Battery management method for intelligent diagnosis and adaptive debugging

Abstract

A battery management method for intelligent diagnosis and adaptive debugging comprises the following steps: S1, initialization configuration: when a system is started, automatically reading and setting the type, capacity and maximum discharge current of a battery as basic parameters through a built-in initialization program; s2, self-calibration: reading voltage, current and temperature data of the battery unit by a sensor, and automatically adjusting deviation; s3, real-time monitoring: continuously monitoring various parameters of the battery, and identifying abnormal conditions; s4, performing fault prediction and self-repair; s5, cloud platform connection: the BMS is connected to a cloud server through Wi-Fi or a cellular network and uploads real-time data for remote monitoring; s6, OTA updating; and S7, visual debugging. According to the invention, system debugging can be automatically completed in the installation and operation process, the battery state is monitored in real time during operation, and the reliability and efficiency of the system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of battery management, and in particular to a battery management method of intelligent diagnosis and adaptive debugging. Background Art

[0002] With the development of electric vehicles and energy storage systems, battery management systems (BMS) have become one of the key technologies to ensure battery safety and extend battery life. The current battery management system mainly relies on manual operation, which has the following defects: long debugging cycle: manual debugging requires repeated testing and adjustment, resulting in a long debugging cycle; error-prone: manual operation may lead to incorrect or missing parameter settings, affecting the stability and reliability of the system; lack of intelligent diagnosis: the existing system is difficult to diagnose and repair potential faults in real time during operation. Summary of the invention

[0003] The object of the present invention is to provide a battery management method with intelligent diagnosis and adaptive debugging, which can effectively solve the technical problems mentioned in the background technology.

[0004] To achieve the above object, the present invention provides the following technical solutions: A battery management method with intelligent diagnosis and adaptive debugging includes the following steps: S1. Initialization configuration: When the system starts, the built-in initialization program automatically reads and sets the battery type, capacity, and maximum discharge current as basic parameters; S2. Self-calibration: The sensor reads the voltage, current and temperature data of the battery cell and automatically adjusts the deviation to ensure accurate readings. When improper operation by the user causes a large deviation in the current, causing the output value of the current to change beyond the normal range, the current calibration coefficient will be automatically adjusted. S21, data acquisition, regularly collecting voltage, current and temperature data of the battery cell through sensors; storing the collected data in memory for subsequent analysis and calibration; S22, deviation detection: compare the current reading with the expected value to determine whether the deviation value exceeds the preset normal range; S23, dynamic adjustment: if it is detected that the temperature change causes data deviation, the sensor calibration coefficient is adjusted according to the pre-stored temperature compensation table and updated to the data table library in step S7; Improper operation by the user causes a large deviation in the current, causing the output value of the current to change beyond the normal range. The system will automatically adjust the current and voltage; apply the updated calibration coefficient to the sensor to ensure accurate readings; S24, Deep Calibration: For persistent data deviation, a comprehensive calibration process is initiated, including sensor recalibration or troubleshooting, and the temperature and current information of the battery are continuously detected through duplex communication. If the sensor still has deviation, the fault information is recorded and the user is prompted to check or replace it. S3, real-time monitoring: continuously monitor various battery parameters and identify abnormal conditions; use data analysis methods to extract and analyze the statistical characteristics of various battery parameter data and monitor battery status. Data analysis algorithms include mean, variance, maximum, minimum, and standard deviation; S4, Fault prediction and self-repair: Once an abnormality is detected, the system will attempt to self-repair according to preset rules, restart some functions or prompt the user to check; fault prediction is performed by analyzing the trend of historical data and current data. The preset rules include but are not limited to: abnormal battery cell voltage, high temperature, and excessive current; S5, cloud platform connection: BMS connects to the cloud server via Wi-Fi or cellular network and uploads real-time data for remote monitoring; the real-time data comes from the abnormal data recorded in the user database in step S4, including the voltage, current, and temperature parameters of each battery cell; S51, network connection, establish a connection with the cloud server through Wi-Fi or cellular network, and verify the security and validity of the connection; S52, data upload, data preparation: read real-time data from the memory, including the voltage, current, and temperature parameters of each battery cell; package the data into a format suitable for transmission; encrypt the data using encryption algorithms such as AES to ensure transmission security; upload the encrypted data to the cloud server through the established network connection; S53, remote monitoring, the cloud server receives the uploaded data and decrypts it; the decrypted data is stored in the cloud database; real-time data is displayed through the remote monitoring interface, including normal data and abnormal data marked in red; S6, OTA update: supports online firmware upgrade, and system updates can be completed without on-site operation: S7. Visual debugging.

[0005] Preferably, in step S1, the initialization configuration includes the following steps: S11, startup detection: When the system starts, the self-test program automatically runs to confirm that all hardware components are working properly, including sensors, processors, and communication modules; When any hardware failure is detected, the system records the error information and displays it on the user interface, prompting the user to check or repair it; S12, parameter reading: reading preset battery type, capacity, and maximum discharge current parameters from the non-volatile memory; Parameter verification: Verify whether the read parameters meet the system requirements and prompt the user to enter the correct parameter values ​​as needed; S13, Usage pattern recognition: Data collection, collecting battery usage records from stored historical data; Applying statistical algorithms to analyze historical usage data and identify current battery usage patterns, including daily commuting and long-distance travel; Based on the identified usage patterns, optimize battery management methods, including adjusting charging rates and discharge limits; S14, safety threshold setting: according to the battery type and usage mode, set the corresponding safety threshold, the safety threshold includes the maximum temperature and the minimum voltage; Threshold Storage: Store defined safety thresholds in system memory for subsequent monitoring and comparison.

[0006] Preferably, in step S7, visual debugging includes the following steps: S71, Graphical interface: Provides a user-friendly graphical interface to display system status and debugging results; The displayed content includes the mean, variance, maximum, minimum, standard deviation of each battery parameter recorded in step S3, and the user debugging record in step S5; S72, Simulation test: Integrate the simulation environment in the debugging software to allow users to test system performance under virtual conditions; users can define different test scenarios, including normal working conditions, high temperature environments, and low temperature environments; S73, Multiple protections: Set up protection mechanisms for overcharge, over discharge, short circuit, over-high or under-temperature to ensure battery safety; S74. Encrypted communication: AES encryption algorithm is used to protect data transmission and prevent data from being stolen or tampered with.

[0007] Preferably, in step S4, fault prediction and self-repair include the following steps: S41, data analysis: real-time monitoring of battery parameters, and analysis of data trends using mean and variance; Load historical data from memory or cloud servers and merge historical data with current data to form a complete data set; S42, abnormality detection: when an abnormality is detected according to the extracted features, the fault prediction model is triggered; the abnormality is recorded in the memory, and an alarm message is sent; S43, fault prediction: Use historical data in the database to train a fault prediction model, detect battery current in real time based on current data and the fault prediction model, and predict possible subsequent faults; S44, self-repair attempt: attempt to repair according to preset rules. When abnormal voltage, high temperature, excessive current, etc. occur, adopt the corresponding repair form in the preset rules, including reducing output power and disconnecting the charging circuit; S45, User notification: If self-repair is ineffective, a warning message is sent to the user, instructing the user to conduct further inspection or contact technical support.

[0008] Preferably, in step S6, OTA update includes the following steps: S61, receiving an update request: the BMS system receives an update request from the cloud platform via a network connection; S62, downloading the update package: after confirming the validity of the update package, start downloading the update file to the local cache; S63, Encrypted transmission: All update data is encrypted using the AES encryption algorithm to ensure transmission security; File verification, using hash algorithms to verify the integrity of downloaded files; verifying the digital signature of files to ensure that the files have not been tampered with; S64, installing updates: after successful verification, installing update packages in a predetermined order, and installing update files into the system; S65, Rollback mechanism: After the system is started, verify whether the update is successful. If an error is encountered during the update or the system status is abnormal, roll back to the state before the update to ensure stable operation of the system; S66, Update confirmation: Send an update status report to the cloud server, including success or failure information, and record detailed logs during the update process to facilitate subsequent analysis and debugging.

[0009] Preferably, in step S3, real-time monitoring includes the following steps: S31, data acquisition, continuously reading the voltage, current and temperature data of the battery cell from the sensor, and storing the acquired data in the memory for subsequent analysis and recording; S32. Data analysis, statistical feature extraction, use statistical methods such as mean, variance, maximum, minimum, and standard deviation to extract data features; analyze the time series trend of data and identify potential anomalies; S33. Compare the current data with the preset safety threshold. If the data exceeds the threshold, it is judged as an abnormal situation. The abnormal data is recorded in the memory and marked in red to indicate that it needs attention. Compared with the prior art, the present invention has the following beneficial effects: The present invention can automatically complete system debugging during installation and operation, and monitor the battery status in real time during operation, thereby improving the reliability and efficiency of the system; the automatic debugging function greatly shortens the debugging cycle and improves work efficiency; the automation process reduces errors caused by human operation, and the real-time diagnosis and self-repair functions improve the stability and reliability of the system. Through cloud connection, remote monitoring and maintenance are supported, reducing maintenance costs; the adaptive capability is enhanced, and the statistical characteristics of various battery parameter data, such as mean, variance, maximum, minimum, standard deviation, and environmental characteristics, such as temperature and humidity, are extracted, so that the system can automatically adjust parameters according to different usage environments and battery characteristics. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 It is a work flow chart of the present invention. DETAILED DESCRIPTION

[0011] 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 described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0012] like Figure 1 As shown, a battery management method with intelligent diagnosis and adaptive debugging includes the following steps: S1. Initialization configuration: When the system starts, the built-in initialization program automatically reads and sets basic parameters such as battery type, capacity, and maximum discharge current; S11, startup detection: When the system starts, the self-test program automatically runs to confirm that all hardware components are working properly, including sensors, processors, communication modules, etc.; When any hardware failure is detected, the system records the error information and displays it on the user interface, prompting the user to check or repair it; S12, parameter reading: reading the preset basic parameters such as battery type, capacity, maximum discharge current, etc. from the non-volatile memory; Parameter verification: Verify whether the read parameters meet the system requirements and prompt the user to enter the correct parameter values ​​as needed; S13, Usage pattern recognition: Data collection, collecting battery usage records from stored historical data; Applying statistical algorithms to analyze historical usage data and identify current battery usage patterns, such as daily commuting, long-distance travel, etc.; Mode optimization, optimizing battery management strategies based on the identified usage patterns, such as adjusting charging rate, discharge limit, etc.; S14, safety threshold setting: according to the battery type and usage mode, set the corresponding safety threshold, such as maximum temperature, minimum voltage, etc.; Threshold storage: Store defined safety thresholds in system memory for subsequent monitoring and comparison; S2. Self-calibration: The sensor reads the voltage, current, temperature and other data of the battery cell and automatically adjusts the deviation to ensure accurate readings. For example, if the user's improper operation causes a large deviation in the current, if the output value of the current changes beyond the normal range, the current calibration coefficient will be automatically adjusted. S21, data collection, regularly collects data such as voltage, current, temperature, etc. of the battery unit through sensors; stores the collected data in memory for subsequent analysis and calibration; S22, deviation detection: compare the current reading with the expected value to determine whether the deviation value exceeds the preset normal range; S23, dynamic adjustment: if it is detected that the temperature change causes data deviation, the sensor calibration coefficient is adjusted according to the pre-stored temperature compensation table and updated to the data table library in step S7; For example, if the user's improper operation causes a large deviation in the current, if the current output value changes beyond the normal range, the system will automatically adjust the current and voltage; apply the updated calibration coefficient to the sensor to ensure accurate readings; S24, Deep Calibration: For persistent data deviation, a comprehensive calibration process is initiated, including sensor recalibration or troubleshooting, and the temperature, current and other information of the battery are continuously detected through duplex communication. If the sensor still has deviation, the fault information is recorded and the user is prompted to check or replace it. S3. Real-time monitoring: Continuously monitor various battery parameters (such as voltage, current, temperature, etc.) to identify abnormal conditions; use data analysis methods to extract and analyze the statistical characteristics of various battery parameter data to help the system better understand the battery status. Commonly used data analysis algorithms include mean, variance, maximum, minimum, standard deviation, etc. S31, data acquisition, continuously reading data such as voltage, current and temperature of the battery cell from the sensor, and storing the acquired data in the memory for subsequent analysis and recording; S32, data analysis, statistical feature extraction, use statistical methods (such as mean, variance, maximum, minimum, standard deviation) to extract data features; analyze the time series trend of data and identify potential anomalies; S33. Compare the current data with the preset safety threshold. If the data exceeds the threshold, it is judged as an abnormal situation. The abnormal data is recorded in the memory and marked in red to indicate that it needs attention. S4, Fault prediction and self-repair: Once an abnormality is detected, the system will attempt to self-repair according to preset rules, restart some functions or prompt the user to check; fault prediction is performed by analyzing the trend of historical data and current data. The preset rules include but are not limited to: abnormal battery cell voltage, high temperature, excessive current, etc.; S41, data analysis: real-time monitoring of battery parameters, and analysis of data trends using mean and variance; Load historical data from memory or cloud servers and merge historical data with current data to form a complete data set; S42, abnormality detection: when an abnormal situation (such as voltage fluctuation, abnormal temperature rise) is detected according to the extracted features, the fault prediction model is triggered; the abnormal situation is recorded in the memory, and an alarm message is sent; S43, fault prediction: Use historical data in the database to train a fault prediction model, detect battery current in real time based on current data and the fault prediction model, and predict possible subsequent faults; S44, self-repair attempt: attempt to repair according to preset rules. If voltage abnormality, temperature over-high, current over-high, etc. occur, the corresponding repair method in the preset rules is adopted, including reducing output power, disconnecting the charging circuit, etc.; Perform repair, execute the selected repair strategy, and try to restore the system to normal operation; Result evaluation: evaluate the repair effect. If the repair is successful, continue monitoring; otherwise, send a user notification; S45, User Notification: If self-repair is ineffective, a warning message (including the type of anomaly, location, and recommended measures) is sent to the user, guiding the user to conduct further inspection or contact technical support; S5, cloud platform connection: BMS (battery monitoring and management system) connects to the cloud server via Wi-Fi or cellular network, and uploads real-time data for remote monitoring; the real-time data comes from the abnormal data recorded in the user database in step S4, such as key parameters such as voltage, current, and temperature of each battery cell. The red color of this information indicates abnormality; S51, network connection, establish a connection with the cloud server through Wi-Fi or cellular network, and verify the security and validity of the connection; S52, data upload, data preparation: read real-time data from the memory, including key parameters such as voltage, current, temperature, etc. of each battery cell; package the data into a format suitable for transmission, such as JSON or binary format; encrypt the data using encryption algorithms such as AES to ensure transmission security; upload the encrypted data to the cloud server through the established network connection; S53, remote monitoring, the cloud server receives the uploaded data and decrypts it; the decrypted data is stored in the cloud database; real-time data is displayed through the remote monitoring interface, including normal data and abnormal data marked in red; S6, OTA update (wireless update): supports online firmware upgrade, and system updates can be completed without on-site operation: Add new functional modules, optimize existing functions, enhance user experience, improve the performance and software bugs of existing functions, and improve the security, stability and functionality of the system; S61, receiving an update request: the BMS system receives an update request from the cloud platform via a network connection; S62, downloading the update package: after confirming the validity of the update package, start downloading the update file to the local cache; S63, Encrypted transmission: All update data is encrypted using the AES encryption algorithm to ensure transmission security; File verification, using hash algorithms (such as MD5 or SHA-256) to verify the integrity of the downloaded file; verify the digital signature of the file to ensure that the file has not been tampered with; S64, installing updates: after successful verification, installing update packages in a predetermined order, and installing update files into the system; S65, Rollback mechanism: After the system is started, verify whether the update is successful. If an error is encountered during the update or the system status is abnormal, roll back to the state before the update to ensure stable operation of the system; S66, update confirmation: sending an update status report to the cloud server, including success or failure information, and recording detailed logs during the update process to facilitate subsequent analysis and debugging; S7. Visual debugging: S71, Graphical interface: Provides a user-friendly graphical interface to display system status and debugging results; The displayed content includes the mean, variance, maximum, minimum, and standard deviation of the battery parameters recorded in step S3, and the user debugging record in step S5, wherein each debugging record of the user will be recorded in the background data table and stored in the database; S72, Simulation test: Integrate the simulation environment in the debugging software to allow users to test system performance under virtual conditions; users can define different test scenarios, such as normal working state, high temperature environment, low temperature environment, etc. S73, multiple protections: set up multiple protection mechanisms such as overcharge, over discharge, short circuit, over / under temperature to ensure battery safety; S74. Encrypted communication: Use encryption algorithms such as AES (Advanced Encryption Standard) to protect data transmission and prevent data from being stolen or tampered with.

[0013] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the present invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

Claims

1. A battery management method with intelligent diagnosis and adaptive debugging, characterized in that: The following steps are involved: S1. Initialization configuration: When the system starts, the built-in initialization program automatically reads and sets the battery type, capacity, and maximum discharge current as basic parameters; S2, Self-calibration: The sensor reads the voltage, current and temperature data of the battery cell and automatically adjusts the deviation to ensure accurate readings; When the user's improper operation causes a large deviation in the current, causing the current output value to change beyond the normal range, the current calibration coefficient will be automatically adjusted; S21, data collection, regularly collecting voltage, current and temperature data of battery cells through sensors; Store the acquired data in memory for subsequent analysis and calibration; S22, deviation detection: compare the current reading with the expected value to determine whether the deviation value exceeds the preset normal range; S23, dynamic adjustment: if it is detected that the temperature change causes data deviation, the sensor calibration coefficient is adjusted according to the pre-stored temperature compensation table and updated to the data table library in step S7; Improper operation by the user causes a large deviation in the current, causing the output value of the current to change beyond the normal range. The system will automatically adjust the current and voltage; apply the updated calibration coefficient to the sensor to ensure accurate readings; S24, Deep Calibration: For persistent data offsets, a comprehensive calibration process is initiated, including sensor recalibration or troubleshooting, and the temperature and current information of the battery are continuously detected through duplex communication; If the sensor still has deviation, the fault information is recorded and the user is prompted to check or replace it; S3, real-time monitoring: continuously monitor various battery parameters and identify abnormal conditions; use data analysis methods to extract and analyze the statistical characteristics of various battery parameter data and monitor battery status. Data analysis algorithms include mean, variance, maximum, minimum, and standard deviation; S4, Fault prediction and self-repair: Once an anomaly is detected, the system will attempt to self-repair according to preset rules, restart some functions or prompt the user to check; Fault prediction is performed by analyzing the trends of historical data and current data. The preset rules include but are not limited to: abnormal battery cell voltage, excessive temperature, and excessive current; S5, cloud platform connection: BMS connects to the cloud server via Wi-Fi or cellular network and uploads real-time data for remote monitoring; the real-time data comes from the abnormal data recorded in the user database in step S4, including the voltage, current, and temperature parameters of each battery cell; S51, network connection, establish a connection with the cloud server through Wi-Fi or cellular network, and verify the security and validity of the connection; S52, data upload, data preparation: read real-time data from the memory, including the voltage, current, and temperature parameters of each battery cell; package the data into a format suitable for transmission; encrypt the data using encryption algorithms such as AES to ensure transmission security; upload the encrypted data to the cloud server through the established network connection; S53, remote monitoring, the cloud server receives the uploaded data and decrypts it; the decrypted data is stored in the cloud database; real-time data is displayed through the remote monitoring interface, including normal data and abnormal data marked in red; S6, OTA update: supports online firmware upgrade, and system updates can be completed without on-site operation: S7. Visual debugging.

2. The battery management method with intelligent diagnosis and adaptive debugging according to claim 1 is characterized in that: In step S1, the initialization configuration includes the following steps: S11, startup detection: When the system starts, the self-test program automatically runs to confirm that all hardware components are working properly, including sensors, processors, and communication modules; When any hardware failure is detected, the system records the error information and displays it on the user interface, prompting the user to check or repair it; S12, parameter reading: reading preset battery type, capacity, and maximum discharge current parameters from the non-volatile memory; Parameter verification: Verify whether the read parameters meet the system requirements and prompt the user to enter the correct parameter values ​​as needed; S13, usage pattern recognition: data collection, collecting battery usage records from stored historical data; applying statistical algorithms to analyze historical usage data and identify current battery usage patterns, including daily commuting and long-distance travel; Optimize battery management based on identified usage patterns, including adjusting charging rates and discharge limits; S14, safety threshold setting: according to the battery type and usage mode, set the corresponding safety threshold, the safety threshold includes the maximum temperature and the minimum voltage; Threshold Storage: Store defined safety thresholds in system memory for subsequent monitoring and comparison.

3. The battery management method of intelligent diagnosis and adaptive debugging according to claim 2 is characterized in that: In step S7, visual debugging includes the following steps: S71, Graphical interface: Provides a user-friendly graphical interface to display system status and debugging results; The displayed content includes the mean, variance, maximum, minimum, standard deviation of each battery parameter recorded in step S3, and the user debugging record in step S5; S72, Simulation test: Integrate the simulation environment in the debugging software to allow users to test system performance under virtual conditions; users can define different test scenarios, including normal working conditions, high temperature environments, and low temperature environments; S73, Multiple protections: Set up protection mechanisms for overcharge, over discharge, short circuit, over-high or under-temperature to ensure battery safety; S74. Encrypted communication: AES encryption algorithm is used to protect data transmission and prevent data from being stolen or tampered with.

4. The battery management method with intelligent diagnosis and adaptive debugging according to claim 1 is characterized in that: In step S4, fault prediction and self-repair include the following steps: S41, data analysis: real-time monitoring of battery parameters, and analysis of data trends using mean and variance; Load historical data from memory or cloud servers and merge historical data with current data to form a complete data set; S42, anomaly detection: when an abnormal situation is detected according to the extracted features, the fault prediction model is triggered; the abnormal situation is recorded in the memory, and an alarm message is sent; S43, fault prediction: Use historical data in the database to train a fault prediction model, detect battery current in real time based on current data and the fault prediction model, and predict possible subsequent faults; S44, self-repair attempt: attempt to repair according to preset rules. When abnormal voltage, high temperature, excessive current, etc. occur, adopt the corresponding repair form in the preset rules, including reducing output power and disconnecting the charging circuit; S45, User notification: If self-repair is ineffective, a warning message is sent to the user, instructing the user to conduct further inspection or contact technical support.

5. The battery management method with intelligent diagnosis and adaptive debugging according to claim 1 is characterized in that: In step S6, the OTA update includes the following steps: S61, receiving an update request: the BMS system receives an update request from the cloud platform via a network connection; S62, downloading the update package: after confirming the validity of the update package, start downloading the update file to the local cache; S63, Encrypted transmission: All update data is encrypted using the AES encryption algorithm to ensure transmission security; File verification, using hash algorithms to verify the integrity of downloaded files; verifying the digital signature of files to ensure that the files have not been tampered with; S64, installing updates: after successful verification, installing update packages in a predetermined order, and installing update files into the system; S65, Rollback mechanism: After the system is started, verify whether the update is successful. If an error is encountered during the update or the system status is abnormal, roll back to the state before the update to ensure stable operation of the system; S66, Update confirmation: Send an update status report to the cloud server, including success or failure information, and record detailed logs during the update process to facilitate subsequent analysis and debugging.

6. The battery management method with intelligent diagnosis and adaptive debugging according to claim 1 is characterized in that: In step S3, real-time monitoring includes the following steps: S31, data acquisition, continuously reading the voltage, current and temperature data of the battery cell from the sensor, and storing the acquired data in the memory for subsequent analysis and recording; S32. Data analysis, statistical feature extraction, use statistical methods such as mean, variance, maximum, minimum, and standard deviation to extract data features; analyze the time series trend of data and identify potential anomalies; S33. Compare the current data with a preset safety threshold. If the data exceeds the threshold, it is judged as an abnormal situation. The abnormal data is recorded in the memory and marked in red to indicate that attention is needed.

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