A protection control system for a dedicated chip for lithium battery charge and discharge management

Abstract

The present invention discloses a protection control system for a dedicated chip for lithium battery charge and discharge management, which specifically relates to the field of lithium battery protection technology, including a voltage protection module for ensuring that the battery voltage is within a safe range to prevent overvoltage and undervoltage from damaging the battery, a current protection module for monitoring the charge and discharge current to prevent overcurrent and short circuit from causing battery damage, a temperature protection module for adjusting and monitoring the battery temperature to prevent overheating or low temperature from causing adverse effects on the battery, and a communication management module for realizing data transmission and control between the battery and external devices. The present invention is designed to monitor the temperature, humidity, air pressure and other information of the battery environment in real time, further optimize the working conditions of the battery, and the entire system, through the close cooperation between the modules, not only improves the safety and reliability of the battery, but also extends the service life of the battery, ensuring efficient operation in different use environments.

Description

Technical Field

[0001] The present invention relates to the technical field of lithium battery protection, and in particular to a protection control system of a dedicated chip for lithium battery charge and discharge management. Background Art

[0002] As society's demand for renewable energy and green energy continues to increase, lithium batteries, as a high energy density, high efficiency and long life energy storage device, are widely used in various electronic devices such as electric vehicles, smart phones, laptops and energy storage systems. However, lithium batteries may encounter problems such as overvoltage, undervoltage, overcurrent, short circuit and abnormal temperature during use, which not only affect battery performance but also may cause safety hazards. In order to ensure the safety and efficient operation of lithium batteries, the charge and discharge management system has become an indispensable key component. The traditional charge and discharge management system mainly relies on fixed voltage and current protection thresholds, which is difficult to adapt to different use environments and changes in battery health status, resulting in unsatisfactory battery protection and management effects. Therefore, it is an important direction of current technological development to develop a protection control system for a dedicated chip for lithium battery charge and discharge management that can dynamically adjust protection parameters and has intelligent monitoring and self-repair functions.

[0003] However, the existing lithium battery charge and discharge management system still has certain shortcomings in actual use. First, the traditional system adopts a fixed protection threshold, which is difficult to dynamically adjust according to the battery's use environment and health status, which easily leads to over-protection or under-protection, affecting battery performance and life. Secondly, the traditional system lacks efficient real-time monitoring and data analysis capabilities, and cannot predict the changing trends of voltage and temperature in advance, which may cause temperature anomalies and battery damage. In addition, the existing system has hidden dangers in the security of data transmission and storage, and it is difficult to prevent data tampering and loss, affecting the reliability of the system. When communication is interrupted, the traditional system is also difficult to recover automatically, affecting the continuity of the system. Finally, the traditional system lacks real-time perception and optimization functions of environmental conditions, and it is difficult to optimize the working conditions of the battery according to environmental information such as temperature, humidity and air pressure, resulting in poor performance of the battery in complex environments. Therefore, we propose a protection control system for a dedicated chip for lithium battery charge and discharge management. Summary of the invention

[0004] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a protection control system for a dedicated chip for lithium battery charge and discharge management.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] A protection control system for a dedicated chip for lithium battery charge and discharge management, comprising a voltage protection module for ensuring that the battery voltage is within a safe range to prevent overvoltage and undervoltage from damaging the battery, a current protection module for monitoring the charge and discharge current to prevent overcurrent and short circuit from causing battery damage, a temperature protection module for regulating and monitoring the battery temperature to prevent overheating or low temperature from causing adverse effects on the battery, and a communication management module for realizing data transmission and control between the battery and external equipment;

[0007] The voltage protection module includes an adaptive voltage regulation module for dynamically adjusting the voltage protection threshold according to the use environment and battery health status using an artificial intelligence algorithm, an intelligent voltage equalization module for predicting the future voltage change trend of the single battery through big data analysis and performing voltage equalization in advance, and a bidirectional voltage protection module for stopping charging and discharging when the voltage is abnormal and automatically resuming the charging and discharging function after the voltage returns to normal;

[0008] The current protection module includes a self-learning overcurrent protection module for dynamically adjusting the overcurrent protection threshold by learning and adapting to the charging and discharging characteristics of different devices using machine learning technology, a real-time current monitoring module for real-time monitoring of current changes and rapid response through high-speed data acquisition and processing, and a composite short-circuit protection module for combining multiple detection technologies to more quickly identify and respond to short-circuit conditions.

[0009] The present invention is further configured as follows: the temperature protection module includes a regional temperature control module for dividing the battery pack into a plurality of regions to monitor and control the temperature of each region respectively, an active temperature control module for actively adjusting the battery temperature by a heating or cooling device in combination with the battery use environment, and a predictive temperature management module for taking measures in advance to prevent temperature abnormalities according to the current temperature trend and use mode using a prediction algorithm;

[0010] The communication management module includes a blockchain data security module for ensuring the security of battery data transmission and storage using blockchain technology, a remote diagnosis module for remotely monitoring and diagnosing the battery status through a cloud platform to provide timely maintenance suggestions and operation guidance, a self-healing communication module for the system to automatically find alternative communication paths or methods when communication is interrupted or abnormal, and an environmental perception module for integrating environmental sensors to monitor the temperature, humidity, air pressure and other information of the battery environment in real time.

[0011] The present invention is further configured as follows: the adaptive voltage regulation module includes an intelligent prediction algorithm module for using machine learning and AI algorithms to predict future voltage change trends based on historical data and real-time data to adjust the voltage protection threshold in advance, a context perception module for detecting the usage scenario of the battery and dynamically adjusting the voltage protection strategy, a context determination module for determining whether the current voltage protection strategy needs to be adjusted according to the output of the intelligent prediction algorithm and the context perception module, an electrochemical characteristic monitoring module for monitoring the electrochemical reaction inside the battery in real time through a microsensor to provide accurate battery status data to assist in voltage regulation, a distributed control module for dividing the battery pack into multiple small areas to independently monitor and adjust the voltage of each area, a voltage self-healing module for automatically adjusting the chemical reaction speed and current path inside the battery when the voltage is abnormal, and an environmental adaptation determination module for comprehensively determining whether the voltage protection strategy needs to be adjusted in the current environment of the battery based on the data of the intelligent prediction algorithm, context perception and electrochemical characteristic monitoring modules;

[0012] The scenario determination module includes a user habit analysis module for analyzing user habits by monitoring the user's usage mode and behavior to provide a basis for scenario determination, a usage frequency monitoring module for monitoring the usage frequency and duration of the device to determine the battery load condition, a workload detection module for real-time detection of the device's workload condition to provide device operation data to help determine the voltage protection demand, a power consumption prediction module for predicting future power consumption changes based on the device's operation history data and current status to assist in determining whether the voltage protection strategy needs to be adjusted, and an abnormal triggering module for detecting abnormal data and sending an alarm to the staff;

[0013] The environmental adaptation determination module includes a temperature change monitoring module for real-time monitoring of changes in ambient temperature to provide temperature data to support the determination of voltage protection strategy adjustment, a humidity monitoring module for monitoring changes in ambient humidity to determine the impact of humidity on battery performance, an air pressure monitoring module for monitoring changes in ambient air pressure to determine the impact of air pressure on the battery, and an environmental sensor fusion module for integrating data from multiple environmental sensors such as temperature, humidity, and air pressure to determine whether the current environment is abnormal.

[0014] The present invention is further configured as follows: the intelligent prediction algorithm module uses machine learning and AI algorithms to predict future voltage change trends based on historical data and real-time data, and transmits the data to a context perception module to detect battery usage scenarios; the context perception module detects battery usage scenarios and transmits the data to a context determination module to determine whether the current voltage protection strategy needs to be adjusted based on the output of the intelligent prediction algorithm and the context perception module; the context determination module determines whether the current voltage protection strategy needs to be adjusted based on the output of the intelligent prediction algorithm and the context perception module, and transmits the result to an electrochemical characteristic monitoring module to monitor the electrochemical reaction inside the battery in real time through a microsensor; the electrochemical characteristic monitoring module uses microsensors to monitor the electrochemical reaction inside the battery in real time. The sensor monitors the electrochemical reaction inside the battery in real time and transmits the data to the distributed control module to divide the battery pack into multiple small areas to independently monitor and adjust the voltage of each area; the distributed control module divides the battery pack into multiple small areas to independently monitor and adjust the voltage of each area and transmits the information to the voltage self-healing module to automatically adjust the chemical reaction speed and current path inside the battery when the voltage is abnormal; the voltage self-healing module automatically adjusts the chemical reaction speed and current path inside the battery when the voltage is abnormal and transmits the adjusted data to the environmental adaptation judgment module to comprehensively judge whether the voltage protection strategy needs to be adjusted in the current environment of the battery based on the data of the intelligent prediction algorithm, situational perception and electrochemical characteristics monitoring module.

[0015] The present invention is further configured as follows: the adaptive voltage regulation module uses an artificial intelligence algorithm to dynamically adjust the voltage protection threshold according to the use environment and the battery health status and transmits the data to the intelligent voltage balancing module to predict the future voltage change trend of the single battery through big data analysis to perform voltage balancing in advance; the intelligent voltage balancing module predicts the future voltage change trend of the single battery through big data analysis to perform voltage balancing in advance and transmits the voltage abnormality information to the bidirectional voltage protection module to control the stop-discharge function.

[0016] The present invention is further configured as follows: the self-learning overcurrent protection module utilizes machine learning technology to learn and adapt to the charging and discharging characteristics of different devices to dynamically adjust the overcurrent protection threshold and transmit real-time data to the real-time current monitoring module to monitor current changes in real time and respond quickly through high-speed data acquisition and processing; the real-time current monitoring module monitors current changes in real time and responds quickly through high-speed data acquisition and processing and transmits the monitored abnormal current conditions to the composite short-circuit protection module to combine multiple detection technologies to more quickly identify and respond to short-circuit conditions.

[0017] The present invention is further configured as follows: the regional temperature control module divides the battery pack into a plurality of regions, monitors and controls the temperature of each region respectively, and transmits the regional temperature data to the active temperature control module to actively adjust the battery temperature through a heating or cooling device in combination with the battery use environment; the active temperature control module actively adjusts the battery temperature through a heating or cooling device in combination with the battery use environment and transmits the current temperature trend and usage mode data to the predictive temperature management module, which uses a predictive algorithm to take measures in advance to prevent temperature abnormalities according to the current temperature trend and usage mode.

[0018] The present invention is further configured as follows: the blockchain data security module utilizes blockchain technology to ensure the security of battery data transmission and storage and transmits the data to the remote diagnosis module, which remotely monitors and diagnoses the battery status through a cloud platform and provides timely maintenance suggestions and operation guidance; the remote diagnosis module remotely monitors and diagnoses the battery status through a cloud platform and provides timely maintenance suggestions and operation guidance, and transmits the information to the self-healing communication module when the communication is interrupted or abnormal, so that the system can automatically find an alternative communication path or method when the communication is interrupted or abnormal; the self-healing communication module can automatically find an alternative communication path or method when the communication is interrupted or abnormal and transmit the environmental monitoring data to the environmental perception module, which integrates environmental sensors to monitor the temperature, humidity, air pressure and other information of the battery environment in real time.

[0019] The beneficial effects of the present invention are:

[0020] The present invention realizes comprehensive protection and efficient management of batteries by integrating multiple advanced technologies. First, the system uses artificial intelligence and machine learning technologies to dynamically adjust the voltage and current protection thresholds according to the use environment and battery health status to ensure that the battery always operates within a safe range and prevent overvoltage, undervoltage, overcurrent and short circuit. Through real-time monitoring and data analysis, the system can predict the changing trends of voltage and temperature in advance, and take active measures to adjust them to avoid abnormal temperature and battery damage. In addition, the system ensures the security of data transmission and storage through blockchain technology, and realizes remote monitoring and diagnosis through the cloud platform, providing timely maintenance suggestions and operation guidance. When communication is interrupted, the self-healing communication module can automatically find alternative paths to ensure the continuity and reliability of the system. Through the environmental perception module, the system can monitor the temperature, humidity, air pressure and other information of the battery environment in real time, and further optimize the working conditions of the battery. The entire system not only improves the safety and reliability of the battery through the close cooperation between the modules, but also extends the service life of the battery, ensuring efficient operation in different use environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a schematic diagram of the system in the present invention.

[0022] Figure 2It is a partial flow chart of the adaptive voltage regulation module in the present invention. DETAILED DESCRIPTION

[0023] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0024] Example 1

[0025] like Figure 1 As shown, a protection control system for a dedicated chip for lithium battery charge and discharge management includes a voltage protection module for ensuring that the battery voltage is within a safe range to prevent overvoltage and undervoltage from damaging the battery, a current protection module for monitoring the charge and discharge current to prevent overcurrent and short circuit from causing battery damage, a temperature protection module for regulating and monitoring the battery temperature to prevent overheating or low temperature from causing adverse effects on the battery, and a communication management module for realizing data transmission and control between the battery and external devices;

[0026] The voltage protection module includes an adaptive voltage regulation module for dynamically adjusting the voltage protection threshold according to the use environment and battery health status using an artificial intelligence algorithm, an intelligent voltage equalization module for predicting the future voltage change trend of the single battery through big data analysis and performing voltage equalization in advance, and a bidirectional voltage protection module for stopping charging and discharging when the voltage is abnormal and automatically resuming the charging and discharging function after the voltage returns to normal;

[0027] The adaptive voltage regulation module uses an artificial intelligence algorithm to dynamically adjust the voltage protection threshold according to the use environment and battery health status and transmits the data to the intelligent voltage balancing module to predict the future voltage change trend of the single battery through big data analysis to perform voltage balancing in advance; the intelligent voltage balancing module predicts the future voltage change trend of the single battery through big data analysis to perform voltage balancing in advance and transmits the voltage abnormality information to the bidirectional voltage protection module to control the stop-discharge function;

[0028] The current protection module includes a self-learning overcurrent protection module for dynamically adjusting the overcurrent protection threshold by learning and adapting to the charging and discharging characteristics of different devices using machine learning technology, a real-time current monitoring module for real-time monitoring of current changes and rapid response through high-speed data acquisition and processing, and a composite short-circuit protection module for combining multiple detection technologies to more quickly identify and respond to short-circuit conditions;

[0029] The self-learning overcurrent protection module uses machine learning technology to learn and adapt to the charging and discharging characteristics of different devices to dynamically adjust the overcurrent protection threshold and transmit real-time data to the real-time current monitoring module to monitor current changes in real time and respond quickly through high-speed data acquisition and processing; the real-time current monitoring module monitors current changes in real time and responds quickly through high-speed data acquisition and processing, and transmits the monitored abnormal current conditions to the composite short-circuit protection module to combine multiple detection technologies to more quickly identify and respond to short-circuit conditions;

[0030] The temperature protection module includes a regional temperature control module for dividing the battery pack into a plurality of regions to monitor and control the temperature of each region respectively, an active temperature control module for actively adjusting the battery temperature by heating or cooling devices in combination with the battery use environment, and a predictive temperature management module for taking measures in advance to prevent temperature abnormalities according to the current temperature trend and use mode using a prediction algorithm;

[0031] The regional temperature control module divides the battery pack into multiple regions, monitors and controls the temperature of each region respectively, and transmits the regional temperature data to the active temperature control module to actively adjust the battery temperature through a heating or cooling device in combination with the battery use environment; the active temperature control module actively adjusts the battery temperature through a heating or cooling device in combination with the battery use environment and transmits the current temperature trend and use mode data to the predictive temperature management module, which uses a predictive algorithm to take measures in advance to prevent temperature abnormalities according to the current temperature trend and use mode;

[0032] The communication management module includes a blockchain data security module for ensuring the security of battery data transmission and storage using blockchain technology, a remote diagnosis module for remotely monitoring and diagnosing battery status through a cloud platform to provide timely maintenance suggestions and operation guidance, a self-healing communication module for the system to automatically find alternative communication paths or methods when communication is interrupted or abnormal, and an environmental perception module for integrating environmental sensors to monitor the temperature, humidity, air pressure and other information of the battery environment in real time;

[0033] The blockchain data security module uses blockchain technology to ensure the security of battery data transmission and storage, and transmits the data to the remote diagnosis module, which remotely monitors and diagnoses the battery status through the cloud platform and provides timely maintenance suggestions and operation instructions; the remote diagnosis module remotely monitors and diagnoses the battery status through the cloud platform, provides timely maintenance suggestions and operation instructions, and transmits the information to the self-healing communication module when the communication is interrupted or abnormal, so that the system can automatically find an alternative communication path or method when the communication is interrupted or abnormal; the self-healing communication module can automatically find an alternative communication path or method when the communication is interrupted or abnormal, and transmit the environmental monitoring data to the environmental perception module, which integrates environmental sensors to monitor the temperature, humidity, air pressure and other information of the battery environment in real time.

[0034] In the above embodiment, first, the voltage protection module uses an artificial intelligence algorithm to dynamically adjust the voltage protection threshold according to the use environment and battery health status, and transmits the data to the intelligent equalization voltage module. The intelligent equalization voltage module predicts the future voltage change trend of the single battery through big data analysis, performs voltage equalization in advance, and transmits the information to the bidirectional voltage protection module when the voltage is abnormal to control the charging and discharging function. The current protection module uses machine learning technology through the self-learning overcurrent protection module to learn and adapt to the charging and discharging characteristics of different devices, dynamically adjust the overcurrent protection threshold, and transmit real-time data to the real-time current monitoring module. The real-time current monitoring module monitors the current change in real time through high-speed data acquisition and processing, and transmits it to the composite short-circuit protection module when abnormal current conditions are detected, so as to quickly identify and respond to short circuit conditions. The temperature protection module divides the battery pack into multiple areas through the regional temperature control module, monitors and controls the temperature of each area respectively, and transmits the regional temperature data to the active temperature control module, and actively adjusts the battery temperature through the heating or cooling device in combination with the battery use environment. The active temperature control module transmits the current temperature trend and usage mode data to the predictive temperature management module, and uses the prediction algorithm to take measures in advance to prevent temperature abnormalities according to the current temperature trend and usage mode. The communication management module ensures the security of battery data transmission and storage through the blockchain data security module, and transmits the data to the remote diagnosis module, which remotely monitors and diagnoses the battery status through the cloud platform to provide timely maintenance suggestions and operation guidance. When communication is interrupted or abnormal, the self-healing communication module automatically searches for alternative communication paths and transmits environmental monitoring data to the environmental perception module to monitor the temperature, humidity, air pressure and other information of the battery environment in real time. The entire system ensures the safe, reliable and efficient operation of the battery in different usage environments through close cooperation between the modules.

[0035] Example 2

[0036] like Figure 1-2As shown, a protection control system for a dedicated chip for lithium battery charge and discharge management, the adaptive voltage regulation module includes an intelligent prediction algorithm module for using machine learning and AI algorithms to predict future voltage change trends based on historical data and real-time data to adjust the voltage protection threshold in advance, a situation perception module for detecting the battery usage scenario and dynamically adjusting the voltage protection strategy, a situation determination module for determining whether the current voltage protection strategy needs to be adjusted according to the output of the intelligent prediction algorithm and the situation perception module, an electrochemical characteristic monitoring module for monitoring the electrochemical reaction inside the battery in real time through a microsensor to provide accurate battery status data to assist in voltage regulation, a distributed control module for dividing the battery pack into multiple small areas to independently monitor and adjust the voltage of each area, a voltage self-healing module for automatically adjusting the chemical reaction speed and current path inside the battery when the voltage is abnormal, and an environmental adaptation determination module for comprehensively determining whether the voltage protection strategy needs to be adjusted in the current environment of the battery based on the data of the intelligent prediction algorithm, situation perception and electrochemical characteristic monitoring modules;

[0037] The scenario determination module includes a user habit analysis module for analyzing user habits by monitoring the user's usage mode and behavior to provide a basis for scenario determination, a usage frequency monitoring module for monitoring the usage frequency and duration of the device to determine the battery load condition, a workload detection module for real-time detection of the device's workload condition to provide device operation data to help determine the voltage protection demand, a power consumption prediction module for predicting future power consumption changes based on the device's operation history data and current status to assist in determining whether the voltage protection strategy needs to be adjusted, and an abnormal triggering module for detecting abnormal data and sending an alarm to the staff;

[0038] The environmental adaptation determination module includes a temperature change monitoring module for real-time monitoring of environmental temperature changes to provide temperature data to support the determination of voltage protection strategy adjustment, a humidity monitoring module for monitoring environmental humidity changes to determine the impact of humidity on battery performance, an air pressure monitoring module for monitoring environmental air pressure changes to determine the impact of air pressure on the battery, and an environmental sensor fusion module for integrating data from multiple environmental sensors such as temperature, humidity, and air pressure to determine whether the current environment is abnormal;

[0039] The intelligent prediction algorithm module uses machine learning and AI algorithms to predict future voltage change trends based on historical data and real-time data and transmits the data to the context perception module to detect the battery usage scenario; the context perception module detects the battery usage scenario and transmits the data to the context determination module to determine whether the current voltage protection strategy needs to be adjusted based on the output of the intelligent prediction algorithm and the context perception module; the context determination module determines whether the current voltage protection strategy needs to be adjusted based on the output of the intelligent prediction algorithm and the context perception module and transmits the result to the electrochemical characteristic monitoring module to monitor the electrochemical reaction inside the battery in real time through microsensors; the electrochemical characteristic monitoring module monitors the electrochemical reaction inside the battery in real time through microsensors and transmits the data to the distributed control module to divide the battery pack into multiple small areas to independently monitor and adjust the voltage of each area; the distributed control module divides the battery pack into multiple small areas to independently monitor and adjust the voltage of each area and transmits the information to the voltage self-healing module to automatically adjust the chemical reaction speed and current path inside the battery when the voltage is abnormal; the voltage self-healing module automatically adjusts the chemical reaction speed and current path inside the battery when the voltage is abnormal and transmits the adjusted data to the environmental adaptation determination module to comprehensively determine whether the voltage protection strategy needs to be adjusted in the current environment of the battery based on the data of the intelligent prediction algorithm, context perception and electrochemical characteristic monitoring modules.

[0040] In the above embodiment, first, the intelligent prediction algorithm module uses machine learning and AI algorithms to predict the future voltage change trend based on historical data and real-time data, and transmits these predicted data to the situational perception module. The situational perception module detects the usage scenario of the battery and transmits the relevant data to the situational determination module. The situational determination module determines whether the current voltage protection strategy needs to be adjusted based on the output of the intelligent prediction algorithm and the situational perception module, and transmits the determination result to the electrochemical characteristic monitoring module. The electrochemical characteristic monitoring module monitors the electrochemical reaction inside the battery in real time through microsensors, provides accurate battery status data, and then transmits these data to the distributed control module. The distributed control module divides the battery pack into multiple small areas, independently monitors and adjusts the voltage of each area, and transmits the information to the voltage self-healing module when the voltage is abnormal. The voltage self-healing module automatically adjusts the chemical reaction speed and current path inside the battery when the voltage is abnormal to ensure that the voltage returns to normal, and the adjusted data is then transmitted to the environmental adaptation determination module. The environmental adaptation determination module combines the data of the intelligent prediction algorithm, situational perception and electrochemical characteristic monitoring modules to determine whether the voltage protection strategy needs to be adjusted in the current environment of the battery. This entire system ensures safe, reliable and efficient operation of the battery in different usage environments through close collaboration between modules.

[0041] Among them, the user habit analysis module analyzes user habits by monitoring the user's usage patterns and behaviors and transmits the data to the usage frequency monitoring module to monitor the usage frequency and duration of the equipment; the usage frequency monitoring module monitors the usage frequency and duration of the equipment and transmits the data to the workload detection module to detect the workload of the equipment in real time; the workload detection module detects the workload of the equipment in real time and transmits the data to the power consumption prediction module to predict future power consumption changes based on the equipment's operating history data and current status; the power consumption prediction module predicts future power consumption changes based on the equipment's operating history data and current status and transmits the information to the abnormal trigger module to issue an alarm to the staff when abnormal data is detected.

[0042] Among them, the temperature change monitoring module monitors the changes in ambient temperature in real time and transmits temperature data to the humidity monitoring module to monitor changes in ambient humidity; the humidity monitoring module monitors changes in ambient humidity and transmits data to the air pressure monitoring module to monitor changes in ambient air pressure; the air pressure monitoring module monitors changes in ambient air pressure and transmits data to the environmental sensor fusion module to integrate data from multiple environmental sensors such as temperature, humidity, and air pressure to determine whether the current environment is abnormal.

[0043] Working principle: When the present invention is used, first, the voltage protection module uses an artificial intelligence algorithm to dynamically adjust the voltage protection threshold according to the use environment and battery health status, and transmits these data to the intelligent equalization voltage module. The intelligent equalization voltage module predicts the future voltage change trend of the single battery through big data analysis, performs voltage equalization in advance, and transmits information to the bidirectional voltage protection module to control the charging and discharging function when the voltage is abnormal. The current protection module uses machine learning technology through the self-learning overcurrent protection module to learn and adapt to the charging and discharging characteristics of different devices, dynamically adjust the overcurrent protection threshold, and transmit real-time data to the real-time current monitoring module. The real-time current monitoring module monitors the current change in real time through high-speed data acquisition and processing, and transmits information to the composite short-circuit protection module when abnormal current conditions are detected, so as to quickly identify and respond to short-circuit conditions. The temperature protection module divides the battery pack into multiple areas through the regional temperature control module, monitors and controls the temperature of each area respectively, and transmits the regional temperature data to the active temperature control module. The active temperature control module actively adjusts the battery temperature through the heating or cooling device in combination with the battery use environment, and transmits the current temperature trend and usage mode data to the predictive temperature management module, and takes measures in advance to prevent temperature abnormalities using the prediction algorithm. The communication management module ensures the security of battery data transmission and storage through the blockchain data security module, and transmits the data to the remote diagnosis module, which remotely monitors and diagnoses the battery status through the cloud platform, and provides timely maintenance suggestions and operation guidance. When communication is interrupted or abnormal, the self-healing communication module automatically searches for alternative communication paths and transmits environmental monitoring data to the environmental perception module to monitor the temperature, humidity, air pressure and other information of the battery environment in real time. The entire system ensures the safe, reliable and efficient operation of the battery in different usage environments through close cooperation between the modules.

[0044] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A protection control system for a dedicated chip for lithium battery charge and discharge management, characterized in that: It includes a voltage protection module for ensuring that the battery voltage is within a safe range to prevent overvoltage and undervoltage from damaging the battery, a current protection module for monitoring the charge and discharge current to prevent overcurrent and short circuit from causing battery damage, a temperature protection module for regulating and monitoring the battery temperature to prevent overheating or low temperature from causing adverse effects on the battery, and a communication management module for realizing data transmission and control between the battery and external devices; The voltage protection module includes an adaptive voltage regulation module for dynamically adjusting the voltage protection threshold according to the use environment and battery health status using an artificial intelligence algorithm, an intelligent voltage equalization module for predicting the future voltage change trend of the single battery through big data analysis and performing voltage equalization in advance, and a bidirectional voltage protection module for stopping charging and discharging when the voltage is abnormal and automatically resuming the charging and discharging function after the voltage returns to normal; The current protection module includes a self-learning overcurrent protection module for dynamically adjusting the overcurrent protection threshold by learning and adapting to the charging and discharging characteristics of different devices using machine learning technology, a real-time current monitoring module for real-time monitoring of current changes and rapid response through high-speed data acquisition and processing, and a composite short-circuit protection module for combining multiple detection technologies to more quickly identify and respond to short-circuit conditions; The temperature protection module includes a regional temperature control module for dividing the battery pack into a plurality of regions to monitor and control the temperature of each region respectively, an active temperature control module for actively adjusting the battery temperature by heating or cooling devices in combination with the battery use environment, and a predictive temperature management module for taking measures in advance to prevent temperature abnormalities according to the current temperature trend and use mode using a prediction algorithm; The communication management module includes a blockchain data security module for ensuring the security of battery data transmission and storage using blockchain technology, a remote diagnosis module for remotely monitoring and diagnosing battery status through a cloud platform to provide timely maintenance suggestions and operation guidance, a self-healing communication module for the system to automatically find alternative communication paths or methods when communication is interrupted or abnormal, and an environmental perception module for integrating environmental sensors to monitor the temperature, humidity and air pressure information of the battery environment in real time; The adaptive voltage regulation module includes an intelligent prediction algorithm module for using machine learning and AI algorithms to predict future voltage change trends based on historical data and real-time data to adjust the voltage protection threshold in advance, a context perception module for detecting the usage scenario of the battery and dynamically adjusting the voltage protection strategy, a context determination module for determining whether the current voltage protection strategy needs to be adjusted based on the output of the intelligent prediction algorithm and the context perception module, an electrochemical characteristic monitoring module for monitoring the electrochemical reaction inside the battery in real time through a microsensor to provide accurate battery status data to assist in voltage regulation, a distributed control module for dividing the battery pack into multiple small areas to independently monitor and adjust the voltage of each area, a voltage self-healing module for automatically adjusting the chemical reaction speed and current path inside the battery when the voltage is abnormal, and an environmental adaptation determination module for comprehensively determining whether the voltage protection strategy needs to be adjusted in the current environment of the battery based on the data of the intelligent prediction algorithm, context perception and electrochemical characteristic monitoring modules; The scenario determination module includes a user habit analysis module for analyzing user habits by monitoring the user's usage mode and behavior to provide a basis for scenario determination, a usage frequency monitoring module for monitoring the usage frequency and duration of the device to determine the battery load condition, a workload detection module for real-time detection of the device's workload condition to provide device operation data to help determine the voltage protection demand, a power consumption prediction module for predicting future power consumption changes based on the device's operation history data and current status to assist in determining whether the voltage protection strategy needs to be adjusted, and an abnormality issuance module for detecting abnormal data and issuing an alarm to the staff; The environmental adaptation determination module includes a temperature change monitoring module for real-time monitoring of environmental temperature changes to provide temperature data to support the determination of voltage protection strategy adjustment, a humidity monitoring module for monitoring environmental humidity changes to determine the impact of humidity on battery performance, an air pressure monitoring module for monitoring environmental air pressure changes to determine the impact of air pressure on the battery, and an environmental sensor fusion module for integrating data from multiple environmental sensors such as temperature, humidity, and air pressure to determine whether the current environment is abnormal. The intelligent prediction algorithm module uses machine learning and AI algorithms to predict future voltage change trends based on historical data and real-time data and transmits the data to the context perception module to detect the battery usage scenario; the context perception module detects the battery usage scenario and transmits the data to the context determination module to determine whether the current voltage protection strategy needs to be adjusted based on the output of the intelligent prediction algorithm and the context perception module; the context determination module determines whether the current voltage protection strategy needs to be adjusted based on the output of the intelligent prediction algorithm and the context perception module and transmits the result to the electrochemical characteristic monitoring module to monitor the electrochemical reaction inside the battery in real time through microsensors; the electrochemical characteristic monitoring module monitors the electrochemical reaction inside the battery in real time through microsensors and transmits the data to the distributed control module to divide the battery pack into multiple small areas to independently monitor and adjust the voltage of each area; the distributed control module divides the battery pack into multiple small areas to independently monitor and adjust the voltage of each area and transmits the information to the voltage self-healing module to automatically adjust the chemical reaction speed and current path inside the battery when the voltage is abnormal; the voltage self-healing module automatically adjusts the chemical reaction speed and current path inside the battery when the voltage is abnormal and transmits the adjusted data to the environmental adaptation determination module to comprehensively determine whether the voltage protection strategy needs to be adjusted in the current environment of the battery based on the data of the intelligent prediction algorithm, context perception and electrochemical characteristic monitoring module; The adaptive voltage regulation module uses an artificial intelligence algorithm to dynamically adjust the voltage protection threshold according to the use environment and battery health status and transmits the data to the intelligent voltage balancing module to predict the future voltage change trend of the single battery through big data analysis to perform voltage balancing in advance; the intelligent voltage balancing module predicts the future voltage change trend of the single battery through big data analysis to perform voltage balancing in advance and transmits the voltage abnormality information to the bidirectional voltage protection module to control the stop-discharge function; The self-learning overcurrent protection module uses machine learning technology to learn and adapt to the charging and discharging characteristics of different devices to dynamically adjust the overcurrent protection threshold and transmit real-time data to the real-time current monitoring module to monitor current changes in real time and respond quickly through high-speed data acquisition and processing; the real-time current monitoring module monitors current changes in real time and responds quickly through high-speed data acquisition and processing, and transmits the monitored abnormal current conditions to the composite short-circuit protection module to combine multiple detection technologies to more quickly identify and respond to short-circuit conditions; The regional temperature control module divides the battery pack into multiple regions, monitors and controls the temperature of each region respectively, and transmits the regional temperature data to the active temperature control module to actively adjust the battery temperature through a heating or cooling device in combination with the battery use environment; the active temperature control module actively adjusts the battery temperature through a heating or cooling device in combination with the battery use environment and transmits the current temperature trend and use mode data to the predictive temperature management module, which uses a predictive algorithm to take measures in advance to prevent temperature abnormalities according to the current temperature trend and use mode; The blockchain data security module uses blockchain technology to ensure the security of battery data transmission and storage, and transmits the data to the remote diagnosis module, which remotely monitors and diagnoses the battery status through the cloud platform and provides timely maintenance suggestions and operation instructions; the remote diagnosis module remotely monitors and diagnoses the battery status through the cloud platform, provides timely maintenance suggestions and operation instructions, and transmits the information to the self-healing communication module when the communication is interrupted or abnormal, so that the system can automatically find an alternative communication path or method when the communication is interrupted or abnormal; the self-healing communication module can automatically find an alternative communication path or method when the communication is interrupted or abnormal, and transmit the environmental monitoring data to the environmental perception module, which integrates environmental sensors to monitor the temperature, humidity and air pressure information of the battery environment in real time.

Images