A dynamic adjustment strategy for the internal resistance change of an aging battery and a battery control method thereof

Abstract

The application provides a dynamic adjustment strategy for the resistance change of an aging battery and a battery control method. The method comprises the following steps: monitoring the output end of the started battery in real time to generate battery output voltage data; judging the resistance change state of the battery according to the battery output voltage data to generate internal resistance change trend data; deploying a semiconductor resistor with an adjustable resistance value at the output end of the battery based on the internal resistance change trend data to construct a dynamic resistance adjustment network; and adjusting the resistance value of the semiconductor adjustable resistor through an MCU, thereby improving the stability of the battery output voltage, ensuring that the vehicle system always works in a normal voltage range, reducing the voltage fluctuation risk caused by the increased internal resistance of the aging battery, and effectively reducing the occurrence of the situation that the vehicle system misjudges the battery failure and refuses to start.

Description

Technical Field

[0001] This invention proposes a dynamic adjustment strategy and battery control method for changes in the internal resistance of aging batteries, belonging to the field of battery control technology. Background Technology

[0002] During the use of a starter battery, with the increase in years of use and the number of charge-discharge cycles, the battery pack inevitably undergoes aging, one of the most significant characteristics of which is the gradual increase in internal resistance. This increased internal resistance directly leads to a drop in voltage across the battery terminals when it outputs electrical energy. This voltage fluctuation can not only cause the vehicle's infotainment system to misjudge the battery's condition, such as mistakenly believing the battery has failed and refusing to start the car, but it can also cause insufficient power supply to the infotainment system, triggering various alarm mechanisms and seriously affecting the normal use of the vehicle and the user experience.

[0003] Traditional methods for addressing increased battery internal resistance, such as adjusting circuit parameters by cutting wire lengths, are not only cumbersome and time-consuming, but also have low adjustment accuracy and a low probability of successful adaptation, making it difficult to meet the high requirements of modern vehicles for battery performance stability.

[0004] Against this backdrop, the development of a battery control method capable of dynamically adjusting the resistance value at the battery output terminal to adapt to changes in internal resistance during battery aging is particularly urgent. This method, through intelligent MCU control, automatically reduces the resistance value of the adjustable semiconductor resistor when a voltage drop is detected, ensuring that the battery output voltage remains within the normal operating range of the vehicle's infotainment system. This effectively solves the problems of misjudgment and insufficient power supply caused by battery aging, thereby improving the overall stability and reliability of the battery system. Summary of the Invention

[0005] This invention provides a dynamic adjustment strategy and battery control method for addressing changes in the internal resistance of aging batteries, thereby solving the problems mentioned in the background section. This invention proposes a battery control method with a dynamic adjustment strategy for changes in the internal resistance of aging batteries, the method comprising: S1. Real-time monitoring of the output voltage of the battery is performed to generate battery output voltage data; the battery internal resistance change status is determined based on the battery output voltage data to generate internal resistance change trend data; based on the internal resistance change trend data, an adjustable semiconductor resistor is deployed at the battery output to construct a dynamic resistance adjustment network. S2. Perform real-time voltage threshold analysis based on the dynamic resistor adjustment network. When the battery output voltage is detected to be lower than the preset threshold, generate voltage abnormality signal data. Based on the voltage abnormality signal data, dynamically reduce the resistance value of the semiconductor resistor through the MCU control circuit to generate the adjusted resistance value data. S3. Evaluate the voltage recovery effect based on the adjusted resistance value data and generate voltage recovery trend data; analyze the power supply stability of the vehicle system based on the voltage recovery trend data and generate power supply stability index data; assess the misjudgment risk of the vehicle system based on the power supply stability index data and generate misjudgment risk level data. S4. Optimize the remote monitoring parameters of the dynamic resistance adjustment network using the misjudgment risk level data, and generate optimized adjustment parameter data; adjust the semiconductor resistor remotely and dynamically based on the optimized adjustment parameter data, and generate remote adjustment execution data; verify the adjustment effect of the remote adjustment execution data, and generate adjustment verification result data. S5. Calculate the battery system stability index based on the adjustment and verification results data, and generate the battery system stability index; perform risk warning processing based on the battery system stability index, and generate battery disaster warning data.

[0006] This invention proposes a system for implementing a battery control method based on the dynamic adjustment strategy for changes in the internal resistance of an aging battery as described above. The system comprises: Network construction module: Real-time voltage monitoring of the output terminal of the battery to generate battery output voltage data; Determine the battery internal resistance change state based on the battery output voltage data and generate internal resistance change trend data; Deploy adjustable semiconductor resistors at the battery output terminal based on the internal resistance change trend data to construct a dynamic resistance adjustment network. Resistor adjustment module: Performs real-time voltage threshold analysis based on dynamic resistor adjustment network. When the battery output voltage is detected to be lower than the preset threshold, it generates voltage abnormality signal data. Based on the voltage abnormality signal data, the MCU control circuit dynamically reduces and adjusts the resistance value of the semiconductor resistor, generating the adjusted resistance value data. Voltage recovery module: Evaluates the voltage recovery effect based on the adjusted resistance value data and generates voltage recovery trend data; performs power supply stability analysis on the vehicle system based on the voltage recovery trend data and generates power supply stability index data; assesses the risk of misjudgment in the vehicle system based on the power supply stability index data and generates misjudgment risk level data. The adjustment execution module: remotely monitors and optimizes the parameters of the dynamic resistance adjustment network based on the risk level data of misjudgment, and generates optimized adjustment parameter data; remotely and dynamically adjusts the resistance value of the semiconductor resistor based on the optimized adjustment parameter data, and generates remote adjustment execution data; verifies the adjustment effect of the remote adjustment execution data, and generates adjustment verification result data. Risk warning module: Calculates the battery system stability index based on the adjustment and verification results data, and generates the battery system stability index; performs risk warning processing based on the battery system stability index, and generates battery disaster warning data.

[0007] The beneficial effects of this invention are as follows: By intelligently controlling the resistance value of the adjustable semiconductor resistor through the MCU, the stability of the battery output voltage is improved, ensuring that the vehicle system always operates within the normal voltage range; the risk of voltage fluctuations caused by increased internal resistance due to battery aging is reduced, effectively reducing the occurrence of the vehicle system misjudging battery failure and refusing to start the engine; the adaptability of the battery system to different aging levels is enhanced, avoiding frequent triggering of various alarm mechanisms caused by insufficient power supply; it can dynamically adjust the resistance value in real time to cope with the instantaneous changes in battery internal resistance, and can also optimize parameters through remote monitoring, reducing the tediousness and inconvenience of manual on-site debugging; during the battery aging process, it effectively avoids abnormal operation of the vehicle system caused by increased internal resistance, significantly improving the overall reliability of the vehicle and the user experience, and providing a strong guarantee for the long-term stable use of the start-up battery. Attached Figure Description

[0008] Figure 1 This is a diagram illustrating the steps of the method described in this invention; Figure 2 This is a system module diagram of the present invention. Detailed Implementation

[0009] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0010] One embodiment of the present invention, such as Figure 1 As shown, a battery control method with a dynamic adjustment strategy for changes in the internal resistance of an aging battery is disclosed, the method comprising: S1. Real-time monitoring of the output voltage of the battery is performed to generate battery output voltage data; the battery internal resistance change status is determined based on the battery output voltage data to generate internal resistance change trend data; based on the internal resistance change trend data, an adjustable semiconductor resistor is deployed at the battery output to construct a dynamic resistance adjustment network. S2. Perform real-time voltage threshold analysis based on the dynamic resistor adjustment network. When the battery output voltage is detected to be lower than the preset threshold, generate voltage abnormality signal data. Based on the voltage abnormality signal data, dynamically reduce the resistance value of the semiconductor resistor through the MCU control circuit to generate the adjusted resistance value data. S3. Evaluate the voltage recovery effect based on the adjusted resistance value data and generate voltage recovery trend data; analyze the power supply stability of the vehicle system based on the voltage recovery trend data and generate power supply stability index data; assess the misjudgment risk of the vehicle system based on the power supply stability index data and generate misjudgment risk level data. S4. Optimize the remote monitoring parameters of the dynamic resistance adjustment network using the misjudgment risk level data, and generate optimized adjustment parameter data; adjust the semiconductor resistor remotely and dynamically based on the optimized adjustment parameter data, and generate remote adjustment execution data; verify the adjustment effect of the remote adjustment execution data, and generate adjustment verification result data. S5. Calculate the battery system stability index based on the adjustment and verification results data, and generate the battery system stability index; perform risk warning processing based on the battery system stability index, and generate battery disaster warning data.

[0011] The working principle and effects of the above technical solution are as follows: By monitoring the battery output voltage in real time and dynamically judging the changes in internal resistance, combined with a dynamic adjustment network constructed using semiconductor resistors, it can accurately adapt to the dynamic changes in the internal resistance of aging batteries, effectively improving the power supply stability of the vehicle system and reducing malfunctions caused by voltage fluctuations. The dynamic adjustment of semiconductor resistor values ​​allows for rapid response to voltage anomalies, avoiding false triggering of the vehicle system due to excessively low voltage, and reducing the adverse effects of battery aging on vehicle operation. Remote monitoring parameter optimization further improves adjustment accuracy, enhances the adaptability of the strategy to batteries with different aging levels, and reduces manual intervention costs. Combined with risk warning processing based on the stability index, it can predict potential battery safety hazards in advance, preventing battery disasters, ensuring the continued effective use of aging batteries, and providing reliable protection for vehicle operation safety.

[0012] In one embodiment of the present invention, S1 includes: S11. Acquire the voltage signal at the output terminal of the battery with a sampling period of 10ms. After removing high-frequency interference by low-pass filtering, organize the data according to the timestamp order to generate real-time battery voltage sequence data. S12. Combining the battery's rated voltage parameters with real-time voltage sequence data, the internal resistance value is derived using Ohm's law, and dynamic change data of internal resistance is continuously calculated and generated in the time dimension. S13. Based on the dynamic change data of internal resistance, a trend prediction model is constructed using a linear regression algorithm. Input the internal resistance data of the past 5 minutes to calculate the change trajectory of the next 10 minutes and generate the future change trajectory data of internal resistance. S14. Determine the maximum resistance adjustment requirement based on the future change trajectory data of internal resistance, screen semiconductor resistor elements with matching withstand voltage and response speed, and generate resistor selection and adaptation data. S15. Based on resistor selection and adaptation data, semiconductor resistors are symmetrically arranged in the positive and negative output circuits of the battery, and connected by wires to form a star topology to generate a dynamic resistance adjustment architecture. S16. Perform loop continuity test on the dynamic resistance adjustment architecture, collect current signals of each branch to verify connection reliability, and generate architecture continuity verification data.

[0013] The working principle and effects of the above technical solution are as follows: By combining 10ms high-frequency sampling with low-pass filtering to process the battery voltage signal, high-frequency interference can be effectively eliminated, improving the accuracy of real-time battery voltage sequence data and avoiding deviations in internal resistance judgment caused by interference signals. Internal resistance is derived by combining rated voltage and voltage sequence data, and a linear regression algorithm is used to predict future changes, allowing for early understanding of internal resistance trends. This makes semiconductor resistor selection more aligned with actual needs and enhances the adaptability of the dynamic resistance adjustment architecture. Symmetrical resistor placement in a star topology and loop continuity testing verify the reliability of the architecture connection, preventing loop breaks or poor contact during subsequent adjustment, reducing the risk of adjustment failure due to architecture malfunction, and laying a solid foundation for subsequent precise dynamic adjustment.

[0014] In one embodiment of the present invention, S2 includes: S21. For battery application scenarios corresponding to the dynamic resistance regulation architecture, and in combination with the minimum operating voltage requirements of the vehicle system, set upper and lower limit voltage monitoring ranges and generate voltage reference range data. S22. Compare the real-time collected battery output voltage with the voltage reference interval data point by point, calculate the difference between the voltage deviation from the interval boundary, and generate voltage deviation quantification data. S23. Set a deviation threshold. When the voltage deviation quantization data exceeds the threshold, the voltage is determined to be in an abnormal state, and a voltage abnormality trigger signal is generated. The voltage abnormality trigger signal is transmitted to the MCU control circuit, which starts the preset resistance adjustment program and generates circuit drive instruction data. S24. The MCU control circuit outputs a pulse width modulation signal based on the circuit drive instruction data to change the conduction degree of the semiconductor resistor and generate dynamic resistance adjustment data. S25. Real-time acquisition of the actual resistance value of the semiconductor resistor after adjustment, comparison and calibration with the dynamic resistance adjustment data, and generation of resistance calibration feedback data.

[0015] The working principle and effects of the above technical solution are as follows: A voltage monitoring range is set based on the minimum operating voltage requirements of the vehicle's infotainment system, making the voltage reference more closely match actual application scenarios and avoiding issues such as missed or false alarms caused by excessively wide or narrow range settings. Comparing the real-time voltage with the reference range point by point to generate quantified deviation data improves the accuracy of voltage anomaly identification and makes anomaly judgment more reliable. Voltage anomaly signals are quickly transmitted to the MCU and the adjustment program is initiated, ensuring timely response to voltage anomalies and reducing the duration of voltage anomalies. The MCU adjusts the conduction level of the semiconductor resistor through pulse width modulation signals, allowing for more precise and delicate resistance adjustment and improving voltage recovery. Real-time acquisition of actual resistance values ​​and comparison with adjustment data for calibration avoids voltage instability caused by adjustment deviations, providing accurate data support for subsequent voltage recovery assessment and further ensuring the stability of the vehicle's power supply.

[0016] In one embodiment of the present invention, S24 includes: The MCU control circuit parses the circuit drive command data, extracts the target parameters for resistance adjustment, and generates PWM signal configuration data. Based on the PWM signal configuration data, the MCU control circuit generates the corresponding pulse width modulation signal; the pulse width modulation signal is then transmitted to the control port of the semiconductor resistor through the control line. After receiving a pulse width modulation signal, the semiconductor resistor adjusts the duty cycle of its internal conduction circuit to change the degree of conduction; the resistance parameters corresponding to the change in the degree of conduction of the semiconductor resistor are recorded to generate dynamic resistance adjustment data.

[0017] The working principle and effects of the above technical solution are as follows: By analyzing the circuit drive commands to extract the target parameters for resistance adjustment, the PWM signal configuration can better match the actual adjustment needs, avoiding the problem of inaccurate resistance adjustment caused by deviation between the configured parameters and the adjustment target. Based on the configuration data, a corresponding pulse width modulation signal is generated, ensuring the accuracy of the signal parameters and improving the matching degree between the signal and the semiconductor resistor adjustment requirements. The control circuit transmits the pulse width modulation signal in a directional manner, reducing interference and loss during signal transmission and avoiding resistance adjustment delays or malfunctions caused by signal distortion. The semiconductor resistor changes the conduction degree by adjusting the duty cycle of the conduction circuit, making the resistance adjustment more delicate and controllable, improving the accuracy of resistance adjustment, and reducing voltage fluctuations. Recording the resistance parameters corresponding to the conduction degree generates adjustment data, providing an accurate basis for subsequent resistance calibration, avoiding the cumulative effect of adjustment deviations on power supply stability, and further ensuring the stability of the vehicle system's power supply.

[0018] In one embodiment of the present invention, S3 includes: S31. Based on the resistance calibration feedback data, the battery output voltage is collected at 500μs intervals, the voltage change before and after adjustment is recorded, and the adjusted voltage feedback data is generated. S32. Calculate the voltage difference between adjacent sampling points in the adjusted voltage feedback data, divide it by the time interval to obtain the voltage recovery rate, and generate voltage recovery slope data. S33. Based on the voltage rise slope data and combined with the power requirements of each power module of the vehicle system, analyze the ability of the power supply to continuously meet the demand and generate power supply continuity data; use the analytic hierarchy process to calculate the power supply indicators by weighting and generate system stability quantitative indicators, including continuity data, voltage fluctuation amplitude, and current stability. S34. Based on the numerical range of the system stability quantitative indicators, divide the risk into three levels: low, medium, and high, and generate risk classification judgment data. S35. Based on the risk classification data, analyze the possibility of the vehicle system malfunctioning due to unstable power supply, supplement and improve the risk description, and generate a detailed risk assessment report.

[0019] The working principle and effects of the above technical solution are as follows: By acquiring battery output voltage at 500μs intervals and recording changes before and after adjustment, it can accurately capture subtle voltage fluctuations, improve the timeliness and completeness of voltage feedback data after adjustment, and avoid missing key voltage change information due to excessively large sampling intervals. The voltage recovery slope is calculated to intuitively present the voltage recovery trend. Combined with the power demand analysis of various power modules in the vehicle, the power supply continuity is analyzed, making the power supply capacity assessment more consistent with actual operating conditions and reducing one-sided assessments that are detached from actual needs. The Analytic Hierarchy Process (AHP) is used to weightedly integrate multiple power supply indicators to generate quantitative stability indicators, improving the comprehensiveness and accuracy of system stability assessment and avoiding assessment bias caused by single-indicator judgments. Risk levels are classified and detailed risk reports are generated, clearly defining the degree of power supply instability risk, predicting the possibility of erroneous triggering of vehicle functions in advance, avoiding subsequent adjustment delays due to unclear risks, providing a clear basis for subsequent parameter optimization, and further enhancing the controllability of the vehicle's power supply.

[0020] In one embodiment of the present invention, S33 includes: Collect power demand parameters of each power module of the vehicle infotainment system, classify and organize them according to module function type, and generate module power demand dataset; By combining the module power demand dataset with the voltage rise slope data, the continuous power supply capability under different power loads is analyzed, and power supply continuity data is generated. Collect the voltage fluctuation amplitude and current stability parameters of the battery output, integrate the power supply continuity data, and generate a comprehensive power supply index set; The Analytic Hierarchy Process (AHP) is applied to perform weighted calculations on various parameters in the comprehensive power supply index set to generate quantitative indicators for system stability.

[0021] The working principle and effects of the above technical solution are as follows: Categorizing and organizing the power requirements of each electrical module in the vehicle's infotainment system according to functional type makes the power data more organized, avoiding analytical biases caused by mixed power information from different modules, and improving the targeting of power supply capacity assessment. Combining the power demand dataset with voltage rise slope data to analyze the continuous power supply capability ensures that the judgment of power supply continuity closely reflects the actual load conditions, reducing one-sided assessments detached from operating conditions. Integrating voltage fluctuation amplitude, current stability, and power supply continuity to generate a comprehensive power supply index set improves the comprehensiveness of power supply status assessment and avoids missing key stability factors in single-index analysis. Applying the analytic hierarchy process (AHP) to weightedly calculate various indicators to generate a quantitative index of system stability makes stability assessment more accurate and objective, reduces errors caused by subjective judgment, provides a reliable basis for subsequent risk level classification, and further ensures the scientific nature of the vehicle's infotainment system power supply stability assessment.

[0022] In one embodiment of the present invention, step S4 includes: S41. Based on risk classification judgment data and detailed risk assessment report, optimize the parameters of remote monitoring, including voltage monitoring threshold and resistance adjustment sensitivity, and generate monitoring parameter correction data; based on the monitoring parameter correction data, adjust the semiconductor resistor adjustment algorithm, optimize the step size and response time of resistance change, and generate resistance adjustment algorithm data. S42. The resistance adjustment algorithm data is encapsulated into control commands through the wireless communication module and sent to the dynamic resistance adjustment architecture to generate a remote adjustment trigger signal. S43. After receiving the remote adjustment trigger signal, the dynamic resistance adjustment architecture drives the semiconductor resistor to perform resistance adjustment operation and generates actual adjustment operation data. S44. Collect the battery voltage, current and semiconductor resistance value corresponding to the actual adjustment operation data, comprehensively evaluate the adjustment effect and generate adjustment effect feedback data. S45. Compare the adjustment effect feedback data with the expected adjustment target to determine whether the stable power supply requirements have been met, and generate the adjustment effect verification result.

[0023] The working principle and effects of the above technical solution are as follows: By combining risk classification data and detailed assessment reports to optimize remote monitoring parameters and adjustment algorithms, the monitoring threshold, resistance adjustment step size, and response time are made more suitable for actual risk conditions, improving the accuracy of remote monitoring and the rationality of the adjustment algorithm, and avoiding adjustment lag or over-adjustment problems caused by parameter deviations. Control commands are encapsulated and sent through a wireless communication module to achieve precise remote triggering of adjustments, reducing the cost and operational risks of manual on-site intervention and avoiding interference with vehicle operation caused by on-site operations. The dynamic resistance adjustment architecture responds to commands to execute resistance adjustments, ensuring the timeliness of adjustment actions and improving adjustment efficiency. Multi-dimensional data is collected to comprehensively evaluate the adjustment effect and compare it with the expected target for verification. This clearly determines whether the adjustment has met the standard, avoiding ineffective adjustments from continuously affecting power supply stability, providing accurate feedback for subsequent strategy optimization, further enhancing the closed-loop controllability of the entire adjustment system, and ensuring long-term stable power supply to the vehicle's system.

[0024] In one embodiment of the present invention, S41 includes: By combining risk classification data and detailed risk assessment reports, key points for parameter optimization are extracted, and parameter optimization guidance data is generated. Based on parameter optimization guidance data, adjust the voltage monitoring threshold of remote monitoring to generate threshold correction data; integrate the threshold correction data to optimize the resistance adjustment sensitivity parameter of remote monitoring to generate monitoring parameter correction data. Extract the adjustment requirements from the monitoring parameter correction data, sort out the adjustment direction of the semiconductor resistor adjustment algorithm, and generate algorithm adjustment guidance data; Based on the algorithm adjustment guidance data, the adjustment algorithm of the semiconductor resistor is adjusted, the step size and response time of the resistance value change are optimized, and resistance value adjustment algorithm data is generated.

[0025] The working principle and effects of the above technical solution are as follows: By combining risk classification data with detailed assessment reports to extract key points for parameter optimization, subsequent adjustments become more targeted, avoiding parameter mismatch issues caused by blind optimization. Precise adjustment of the voltage monitoring threshold for remote monitoring optimizes resistance adjustment sensitivity, improving the adaptability of monitoring parameters to actual risk states and reducing false alarms and missed alarms caused by improper threshold or sensitivity settings. The algorithm adjustment direction is streamlined, and the adjustment step size and response time of semiconductor resistors are optimized, making the adjustment algorithm more aligned with actual needs, improving the accuracy and timeliness of resistance adjustment, and avoiding over-adjustment or lag that could affect power supply stability. The entire process forms a closed loop from parameter optimization to algorithm adjustment, enhancing both the reliability of remote monitoring and the flexibility of the adjustment strategy, providing solid support for subsequent remote dynamic adjustment, and further ensuring the stability of the vehicle's power supply.

[0026] In one embodiment of the present invention, S42 includes: Extract the core adjustment parameters and execution logic from the resistance adjustment algorithm data to generate a set of core data elements; Based on the core data element set, a basic format framework for control instructions is constructed, and instruction framework data is generated. The core data elements are populated into the instruction framework data to complete the encapsulation of control instructions and generate complete control instructions; a suitable wireless communication protocol is selected to generate protocol adaptation parameters. By loading protocol adaptation parameters through the wireless communication module, complete control commands are sent to the dynamic resistance regulation architecture to generate a remote regulation trigger signal.

[0027] The working principle and effects of the above technical solution are as follows: Extracting the core parameters and execution logic from the resistance adjustment algorithm eliminates redundant information, making the core elements of the control command more prominent and avoiding invalid data consuming transmission resources or causing command parsing chaos. Constructing a unified control command format framework and filling it with core elements makes the command structure more standardized, improves command parsing efficiency, and reduces parsing failures caused by inconsistent formats. Selecting an appropriate wireless communication protocol and loading corresponding parameters enhances the compatibility and stability of command transmission, reduces signal loss or interference during transmission, and avoids command loss or distortion. Accurately sending complete control commands through the wireless communication module ensures timely delivery of remote adjustment trigger signals, allowing the dynamic resistance adjustment architecture to quickly respond to adjustment needs. This improves both the timeliness of remote adjustment and the reliability of adjustment command transmission, laying the foundation for subsequent accurate resistance adjustment.

[0028] In one embodiment of the present invention, step S5 includes: S51. Based on the verification results of the adjustment effect, integrate relevant parameters, including battery voltage stability, current fluctuation range, internal resistance change amplitude, and semiconductor resistance working state, to generate a comprehensive battery operation dataset; use the entropy weight method to determine the weight of each parameter, calculate the stability score of the battery system through a multi-index comprehensive evaluation model, and generate the battery stability index. S52. Combining historical data of battery stability index and industry safety standards, set different levels of warning threshold ranges and generate warning trigger condition data. S53. Real-time monitoring of changes in the battery stability index; when the value reaches the threshold in the warning trigger condition data, a risk warning trigger signal is generated. S54. Based on the level of the risk warning trigger signal, output the corresponding audio-visual prompts and text warning content to generate battery safety warning data; synchronize the battery safety warning data to the vehicle control system and user terminal to perform multi-terminal warning synchronization and generate warning synchronization feedback data.

[0029] The working principle and effects of the above technical solution are as follows: It integrates multi-dimensional parameters such as battery voltage stability and current fluctuation range to generate a comprehensive operational dataset, which can fully reflect the battery system's operating status and avoid the bias caused by single-parameter evaluation. The entropy weight method is used to determine the weight of each parameter and calculate the stability state index, making stability assessment more objective and accurate, reducing errors caused by subjective judgment. By combining historical index data and industry safety standards to set warning threshold ranges, the warning triggering conditions are made more aligned with actual safety needs, avoiding missed or false warnings caused by thresholds that are too high or too low. Real-time monitoring of index changes and triggering corresponding level warnings improves the timeliness of battery risk detection and prevents small risks from accumulating into serious malfunctions. Multi-terminal synchronized warning data allows the vehicle control system and user terminals to obtain warning information in a timely manner, reducing untimely responses caused by information transmission delays. This ensures battery operational safety and provides users and vehicle systems with sufficient preparation time.

[0030] In one embodiment of the present invention, S52 includes: Collect historical operating records of the battery stability index, sort and organize them according to the time dimension, and generate a historical index dataset; collect relevant standard parameters for safe operation in the battery industry, classify and summarize them to generate industry standard reference data. By comparing and analyzing historical index datasets with industry standard reference data, the core basis for threshold division is identified, and threshold division reference data is generated. Based on the threshold classification reference data, the warning threshold ranges of low, medium and high levels are divided to generate graded threshold interval data; Integrate the tiered threshold range data with the corresponding warning level identifiers to generate warning trigger condition data.

[0031] The working principle and effects of the above technical solution are as follows: First, the historical records of the battery stability index are organized according to the time dimension, making the historical dataset more regular and avoiding the problem of unclear threshold division due to the disorganization of historical data. Second, battery industry safety standard parameters are categorized and summarized, providing authoritative references for threshold setting and avoiding unreasonable thresholds due to deviations from industry standards. Third, the core basis is identified by comparing and analyzing the historical dataset with industry standards, making threshold division more supported and reducing deviations caused by blindly setting thresholds. Fourth, three levels of warning thresholds (low, medium, and high) are defined, making the warning levels clearer and avoiding situations where a single threshold cannot accurately distinguish the degree of risk. Fifth, the tiered thresholds and warning level identifiers are integrated to generate trigger condition data, making subsequent warning judgments smoother and reducing false or missed warnings caused by confusion in the correspondence between levels and thresholds, further improving the accuracy and reliability of risk warnings.

[0032] One embodiment of the present invention, such as Figure 2 As shown, a system for implementing a battery control method for a dynamic adjustment strategy for changes in the internal resistance of an aging battery as described above is characterized in that the system comprises: Network construction module: Real-time voltage monitoring of the output terminal of the battery to generate battery output voltage data; Determine the battery internal resistance change state based on the battery output voltage data and generate internal resistance change trend data; Deploy adjustable semiconductor resistors at the battery output terminal based on the internal resistance change trend data to construct a dynamic resistance adjustment network. Resistor adjustment module: Performs real-time voltage threshold analysis based on dynamic resistor adjustment network. When the battery output voltage is detected to be lower than the preset threshold, it generates voltage abnormality signal data. Based on the voltage abnormality signal data, the MCU control circuit dynamically reduces and adjusts the resistance value of the semiconductor resistor, generating the adjusted resistance value data. Voltage recovery module: Evaluates the voltage recovery effect based on the adjusted resistance value data and generates voltage recovery trend data; performs power supply stability analysis on the vehicle system based on the voltage recovery trend data and generates power supply stability index data; assesses the risk of misjudgment in the vehicle system based on the power supply stability index data and generates misjudgment risk level data. The adjustment execution module: remotely monitors and optimizes the parameters of the dynamic resistance adjustment network based on the risk level data of misjudgment, and generates optimized adjustment parameter data; remotely and dynamically adjusts the resistance value of the semiconductor resistor based on the optimized adjustment parameter data, and generates remote adjustment execution data; verifies the adjustment effect of the remote adjustment execution data, and generates adjustment verification result data. Risk warning module: Calculates the battery system stability index based on the adjustment and verification results data, and generates the battery system stability index; performs risk warning processing based on the battery system stability index, and generates battery disaster warning data.

[0033] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A battery control method with a dynamic adjustment strategy for changes in the internal resistance of an aging battery, characterized in that, The method includes: S1. Monitor the voltage at the output terminal of the battery in real time, generate battery output voltage data, determine the battery internal resistance change state, and generate internal resistance change trend data; deploy semiconductor resistors at the battery output terminal to construct a dynamic resistance adjustment network. S2. Perform real-time voltage threshold analysis. When the battery output voltage is detected to be lower than the preset threshold, generate voltage abnormality signal data. Dynamically reduce and adjust the resistance value of the semiconductor resistor and generate the adjusted resistance value data. S3. Evaluate the voltage recovery effect and generate voltage recovery trend data; perform power supply stability analysis on the vehicle infotainment system and generate power supply stability index data; conduct a misjudgment risk assessment on the vehicle infotainment system and generate misjudgment risk level data. S4. Perform remote monitoring parameter optimization and generate optimized adjustment parameter data; perform remote dynamic resistance adjustment and generate remote adjustment execution data; perform adjustment effect verification and generate adjustment verification result data. S5. Calculate the battery system stability index based on the adjustment and verification results data, and generate the battery system stability index; perform risk warning processing based on the battery system stability index, and generate battery disaster warning data.

2. The battery control method according to claim 1, which employs a dynamic adjustment strategy to address changes in the internal resistance of an aging battery, is characterized in that... S1 includes: S11. Acquire the voltage signal at the output terminal of the battery with a sampling period of 10ms. After removing high-frequency interference by low-pass filtering, organize the data according to the timestamp order to generate real-time battery voltage sequence data. S12. Combining the battery's rated voltage parameters with real-time voltage sequence data, the internal resistance value is derived using Ohm's law, and dynamic change data of internal resistance is continuously calculated and generated in the time dimension. S13. Based on the dynamic change data of internal resistance, a trend prediction model is constructed using a linear regression algorithm. Input the internal resistance data of the past 5 minutes to calculate the change trajectory of the next 10 minutes and generate the future change trajectory data of internal resistance. S14. Determine the maximum resistance adjustment requirement based on the future change trajectory data of internal resistance, screen semiconductor resistor elements with matching withstand voltage and response speed, and generate resistor selection and adaptation data. S15. Based on resistor selection and adaptation data, semiconductor resistors are symmetrically arranged in the positive and negative output circuits of the battery, and connected by wires to form a star topology to generate a dynamic resistance adjustment architecture. S16. Perform loop continuity test on the dynamic resistance adjustment architecture, collect current signals of each branch to verify connection reliability, and generate architecture continuity verification data.

3. The battery control method according to claim 1, which employs a dynamic adjustment strategy to address changes in the internal resistance of an aging battery, is characterized in that... The S2 includes: S21. For battery application scenarios corresponding to the dynamic resistance regulation architecture, and in combination with the minimum operating voltage requirements of the vehicle system, set upper and lower limit voltage monitoring ranges and generate voltage reference range data. S22. Compare the real-time collected battery output voltage with the voltage reference interval data point by point, calculate the difference between the voltage deviation from the interval boundary, and generate voltage deviation quantification data. S23. Set a deviation threshold. When the voltage deviation quantization data exceeds the threshold, the voltage is determined to be in an abnormal state, and a voltage abnormality trigger signal is generated. The voltage abnormality trigger signal is transmitted to the MCU control circuit, which starts the preset resistance adjustment program and generates circuit drive instruction data. S24. The MCU control circuit outputs a pulse width modulation signal based on the circuit drive instruction data to change the conduction degree of the semiconductor resistor and generate dynamic resistance adjustment data. S25. Real-time acquisition of the actual resistance value of the semiconductor resistor after adjustment, comparison and calibration with the dynamic resistance adjustment data, and generation of resistance calibration feedback data.

4. The battery control method according to claim 3, which employs a dynamic adjustment strategy to address changes in the internal resistance of an aging battery, is characterized in that... S24 includes: The MCU control circuit parses the circuit drive command data, extracts the target parameters for resistance adjustment, and generates PWM signal configuration data. Based on the PWM signal configuration data, the MCU control circuit generates the corresponding pulse width modulation signal; the pulse width modulation signal is then transmitted to the control port of the semiconductor resistor through the control line. After receiving a pulse width modulation signal, the semiconductor resistor adjusts the duty cycle of its internal conduction circuit to change the degree of conduction; the resistance parameters corresponding to the change in the degree of conduction of the semiconductor resistor are recorded to generate dynamic resistance adjustment data.

5. The battery control method according to claim 1, which employs a dynamic adjustment strategy to address changes in the internal resistance of an aging battery, is characterized in that... The S3 includes: S31. Based on the resistance calibration feedback data, the battery output voltage is collected at 500μs intervals, the voltage change before and after adjustment is recorded, and the adjusted voltage feedback data is generated. S32. Calculate the voltage difference between adjacent sampling points in the adjusted voltage feedback data, divide it by the time interval to obtain the voltage recovery rate, and generate voltage recovery slope data. S33. Based on the voltage recovery slope data and the power requirements of each power module of the vehicle system, analyze the ability of the power supply to continuously meet the demand and generate power supply continuity data; use the analytic hierarchy process to weight the power supply indicators and generate system stability quantitative indicators. S34. Based on the numerical range of the system stability quantitative indicators, divide the risk into three levels: low, medium, and high, and generate risk classification judgment data. S35. Based on the risk classification data, analyze the possibility of the vehicle system malfunctioning due to unstable power supply, supplement and improve the risk description, and generate a detailed risk assessment report.

6. The battery control method according to claim 5, which employs a dynamic adjustment strategy to address changes in the internal resistance of an aging battery, is characterized in that... S33 includes: Collect power demand parameters of each power module of the vehicle infotainment system, classify and organize them according to module function type, and generate module power demand dataset; By combining the module power demand dataset with the voltage rise slope data, the continuous power supply capability under different power loads is analyzed, and power supply continuity data is generated. Collect the voltage fluctuation amplitude and current stability parameters of the battery output, integrate the power supply continuity data, and generate a comprehensive power supply index set; The Analytic Hierarchy Process (AHP) is applied to perform weighted calculations on various parameters in the comprehensive power supply index set to generate quantitative indicators for system stability.

7. The battery control method according to claim 1, which employs a dynamic adjustment strategy to address changes in the internal resistance of an aging battery, is characterized in that... The S4 includes: S41. Based on risk classification data and detailed risk assessment reports, optimize the parameters of remote monitoring and generate monitoring parameter correction data; based on the monitoring parameter correction data, adjust the semiconductor resistor adjustment algorithm, optimize the step size and response time of resistance value change, and generate resistance value adjustment algorithm data. S42. The resistance adjustment algorithm data is encapsulated into control commands through the wireless communication module and sent to the dynamic resistance adjustment architecture to generate a remote adjustment trigger signal. S43. After receiving the remote adjustment trigger signal, the dynamic resistance adjustment architecture drives the semiconductor resistor to perform resistance adjustment operation and generates actual adjustment operation data. S44. Collect the battery voltage, current and semiconductor resistance value corresponding to the actual adjustment operation data, comprehensively evaluate the adjustment effect and generate adjustment effect feedback data. S45. Compare the adjustment effect feedback data with the expected adjustment target to determine whether the stable power supply requirements have been met, and generate the adjustment effect verification result.

8. The battery control method according to claim 7, which employs a dynamic adjustment strategy to address changes in the internal resistance of an aging battery, is characterized in that... S41 includes: By combining risk classification data and detailed risk assessment reports, key points for parameter optimization are extracted, and parameter optimization guidance data is generated. Based on parameter optimization guidance data, adjust the voltage monitoring threshold of remote monitoring to generate threshold correction data; integrate the threshold correction data to optimize the resistance adjustment sensitivity parameter of remote monitoring to generate monitoring parameter correction data. Extract the adjustment requirements from the monitoring parameter correction data, sort out the adjustment direction of the semiconductor resistor adjustment algorithm, and generate algorithm adjustment guidance data; Based on the algorithm adjustment guidance data, the adjustment algorithm of the semiconductor resistor is adjusted, the step size and response time of the resistance value change are optimized, and resistance value adjustment algorithm data is generated.

9. The battery control method according to claim 1, which employs a dynamic adjustment strategy to address changes in the internal resistance of an aging battery, is characterized in that... The S5 includes: S51. Based on the verification results of the adjustment effect, integrate relevant parameters to generate a comprehensive battery operation dataset; use the entropy weight method to determine the weight of each parameter, calculate the stability score of the battery system through a multi-index comprehensive evaluation model, and generate the battery stability index. S52. Combining historical data of battery stability index and industry safety standards, set different levels of warning threshold ranges and generate warning trigger condition data. S53. Real-time monitoring of changes in the battery stability index; when the value reaches the threshold in the warning trigger condition data, a risk warning trigger signal is generated. S54. Based on the level of the risk warning trigger signal, output the corresponding audio-visual prompts and text warning content to generate battery safety warning data; synchronize the battery safety warning data to the vehicle control system and user terminal to perform multi-terminal warning synchronization and generate warning synchronization feedback data.

10. A system for implementing a battery control method for a dynamic adjustment strategy for changes in the internal resistance of an aging battery as described in claim 1, characterized in that, The system includes: Network construction module: Real-time voltage monitoring of the output terminal of the battery to generate battery output voltage data; Determine the battery internal resistance change state based on the battery output voltage data and generate internal resistance change trend data; Deploy adjustable semiconductor resistors at the battery output terminal based on the internal resistance change trend data to construct a dynamic resistance adjustment network. Resistor adjustment module: Performs real-time voltage threshold analysis based on dynamic resistor adjustment network. When the battery output voltage is detected to be lower than the preset threshold, it generates voltage abnormality signal data. Based on the voltage abnormality signal data, the MCU control circuit dynamically reduces and adjusts the resistance value of the semiconductor resistor, generating the adjusted resistance value data. Voltage recovery module: Evaluates the voltage recovery effect based on the adjusted resistance value data and generates voltage recovery trend data; performs power supply stability analysis on the vehicle system based on the voltage recovery trend data and generates power supply stability index data; assesses the risk of misjudgment in the vehicle system based on the power supply stability index data and generates misjudgment risk level data. The adjustment execution module: remotely monitors and optimizes the parameters of the dynamic resistance adjustment network based on the risk level data of misjudgment, and generates optimized adjustment parameter data; remotely and dynamically adjusts the resistance value of the semiconductor resistor based on the optimized adjustment parameter data, and generates remote adjustment execution data; verifies the adjustment effect of the remote adjustment execution data, and generates adjustment verification result data. Risk warning module: Calculates the battery system stability index based on the adjustment and verification results data, and generates the battery system stability index; performs risk warning processing based on the battery system stability index, and generates battery disaster warning data.

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