Valve-regulated lead-acid storage battery intelligent matching system for electric bicycle

Abstract

The invention belongs to the technical field of power batteries of electric bicycles, and discloses an intelligent matching system for valve-regulated lead-acid storage batteries for electric bicycles, which comprises an intelligent pre-screening system, an intelligent control system and a power supply system, the intelligent pre-screening system comprises a high-precision battery tester, an intelligent temperature control system, a multi-channel data acquisition system and an automatic transmission device, high-precision matching and dynamic balance management of battery packs are realized, and the consistency of the battery packs is improved through a multi-dimensional parameter test and an intelligent matching algorithm. The service life of the battery pack is prolonged through dynamic equalization management of the self-adaptive equalization system, the production efficiency of the battery pack is improved through cooperative work of the intelligent pre-screening system and the artificial intelligence matching unit, and the reliability of the system is enhanced through dynamic equalization management of the self-adaptive equalization system and full-process monitoring of the quality control system.

Description

Technical Field

[0001] The invention belongs to the technical field of electric bicycle power batteries, and in particular relates to an intelligent assembly system for valve-regulated lead-acid batteries for electric bicycles. Background Art

[0002] As an environmentally friendly and convenient means of transportation, electric bicycles have been widely used around the world in recent years. Their power systems mainly rely on lead-acid batteries, especially valve-regulated lead-acid batteries (VRLA). VRLA batteries have the advantages of low cost, high safety, and simple maintenance, and therefore occupy an important position in the field of electric bicycles. However, with the rapid development of the electric bicycle market, users have increasingly higher requirements for battery performance, especially in terms of battery pack consistency, service life and safety. In addition, VRLA batteries have strict requirements on parameters such as voltage, current, and temperature during the charging and discharging process. Overcharging or over-discharging can lead to problems such as battery plate sulfation and water loss, greatly shortening the battery life.

[0003] The lead-acid battery packs in the prior art mainly have the following problems:

[0004] 1. The test parameters are single and cannot fully reflect the battery performance. The matching method is rough and does not consider the influence of temperature coefficient.

[0005] 2. Lack of intelligent analysis and dynamic balance mechanism, low production efficiency and high cost.

[0006] To this end, we proposed an intelligent matching system for valve-regulated lead-acid batteries for electric bicycles to address the problems existing in the existing technology. Through an intelligent pre-screening system, an intelligent matching algorithm, adaptive balancing technology and production quality control, we have achieved high-precision matching of valve-regulated lead-acid batteries for electric bicycles. This system not only improves the consistency and service life of the battery pack, but also significantly enhances production efficiency and system reliability. Summary of the Invention

[0007] The purpose of the present invention is to provide an intelligent pairing system for valve-regulated lead-acid batteries for electric bicycles, so as to solve the problems mentioned in the above background technology in the prior art, such as single test parameters, inability to fully reflect battery performance, rough pairing methods, failure to consider the influence of temperature coefficients, lack of intelligent analysis and dynamic balancing mechanisms, low production efficiency and high costs.

[0008] To achieve the above object, the present invention adopts the following technical solutions:

[0009] The intelligent assembly system of valve-regulated lead-acid batteries for electric bicycles includes:

[0010] An intelligent pre-screening system for testing and collecting battery parameters, comprising a high-precision battery tester, an intelligent temperature control system, a multi-channel data acquisition system, and an automated transmission device, wherein the intelligent temperature control system is electrically connected to the high-precision battery tester, the multi-channel data acquisition system, and the automated transmission device;

[0011] An artificial intelligence matching unit, used to analyze data and generate a matching plan, comprising a multi-dimensional scoring system, a temperature characteristics matching system, and artificial intelligence matching, wherein the temperature characteristics matching system is electrically connected to the intelligent temperature control system;

[0012] An adaptive balancing system for achieving dynamic balancing of the battery pack, comprising a pre-charge balancing module and a dynamic balancing management module, both of which are electrically connected to a multi-channel data acquisition system;

[0013] The quality control system is used for full-process monitoring and tracing. The quality control system is electrically connected to the intelligent pre-screening system. The quality control system includes a full-process tracing module, a real-time monitoring module, an intelligent early warning module and a data statistical analysis module.

[0014] Furthermore, the high-precision battery tester is used for open circuit voltage test, internal resistance test, 2-hour rate capacity test and dynamic response test of the battery;

[0015] The intelligent temperature control system is used to accurately control the temperature of the test environment to ensure consistency in battery performance testing under different temperature conditions;

[0016] The multi-channel data acquisition system is used to collect various test data of the battery in real time;

[0017] The automated transport device is used for loading, unloading and transporting batteries, ensuring that the batteries can accurately enter the test position and complete the test process.

[0018] Furthermore, the open circuit voltage test is conducted at least three times and the average value is taken, the internal resistance test is conducted at least 5 times and the maximum value is removed to take the average value, the 2-hour rate capacity test is used to detect the battery capacity difference during the battery charging and discharging process, and the dynamic response test is used for pulse discharge during the battery charging and discharging process, and the test results are transmitted to the channel data acquisition system through the digital interface.

[0019] Furthermore, the multi-dimensional scoring system includes a parameter acquisition module, a weight distribution module, a temperature characteristic matching module, a scoring calculation module and a data storage and output module, wherein the temperature characteristic matching module is provided inside the temperature characteristic matching system;

[0020] The artificial intelligence combination includes a data input module, a deep learning model module and an algorithm module.

[0021] Furthermore, the parameter acquisition module is used to collect test parameters of the open circuit voltage test, internal resistance test, 2-hour rate capacity test and dynamic response test of the high-precision tester;

[0022] The weight allocation module is used to allocate corresponding weights according to the degree of influence of different battery parameters on the performance of the battery pack;

[0023] The temperature characteristic matching module is used to match the performance of the battery at different temperatures. The temperature characteristic matching module mainly tests the temperature change of the battery's capacity and internal resistance at 0°C, 25°C, and 40°C.

[0024] The scoring calculation module scores the battery by weight distribution and temperature characteristic matching results. The data storage output module is used to store the scoring results and transmit the data to the intelligent matching algorithm module through the data input module. The multi-dimensional parameters of the structure are analyzed through the deep learning algorithm module.

[0025] Furthermore, the pre-charging equalization module is provided with a three-stage charging strategy, wherein the first and second stages of the three-stage charging strategy both adopt constant current charging, and the third stage of the three-stage charging strategy adopts constant voltage charging and reduces the current value;

[0026] Phase 1: 0.1C 10 A is charged with constant current to 13.8V;

[0027] The second stage: 0.05C 10 A is charged with constant current to 14.4V;

[0028] Stage 3: Charge at a constant voltage of 14.4V until the current drops to 0.01C 10 A;

[0029] When the voltage difference threshold value of the single cells in the battery pack is greater than 20mV, equalization charging is triggered.

[0030] Furthermore, the dynamic balancing management module is used to detect the voltage, current and temperature changes of each single cell in the battery pack in real time. The sampling frequency, voltage progress, current accuracy and temperature accuracy of the dynamic balancing management module are 1Hz, ±1mV, ±10mA and ±0.1℃ respectively.

[0031] Furthermore, the full-process traceability module is used to manage battery raw material batches, record production parameters, store test data, and trace matching plans, and bind the data to the battery's unique identification code to trace the data in real time.

[0032] Furthermore, the real-time monitoring module is used to detect the voltage parameters, current parameters, temperature parameters and humidity parameters during the battery production process in real time. The voltage parameters, current parameters, temperature parameters and humidity parameters of the real-time monitoring module are ±1mV, ±10mA, ±0.1℃ and ±5%RH respectively.

[0033] Furthermore, the intelligent early warning module is used to monitor the real-time monitoring module, and the intelligent early warning module includes a threshold anomaly detection function, an automatic alarm function and an early warning recording function;

[0034] The data statistical analysis module is used to analyze the test data in the battery production process and generate trend predictions.

[0035] The intelligent assembly system for valve-regulated lead-acid batteries for electric bicycles proposed in this invention has the following advantages over the prior art:

[0036] The present invention achieves high-precision battery pack matching and dynamic balance management through the coordinated work of the intelligent pre-screening system, the artificial intelligence matching unit, the adaptive balancing system and the quality control system. It improves the consistency of the battery pack through multi-dimensional parameter testing and the intelligent matching algorithm. It extends the service life of the battery pack through the dynamic balancing management of the adaptive balancing system. It improves the production efficiency of the battery pack through the coordinated work of the intelligent pre-screening system and the artificial intelligence matching unit. It enhances the reliability of the system through the dynamic balancing management of the adaptive balancing system and the full-process monitoring of the quality control system. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 A block diagram of an intelligent assembly system for valve-regulated lead-acid batteries for electric bicycles according to an embodiment of the present invention is shown;

[0038] Figure 2 A flow chart of a system for intelligent grouping of valve-regulated lead-acid batteries for electric bicycles according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0039] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. The specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0040] The present invention provides Figure 1-2 The valve-regulated lead-acid battery intelligent assembly system for electric bicycles shown includes:

[0041] An intelligent pre-screening system for testing and collecting battery parameters, comprising a high-precision battery tester, an intelligent temperature control system, a multi-channel data acquisition system, and an automated transmission device, wherein the intelligent temperature control system is electrically connected to the high-precision battery tester, the multi-channel data acquisition system, and the automated transmission device;

[0042] An artificial intelligence matching unit, used to analyze data and generate a matching plan, comprising a multi-dimensional scoring system, a temperature characteristics matching system, and artificial intelligence matching, wherein the temperature characteristics matching system is electrically connected to the intelligent temperature control system;

[0043] An adaptive balancing system for achieving dynamic balancing of the battery pack, comprising a pre-charge balancing module and a dynamic balancing management module, both of which are electrically connected to a multi-channel data acquisition system;

[0044] A quality control system for full-process monitoring and tracing, which is electrically connected to the intelligent pre-screening system and includes a full-process tracing module, a real-time monitoring module, an intelligent early warning module, and a data statistical analysis module;

[0045] The intelligent pre-screening system transmits the test data to the data input module of the artificial intelligence matching unit through the multi-channel data acquisition system. The matching plan generated by the artificial intelligence matching unit is transmitted to the pre-charge matching module and dynamic matching management module of the adaptive matching system through the data interface. The adaptive matching system performs pre-charge matching and dynamic matching management on the battery pack according to the matching plan to ensure that the battery pack maintains dynamic balance during use.

[0046] The quality control system exchanges data with the intelligent pre-screening system, artificial intelligence matching unit, and adaptive balancing system through the full-process traceability module, and monitors and records the operating status and data of each module in real time. The quality control system is connected to the multi-channel data acquisition system of the intelligent pre-screening system and the dynamic balancing management module of the adaptive balancing system through the real-time monitoring module, and collects battery parameters such as voltage, current, and temperature in real time. The quality control system is connected to the artificial intelligence matching unit through the intelligent early warning module. When an abnormality is detected, it automatically adjusts the matching plan or issues an early warning prompt. Data transmission between modules is achieved through high-speed data interfaces (such as Ethernet, USB, CAN bus, etc.) to ensure the real-time and accuracy of the data.

[0047] The high-precision battery tester is used for open circuit voltage testing, internal resistance testing, 2-hour rate capacity testing, and dynamic response testing of batteries. The high-precision tester uses multiple sets of high-precision voltage sensors and high-precision current sensors to detect voltage and current changes.

[0048] The intelligent temperature control system is used to accurately control the temperature of the test environment to ensure consistency in battery performance testing under different temperature conditions. The intelligent temperature control system can simulate different ambient temperatures to ensure accurate measurement of battery performance parameters at high, normal, and low temperatures. The intelligent temperature control system has built-in high-precision digital temperature sensors, electric heating elements, and fans. It can use high-precision digital temperature sensors to monitor temperature changes in real time and control the electric heating elements for temperature regulation. At the same time, the fan uses air circulation to ensure uniform temperature.

[0049] The multi-channel data acquisition system is used to collect various test data of the battery in real time. The sensor group includes a voltage sensor and a current sensor, which can process the test data of multiple batteries simultaneously to ensure the efficiency of the test process and the integrity of the data.

[0050] The automated transport device is used for loading, unloading and transporting batteries, ensuring that the batteries can accurately enter the test position and complete the test process. Through high-precision positioning and transport, manual operation errors are reduced and test efficiency is improved.

[0051] The open circuit voltage test is conducted at least three times and the average value is taken. The internal resistance test is conducted at least five times and the average value is taken after removing the maximum value. The 2-hour rate capacity test is used to detect the battery capacity difference during the battery charging and discharging process. The dynamic response test is used for pulse discharge during the battery charging and discharging process. The test results are transmitted to the channel data acquisition system through a digital interface.

[0052] During the open circuit test, the battery is left at rest for 6 hours at 25.0±0.5℃ to ensure that oxygen recombination is fully completed. The system uses a high-precision battery tester to measure the open circuit voltage of the battery with an accuracy of ±0.1mV. The test is repeated three times, and the average value is taken as the final result. The acceptance standard is that the open circuit voltage value should be within the range of 12.80±0.01V and the temperature coefficient should be -2.5mV / ℃.

[0053] During the internal resistance test, the battery was tested at 25.0±0.5℃. The test frequencies included 0.1Hz, 1Hz, 10Hz, 100Hz and 1kHz. The test current was 1A. The AC superposition system used a multi-channel data acquisition system to measure the internal resistance of the battery. The test was repeated 5 times, and the average value was obtained after removing the maximum and minimum values.

[0054] During the 2-hour rate capacity test, the battery is pre-conditioned by charging it with a constant current and then with a constant voltage, then left to stand for 2 hours. After the charging process at both ends, the battery is discharged to measure the battery performance.

[0055] The multi-dimensional scoring system includes a parameter acquisition module, a weight distribution module, a temperature characteristic matching module, a scoring calculation module, and a data storage and output module. The temperature characteristic matching module is set within the temperature characteristic matching system. The multi-dimensional evaluation method can comprehensively reflect the performance of the battery, avoiding the limitations of single parameter evaluation. Through the multi-dimensional scoring system, battery grouping no longer relies solely on a single parameter (such as voltage or internal resistance), but comprehensively considers multiple key parameters to ensure that the battery pack has a high degree of consistency in multiple performance indicators. This high-precision grouping method can significantly improve the overall performance and service life of the battery pack;

[0056] The artificial intelligence matching module includes a data input module, a deep learning model module and an algorithm module. Through the deep learning model and optimization algorithm, it ensures that the battery pack has high consistency in multiple performance indicators, can predict the expected life and performance matching of the battery pack, help users understand the long-term performance of the battery pack, and dynamically adjust the matching plan based on real-time test data and environmental parameters. The deep learning model module adopts LSTM algorithm technology to predict the future performance of the battery by learning the performance changes of the battery at different time points. The algorithm module is used to optimize the deep learning model, including gradient descent algorithm and adaptive learning rate optimization algorithm.

[0057] The parameter acquisition module is used to collect test parameters of the open circuit voltage test, internal resistance test, 2-hour rate capacity test and dynamic response test of the high-precision tester;

[0058] The weight allocation module is used to allocate corresponding weights according to the degree of influence of different battery parameters on battery pack performance, and ensure the accuracy and reliability of the scoring results through weight allocation and temperature characteristic matching;

[0059] The temperature characteristic matching module is used to match the battery's performance at different temperatures. The temperature characteristic matching module mainly tests the battery's capacity temperature change and internal resistance temperature change at 0°C, 25°C, and 40°C. According to the battery's temperature coefficient, the test parameters are automatically adjusted to ensure the accuracy of the scoring results.

[0060] The scoring calculation module scores the battery by weight distribution and temperature characteristic matching results. The data storage output module is used to store the scoring results and transmit the data to the intelligent matching algorithm module through the data input module. The deep learning algorithm module analyzes the multi-dimensional parameters of the structure and scores according to the difference between the test results and the qualified standards.

[0061] The pre-charging equalization module is provided with a three-stage charging strategy, wherein the first and second stages of the three-stage charging strategy both adopt constant current charging, and the third stage of the three-stage charging strategy adopts constant voltage charging and reduces the current value;

[0062] Phase 1: 0.1C 10 A is charged with constant current to 13.8V;

[0063] The second stage: 0.05C 10 A is charged with constant current to 14.4V;

[0064] Stage 3: Charge at a constant voltage of 14.4V until the current drops to 0.01C 10 A;

[0065] When the voltage difference threshold of the single cells in the battery pack is greater than 20mV, the equalization charge is triggered. Before the battery pack is assembled, the batteries are equalized and charged to ensure that each single cell has consistent voltage and capacity in the initial state.

[0066] The dynamic balancing management module is used to detect the voltage, current and temperature changes of each single cell in the battery pack in real time. The sampling frequency, voltage progress, current accuracy and temperature accuracy of the dynamic balancing management module are 1Hz, ±1mV, ±10mA and ±0.1°C respectively. During the use of the battery pack, the voltage, current and temperature of each single cell in the battery pack are monitored in real time, and the battery's charge and discharge status is automatically adjusted to ensure that the battery pack maintains dynamic balance during use. The balancing current and balancing threshold are automatically adjusted according to the battery's temperature changes and voltage differences to ensure that the battery pack maintains dynamic balance during use.

[0067] The full-process traceability module is used to manage battery raw material batches, record production parameters, store test data, and trace grouping plans. It also binds data to the battery's unique identification code and traces the data in real time. By recording and storing various data in the production process, the quality control system can achieve full-process traceability from raw materials to finished products, ensuring that the production process of each battery pack is traceable and monitorable.

[0068] The real-time monitoring module is used to detect the voltage parameters, current parameters, temperature parameters and humidity parameters during the battery production process in real time. The voltage parameters, current parameters, temperature parameters and humidity parameters of the real-time monitoring module are ±1mV, ±10mA, ±0.1℃ and ±5%RH, respectively. By real-time monitoring of various parameters in the production process, the stability and consistency of the production process are ensured, and potential problems are discovered and resolved in a timely manner.

[0069] The intelligent early warning module is used to monitor the real-time monitoring module. The intelligent early warning module includes a threshold anomaly detection function, an automatic alarm function, and an early warning recording function. Through real-time monitoring and data analysis, it automatically detects and warns of abnormal situations in the production process, helping enterprises to quickly solve problems and reduce production losses;

[0070] The data statistical analysis module is used to analyze the test data in the battery production process and generate trend forecasts. By statistically analyzing the production data, it generates quality reports and trend forecasts to help companies optimize production processes and improve product quality and production efficiency.

[0071] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. Intelligent assembly system of valve-regulated lead-acid batteries for electric bicycles, characterized by: include: An intelligent pre-screening system for testing and collecting battery parameters, comprising a high-precision battery tester, an intelligent temperature control system, a multi-channel data acquisition system, and an automated transmission device, wherein the intelligent temperature control system is electrically connected to the high-precision battery tester, the multi-channel data acquisition system, and the automated transmission device; An artificial intelligence matching unit, used to analyze data and generate a matching plan, comprising a multi-dimensional scoring system, a temperature characteristics matching system, and artificial intelligence matching, wherein the temperature characteristics matching system is electrically connected to the intelligent temperature control system; An adaptive balancing system for achieving dynamic balancing of the battery pack, comprising a pre-charge balancing module and a dynamic balancing management module, both of which are electrically connected to a multi-channel data acquisition system; The quality control system is used for full-process monitoring and tracing. The quality control system is electrically connected to the intelligent pre-screening system. The quality control system includes a full-process tracing module, a real-time monitoring module, an intelligent early warning module and a data statistical analysis module.

2. The intelligent assembly system of valve-regulated lead-acid batteries for electric bicycles according to claim 1, characterized in that: The high-precision battery tester is used for open circuit voltage test, internal resistance test, 2-hour rate capacity test and dynamic response test of the battery; The intelligent temperature control system is used to accurately control the temperature of the test environment to ensure consistency in battery performance testing under different temperature conditions; The multi-channel data acquisition system is used to collect various test data of the battery in real time; The automated transport device is used for loading, unloading and transporting batteries, ensuring that the batteries can accurately enter the test position and complete the test process.

3. The intelligent assembly system of valve-regulated lead-acid batteries for electric bicycles according to claim 2, characterized in that: The open circuit voltage test is conducted at least three times and the average value is taken. The internal resistance test is conducted at least five times and the average value is taken after removing the maximum value. The 2-hour rate capacity test is used to detect the battery capacity difference during the battery charging and discharging process. The dynamic response test is used for pulse discharge during the battery charging and discharging process, and the test results are transmitted to the channel data acquisition system through the digital interface.

4. The intelligent assembly system of valve-regulated lead-acid batteries for electric bicycles according to claim 3, characterized in that: The multi-dimensional scoring system includes a parameter acquisition module, a weight distribution module, a temperature characteristic matching module, a scoring calculation module and a data storage and output module. The temperature characteristic matching module is arranged inside the temperature characteristic matching system; The artificial intelligence combination includes a data input module, a deep learning model module and an algorithm module.

5. The intelligent assembly system of valve-regulated lead-acid batteries for electric bicycles according to claim 4, characterized in that: The parameter acquisition module is used to collect test parameters of the open circuit voltage test, internal resistance test, 2-hour rate capacity test and dynamic response test of the high-precision tester; The weight allocation module is used to allocate corresponding weights according to the degree of influence of different battery parameters on the performance of the battery pack; The temperature characteristic matching module is used to match the performance of the battery at different temperatures. The temperature characteristic matching module mainly tests the temperature change of the battery's capacity and internal resistance at 0°C, 25°C, and 40°C. The scoring calculation module scores the battery by weight distribution and temperature characteristic matching results. The data storage output module is used to store the scoring results and transmit the data to the intelligent matching algorithm module through the data input module. The multi-dimensional parameters of the structure are analyzed through the deep learning algorithm module.

6. The intelligent assembly system of valve-regulated lead-acid batteries for electric bicycles according to claim 5, characterized in that: The pre-charging equalization module is provided with a three-stage charging strategy, wherein the first and second stages of the three-stage charging strategy both adopt constant current charging, and the third stage of the three-stage charging strategy adopts constant voltage charging and reduces the current value; Phase 1: 0.1C 10 A is charged with constant current to 13.8V; The second stage: 0.05C 10 A is charged with constant current to 14.4V; Stage 3: Charge at a constant voltage of 14.4V until the current drops to 0.01C 10 A; When the voltage difference threshold value of the single cells in the battery pack is greater than 20mV, equalization charging is triggered.

7. The intelligent assembly system of valve-regulated lead-acid batteries for electric bicycles according to claim 6, characterized in that: The dynamic balancing management module is used to detect the voltage, current and temperature changes of each single battery in the battery pack in real time. The sampling frequency, voltage progress, current accuracy and temperature accuracy of the dynamic balancing management module are 1Hz, ±1mV, ±10mA and ±0.1℃ respectively.

8. The intelligent assembly system of valve-regulated lead-acid batteries for electric bicycles according to claim 7, characterized in that: The full-process traceability module is used to manage battery raw material batches, record production parameters, store test data, and trace grouping plans, and binds the data to the battery's unique identification code to trace the data in real time.

9. The intelligent assembly system of valve-regulated lead-acid batteries for electric bicycles according to claim 8, characterized in that: The real-time monitoring module is used to detect the voltage parameters, current parameters, temperature parameters and humidity parameters in the battery production process in real time. The voltage parameters, current parameters, temperature parameters and humidity parameters of the real-time monitoring module are ±1mV, ±10mA, ±0.1℃ and ±5%RH respectively.

10. The intelligent assembly system of valve-regulated lead-acid batteries for electric bicycles according to claim 9, characterized in that: The intelligent early warning module is used to monitor the real-time monitoring module, and the intelligent early warning module includes a threshold anomaly detection function, an automatic alarm function and an early warning recording function; The data statistical analysis module is used to analyze the test data in the battery production process and generate trend predictions.

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