are determined based on the identification of the multiple test combinations, so as to collect multiple test features; The form and surrounding environment of the lithium battery charger are collected. Based on multiple test characteristics, the form and surrounding environment of the lithium battery charger are used to determine the test scenario of the lithium battery charger. The test scenario of the lithium battery charger includes voltage detection, current detection and stability detection. The dynamic operating conditions are determined based on the testing scenario of the lithium battery charger, the current operating status of the lithium battery charger, and the matching of the lithium battery charger model. These dynamic operating conditions include rated operating conditions, overvoltage operating conditions, and undervoltage operating conditions.

4. The detection method for a lithium battery charger based on dynamic operating conditions according to claim 1, characterized in that, The process involves collecting multiple charging data points from the lithium battery charger during dynamic testing, and determining the state characteristic curve based on these data points and the dynamic operating conditions of the lithium battery charger. This includes: The dynamic operating conditions of the lithium battery charger are collected, and the corresponding dynamic test method is determined based on the matching of the dynamic operating conditions of the lithium battery charger and the preset operating condition matching table. The dynamic test of the lithium battery charger is triggered along the dynamic test method.

5. The detection method for a lithium battery charger based on dynamic operating conditions according to claim 4, characterized in that, The method of collecting multiple charging data points of the lithium battery charger during dynamic testing, and determining the state characteristic curve based on the multiple charging data points and the dynamic operating conditions of the lithium battery charger, also includes: In the dynamic test of lithium battery charger, multiple charging data of lithium battery charger are collected. The first state curve is determined based on the multiple charging data and the corresponding test time. The second state curve is determined based on the multiple charging data and the dynamic working condition of lithium battery charger. In a lithium battery charger, the state characteristic curve of the lithium battery charger is determined based on the synthesis of the first state curve and the second state curve. At this time, the rated operating condition, overvoltage operating condition and undervoltage operating condition are provided with corresponding state characteristic curves.

6. The detection method for a lithium battery charger based on dynamic operating conditions according to claim 1, characterized in that, The process involves collecting multiple state characteristic curves under different dynamic operating conditions, and determining combinations of change regions based on the comparison of these curves. Each combination contains multiple change regions within the same time frame, including: The system monitors the dynamic testing of lithium battery chargers in real time, collecting corresponding characteristic curves for rated, overvoltage, and undervoltage conditions. These characteristic curves are then transmitted to the same curve frame, and a comparison is performed within that frame.

7. The detection method for a lithium battery charger based on dynamic operating conditions according to claim 6, characterized in that, The process involves collecting multiple state characteristic curves under different dynamic operating conditions, determining combinations of change regions based on the comparison of these curves, and including multiple change regions within the same time range in the combination of change regions. The system provides real-time monitoring and comparison of the corresponding characteristic curves for rated operating conditions, overvoltage operating conditions, and undervoltage operating conditions. Based on the comparison of the corresponding characteristic curves for rated operating conditions, overvoltage operating conditions, and undervoltage operating conditions, multiple curve change segments are determined. The corresponding change regions are determined based on multiple curve change segments and corresponding time nodes. Multiple change regions are collected, and a change region combination is determined based on the combination of change regions. The change region combination contains multiple change regions within the same time range.

8. The detection method for a lithium battery charger based on dynamic operating conditions according to claim 1, characterized in that, The actual output voltage is determined based on multiple variation regions, and abnormal nodes are identified based on multiple actual output voltages, preset charging stage voltages, and dynamic operating conditions. Abnormal factors are then identified by tracing these abnormal nodes, including: Multiple variation regions are collected, multiple voltage data are determined based on the identification of multiple variation regions, and multiple actual output voltages are determined based on the filtering of multiple voltage data. The multiple actual output voltages are located at different time points.

9. The detection method for a lithium battery charger based on dynamic operating conditions according to claim 8, characterized in that, The method of determining the actual output voltage based on multiple variation regions, identifying abnormal nodes based on multiple actual output voltages, preset charging stage voltages, and dynamic operating conditions, and determining abnormal factors by tracing these abnormal nodes, also includes: Collect past usage records of lithium battery chargers to identify multiple charging events, determine the voltage of the preset charging stage based on the detection of multiple charging events, determine the first anomaly coefficient based on multiple actual output voltages and the voltage of the preset charging stage, and determine the second anomaly coefficient based on multiple actual output voltages and dynamic operating conditions. The corresponding abnormal node is determined based on the first abnormality coefficient, the second abnormality coefficient, and the mapping relationship between abnormal nodes, and the tracing of the abnormal node is triggered. The detection event of the lithium battery charger at the corresponding time node is determined based on the tracing of the abnormal node, and the corresponding abnormal factor is determined based on the identification of the detection event.

10. A detection system for a lithium battery charger based on dynamic operating conditions, characterized in that, The detection system for the lithium battery charger based on dynamic operating conditions is applied to the detection method for the lithium battery charger based on dynamic operating conditions as described in any one of claims 1-9, wherein the detection system for the lithium battery charger based on dynamic operating conditions includes: The test parameter module is used to collect multiple test parameters of the lithium battery charger during the dynamic testing process. The dynamic operating condition module is used to determine the testing scenario of the lithium battery charger based on multiple test parameters of the lithium battery charger, and to determine the dynamic operating condition according to the testing scenario and the model of the lithium battery charger; the dynamic operating condition includes rated operating condition, overvoltage operating condition and undervoltage operating condition. The state characteristic curve module is used to collect multiple charging data of the lithium battery charger during dynamic testing, and determine the state characteristic curve based on the multiple charging data and the dynamic operating conditions of the lithium battery charger. The variable region combination module is used to collect multiple state characteristic curves under different dynamic working conditions, and determine the variable region combination based on the comparison of multiple state characteristic curves. The variable region combination contains multiple variable regions within the same time range. The anomaly tracing module is used to determine the actual output voltage based on multiple changing regions, and to determine the abnormal node based on the multiple actual output voltages, the voltage of the preset charging stage, and the dynamic operating conditions. The abnormal factors are then determined by tracing the abnormal node.

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