Battery charging and discharging power control precision optimization method for BMS (Battery Management System)

Abstract

The invention discloses a battery charging and discharging power control precision optimization method for a BMS system, and relates to the technical field of charging and discharging parameter control, and the method comprises the steps: constructing a second-order resistance-capacitance network equivalent circuit model, and fitting model parameters through a pulse charging and discharging experiment; identifying an error source and quantifying a charge-discharge power control error of the current BMS; a Hall effect sensor and a voltage sampling chip are selected for experimental data acquisition; establishing a compensation model of the temperature to the charging and discharging efficiency; introducing an adaptive control algorithm; a multi-stage constant current-constant voltage charging algorithm is adopted; integrating the capacity attenuation model and the internal resistance growth model; a real-time operating system and a time-triggered task scheduling mechanism are adopted to control the cycle of a charging and discharging power control instruction, and dynamic resource allocation and delay control are carried out. By adopting technical means such as a second-order resistance-capacitance network equivalent circuit model, the precision and the stability of battery charging and discharging power control are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of charge and discharge parameter control, and in particular to a method for optimizing battery charge and discharge power control accuracy for a BMS system. Background Art

[0002] With the vigorous development of the new energy industry, batteries, as the core components of these systems, have a performance that directly determines the operating efficiency, endurance and safety of the entire system. Especially in the field of electric vehicles, the accuracy of battery charge and discharge power control not only affects the vehicle's mileage and acceleration performance, but is also directly related to the battery's service life and safety. Therefore, it is particularly important to develop efficient and accurate battery charge and discharge power control methods.

[0003] From the perspective of electric vehicles, as consumers' demands for driving range and charging speed continue to increase, the precise control of charging and discharging power by the battery management system has become one of the key technologies. Traditional charging and discharging control methods are often based on simple models or empirical formulas, which are difficult to accurately reflect the dynamic characteristics of batteries under complex working conditions, resulting in low charging and discharging efficiency and accelerated battery loss. Summary of the invention

[0004] In order to solve the above technical problems, a method for optimizing the battery charging and discharging power control accuracy for a BMS system is provided. This technical solution solves the problems raised in the above background technology.

[0005] In order to achieve the above purpose, the technical solution adopted by the present invention is: A method for optimizing battery charging and discharging power control accuracy for a BMS system, comprising: Construct a second-order RC network equivalent circuit model and fit the model parameters through pulse charge and discharge experiments; Through experimental testing and data analysis, identify the error sources and quantify the current BMS charge and discharge power control errors, including the hardware level, software level and system level; Hall effect sensors and voltage sampling chips were selected for experimental data collection, the sensors were calibrated regularly using standard signal sources, and temperature compensation circuits were introduced to reduce temperature drift errors; Deploy at least one temperature sensor at the tab and center area of ​​the battery module, establish a compensation model for temperature on charging and discharging efficiency, and adjust the power based on temperature feedback; Introducing an adaptive control algorithm to dynamically adjust the proportional, integral and differential coefficients according to the error; Adopt multi-stage constant current-constant voltage charging algorithm, and optimize charging and discharging power parameters in combination with battery characteristics; Integrate capacity attenuation model and internal resistance growth model, dynamically adjust power limit, and design redundant control loop to trigger safety protection for abnormal power fluctuations; At the system software architecture level, a real-time operating system and a time-triggered task scheduling mechanism are used to control the cycle of charging and discharging power control instructions, and to perform dynamic resource allocation and delay control.

[0006] Preferably, the construction of a second-order RC network equivalent circuit model and fitting model parameters through a pulse charge and discharge experiment specifically includes: The second-order RC network equivalent circuit model uses a RC loop to simulate the electrochemical and concentration polarization phenomena inside the battery. The potential difference across the voltage source is the open circuit voltage of the battery, and the output is a nonlinear function of the battery state; The ohmic internal resistance is generated by the internal structure of the battery. The resistance generated by the battery electrode material, diaphragm and electrolyte, as well as the contact resistance generated by the mechanical connection of each part are obtained, and the resistance value of the ohmic internal resistance is obtained by adding them together; The resistance and capacitance links are divided into polarization resistance and polarization capacitance to simulate the process of slow voltage change of the battery after discharge due to concentration polarization and diffusion effect; Connect the voltage source, ohmic internal resistance and two resistance and capacitance links in series according to the circuit principle to form a second-order resistance and capacitance network equivalent circuit model; Use a charge and discharge tester to realize the pulse charge and discharge process, and determine the current size, pulse width and interval time parameters of the pulse charge and discharge; Connect the battery to the charge and discharge tester to perform pulse charge and discharge experiments; Use nonlinear least squares optimization function to fit circuit parameters and adjust the parameters in the second-order RC network equivalent circuit model by minimizing the residual error. Use the fitted model to predict the experimental data and compare the predicted results with the actual measured data; It is determined whether the error between the predicted result and the actual measured data is lower than the preset error threshold. If so, the output second-order RC network equivalent circuit model has high reliability. If not, the model structure is readjusted and the model parameters are refitted.

[0007] Preferably, identifying the error source and quantifying the current BMS charge and discharge power control error through experimental testing and data analysis specifically includes: Process the collected data, calculate the difference between the actual charging and discharging power and the BMS control target power, and output it as an error; Analyze the error data and preliminarily determine whether the error comes from the hardware level, software level or system level; Output errors caused by insufficient sensor accuracy, circuit component aging, and unstable performance as hardware-level errors; Output errors caused by inaccurate control algorithms and errors in data acquisition and processing codes as software-level errors; Output the errors caused by sudden changes in system load and ambient temperature fluctuations as system-level errors; For the identified error sources, further quantify their specific impact on the charge and discharge power control error; For hardware-level errors, quantify the errors by calibrating sensors and replacing components; For software-level errors, simulation and code review methods are used to quantify the errors; For system-level errors, statistical analysis is used to quantify system dynamic errors and environmental interference errors based on system dynamic characteristics and environmental factors. The errors from different levels are summarized to comprehensively evaluate the total error of BMS charging and discharging power control.

[0008] Preferably, the selection of Hall effect sensors and voltage sampling chips for experimental data collection, the use of standard signal sources to regularly calibrate the sensors, and the introduction of temperature compensation circuits to reduce temperature drift errors specifically include: A Hall effect sensor is used to measure the change of magnetic field and convert it into a voltage signal, and a voltage sampling chip is used to collect the voltage signal output by the Hall effect sensor; Connect the Hall effect sensor to the voltage sampling chip to build a data acquisition circuit, integrate the temperature compensation circuit into the data acquisition circuit, and correct the deviation caused by temperature in real time; Connecting the standard signal source to the Hall effect sensor, and setting parameters of the Hall effect sensor in the supporting equipment, wherein the parameters of the Hall effect sensor include a range and a unit; Record the output value of the sensor under the action of the standard signal source and make a difference with the theoretical value. The output result is the sensor error. Based on the error situation, adjust the zero point and amplification parameters of the sensor until the sensor error is within the preset allowable range; Start the experimental equipment to make the Hall effect sensor start working, monitor the voltage signal output by the Hall effect sensor in real time through the voltage sampling chip, and record the data; storing the collected data in a computer-readable storage medium; During the experiment, the operating temperature of the circuit was monitored in real time; The temperature compensation circuit automatically adjusts the circuit parameters according to the real-time monitored working temperature to correct the deviation caused by temperature; Check the working effect of the temperature compensation circuit to ensure that the temperature drift error is within the preset acceptable range.

[0009] Preferably, deploying at least one temperature sensor in the tab and center area of ​​the battery module, establishing a compensation model for temperature to charge and discharge efficiency, and adjusting power according to temperature feedback specifically includes: The tab is a key part of the current input and output in the battery module. Its temperature change reflects the thermal effect of the battery module during the charging and discharging process. The temperature sensor is deployed here to monitor the temperature change of the battery module during charging and discharging in real time. The central area is the part with the highest temperature inside the battery module. Temperature sensors are deployed here to monitor the temperature inside the battery module in real time and provide data support for the establishment of the temperature compensation model. The temperature sensors deployed on the tabs and the center area collect the temperature data of the battery module during the charging and discharging process, as well as the corresponding charging and discharging efficiency data; Analyze the collected temperature data and charge-discharge efficiency data, and establish a linear model of the relationship between temperature and charge-discharge efficiency; Based on the data analysis results, a compensation model for the effect of temperature on charging and discharging efficiency is established, and based on the real-time temperature data, changes in charging and discharging efficiency caused by temperature changes are predicted and compensated; Feedback the monitored temperature data to the BMS system; Based on the temperature compensation model, the optimal charge and discharge power at the current temperature is calculated, and the charge and discharge power is adjusted in real time through the BMS system to ensure that the battery module works in the best condition; In the process of regulating power, the BMS system monitors the voltage and current status of the battery module in real time, and takes protective measures when abnormal conditions are found, including overcharging, over-discharging and overheating.

[0010] Preferably, the multi-stage constant current-constant voltage charging algorithm is adopted, and the charging and discharging power parameter optimization is performed in combination with the battery characteristics, specifically including: The current value of the initial constant current charging stage is set according to the maximum charging current and capacity of the battery; Monitor the battery voltage, and when the voltage reaches a preset ratio of the battery's rated voltage, enter the constant voltage charging stage; Set the charging voltage to the battery's charge termination voltage; During the constant voltage charging stage, the charging current gradually decreases until it reaches the preset cut-off current value, and the charging process ends; At each stage, the charging current is adjusted based on the real-time state of charge of the battery to optimize charging efficiency; According to the real-time voltage, current and temperature information of the battery, the charging termination condition is dynamically adjusted. When the battery voltage reaches the charging termination voltage and the charging current drops below the cut-off current value, the charging process ends; The charge termination conditions are adjusted based on individual battery differences and aging.

[0011] Preferably, at the system software architecture level, a real-time operating system and a time-triggered task scheduling mechanism are used to control the cycle of the charge and discharge power control instructions, and to perform dynamic resource allocation and delay control, specifically including: The functions of the battery management system are divided into three tasks: charge and discharge control, status monitoring, and fault diagnosis, and each task is given a priority; According to the requirements of the battery management system, a fixed execution cycle is set for each task; In a real-time operating system, the processor time is divided into at least one time slice, and each time slice is allocated to each task; The task runs in its own time slice. When the time slice ends, the task is suspended and waits for the next time slice to arrive. Adopting the priority preemptive scheduling strategy, when the priority of the new task is higher than the priority of the current task, the new task preempts the processor time of the current task; Generate charging and discharging power control instructions based on battery status information and preset charging and discharging strategies; Dynamically adjust the cycle of charge and discharge power control instructions according to battery status changes and external temperature or load demand environmental factors; By adjusting the time slice size and task cycle, the time for task switching and task waiting can be reduced.

[0012] Compared with the prior art, the present invention has the following beneficial effects: This method can more accurately simulate the dynamic behavior of the battery during the charging and discharging process, which not only improves the BMS system's ability to predict the battery status, but also enhances the accuracy and adaptability of the control strategy, making the charging and discharging process more efficient and stable, and can ensure that the battery remains in the best state during the charging and discharging process, helping to reduce the internal stress of the battery, extend the battery life, and improve charging efficiency and safety. The real-time operating system and time-triggered task scheduling mechanism can accurately control the cycle of the charging and discharging power control instructions, and perform dynamic resource allocation and delay control, which helps to ensure the real-time and stability of the BMS system under complex working conditions and improve the response speed and operating efficiency of the entire system. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 A diagram of a method for optimizing battery charging and discharging power control accuracy for a BMS system according to the present invention; Figure 2 A flow chart of a method for constructing a second-order RC network equivalent circuit model according to the present invention; Figure 3A flow chart of a method for identifying error sources and quantifying the current BMS charge and discharge power control error of the present invention; Figure 4 A flow chart of a method for collecting experimental data by selecting a Hall effect sensor and a voltage sampling chip according to the present invention; Figure 5 A flow chart of a method for establishing a compensation model for temperature to charge and discharge efficiency according to the present invention; Figure 6 A flow chart of a method for charging using a multi-stage constant current-constant voltage algorithm of the present invention; Figure 7 This is a flow chart of the method for dynamic resource allocation and delay control of the present invention. DETAILED DESCRIPTION

[0014] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art may think of other obvious variations.

[0015] Reference Figure 1 As shown, a method for optimizing battery charging and discharging power control accuracy for a BMS system includes: Construct a second-order RC network equivalent circuit model and fit the model parameters through pulse charge and discharge experiments; Through experimental testing and data analysis, identify the error sources and quantify the current BMS charge and discharge power control errors, including the hardware level, software level and system level; Hall effect sensors and voltage sampling chips were selected for experimental data collection, the sensors were calibrated regularly using standard signal sources, and temperature compensation circuits were introduced to reduce temperature drift errors; Deploy at least one temperature sensor at the tab and center area of ​​the battery module, establish a compensation model for temperature on charging and discharging efficiency, and adjust the power based on temperature feedback; Introducing an adaptive control algorithm to dynamically adjust the proportional, integral and differential coefficients according to the error; Adopt multi-stage constant current-constant voltage charging algorithm, and optimize charging and discharging power parameters in combination with battery characteristics; Integrate capacity attenuation model and internal resistance growth model, dynamically adjust power limit, and design redundant control loop to trigger safety protection for abnormal power fluctuations; At the system software architecture level, a real-time operating system and a time-triggered task scheduling mechanism are used to control the cycle of charging and discharging power control instructions, and to perform dynamic resource allocation and delay control.

[0016] Reference Figure 2 As shown in the figure, a second-order RC network equivalent circuit model is constructed, and the model parameters are fitted through pulse charge and discharge experiments, including: The second-order RC network equivalent circuit model uses a RC loop to simulate the electrochemical and concentration polarization phenomena inside the battery. The potential difference across the voltage source is the open circuit voltage of the battery, and the output is a nonlinear function of the battery state; The ohmic internal resistance is generated by the internal structure of the battery. The resistance generated by the battery electrode material, diaphragm and electrolyte, as well as the contact resistance generated by the mechanical connection of each part are obtained, and the resistance value of the ohmic internal resistance is obtained by adding them together; The resistance and capacitance links are divided into polarization resistance and polarization capacitance to simulate the process of slow voltage change of the battery after discharge due to concentration polarization and diffusion effect; Connect the voltage source, ohmic internal resistance and two resistance and capacitance links in series according to the circuit principle to form a second-order resistance and capacitance network equivalent circuit model; Use a charge and discharge tester to realize the pulse charge and discharge process, and determine the current size, pulse width and interval time parameters of the pulse charge and discharge; Connect the battery to the charge and discharge tester to perform pulse charge and discharge experiments; Use nonlinear least squares optimization function to fit circuit parameters and adjust the parameters in the second-order RC network equivalent circuit model by minimizing the residual error. Use the fitted model to predict the experimental data and compare the predicted results with the actual measured data; It is determined whether the error between the predicted result and the actual measured data is lower than the preset error threshold. If so, the output second-order RC network equivalent circuit model has high reliability. If not, the model structure is readjusted and the model parameters are refitted.

[0017] Use a nonlinear least squares optimization function, such as the lsqnonlin function in MATLAB, to fit the circuit parameters. By continuously adjusting the parameter values ​​in the second-order RC network equivalent circuit model, the residual between the voltage response predicted by the model and the actual measured voltage response is minimized. According to the specific objectives of the research, the required model accuracy is determined, and an initial preset error threshold is set based on factors such as the noise level of the experimental data and the accuracy of the measuring instrument.

[0018] Reference Figure 3 As shown in the figure, through experimental testing and data analysis, the error sources are identified and the current BMS charge and discharge power control errors are quantified, including: Process the collected data, calculate the difference between the actual charging and discharging power and the BMS control target power, and output it as an error; Analyze the error data and preliminarily determine whether the error comes from the hardware level, software level or system level; Output errors caused by insufficient sensor accuracy, circuit component aging, and unstable performance as hardware-level errors; Output errors caused by inaccurate control algorithms and errors in data acquisition and processing codes as software-level errors; Output the errors caused by sudden changes in system load and ambient temperature fluctuations as system-level errors; For the identified error sources, further quantify their specific impact on the charge and discharge power control error; For hardware-level errors, quantify the errors by calibrating sensors and replacing components; For software-level errors, simulation and code review methods are used to quantify the errors; For system-level errors, statistical analysis is used to quantify system dynamic errors and environmental interference errors based on system dynamic characteristics and environmental factors. The errors from different levels are summarized to comprehensively evaluate the total error of BMS charging and discharging power control.

[0019] The errors from the hardware, software and system levels are summarized to obtain the total error of the BMS's charge and discharge power control. The size, distribution and impact of the total error are comprehensively evaluated to determine its potential impact on the BMS's performance and reliability. Based on the evaluation results, targeted improvement measures and optimization plans are formulated to improve the BMS's charge and discharge power control accuracy and stability.

[0020] Reference Figure 4 As shown in the figure, Hall effect sensors and voltage sampling chips are selected for experimental data acquisition. Standard signal sources are used to calibrate the sensors regularly, and temperature compensation circuits are introduced to reduce temperature drift errors. Specifically, the following steps are included: A Hall effect sensor is used to measure the change of magnetic field and convert it into a voltage signal, and a voltage sampling chip is used to collect the voltage signal output by the Hall effect sensor; Connect the Hall effect sensor to the voltage sampling chip to build a data acquisition circuit, integrate the temperature compensation circuit into the data acquisition circuit, and correct the deviation caused by temperature in real time; Connecting the standard signal source to the Hall effect sensor, and setting parameters of the Hall effect sensor in the supporting equipment, wherein the parameters of the Hall effect sensor include a range and a unit; Record the output value of the sensor under the action of the standard signal source and make a difference with the theoretical value. The output result is the sensor error. Based on the error situation, adjust the zero point and amplification parameters of the sensor until the sensor error is within the preset allowable range; Start the experimental equipment to make the Hall effect sensor start working, monitor the voltage signal output by the Hall effect sensor in real time through the voltage sampling chip, and record the data; storing the collected data in a computer-readable storage medium; During the experiment, the operating temperature of the circuit was monitored in real time; The temperature compensation circuit automatically adjusts the circuit parameters according to the real-time monitored working temperature to correct the deviation caused by temperature; Check the working effect of the temperature compensation circuit to ensure that the temperature drift error is within the preset acceptable range.

[0021] Different types of sensors have different accuracy and stability characteristics. High-precision sensors themselves have higher measurement accuracy, so their allowable error range can be set smaller. At the same time, the performance parameters of the sensor, such as linearity, repeatability, hysteresis, etc., will also affect the setting of the allowable error range. The temperature variation range of the sensor's working environment is a key factor affecting the acceptable range of temperature drift error. If the temperature variation range is large, the temperature drift error may also increase accordingly. Therefore, a relatively large acceptable range needs to be set. Conversely, if the temperature variation range is small, a smaller acceptable range can be set.

[0022] Reference Figure 5 As shown, at least one temperature sensor is deployed in the tab and center area of ​​the battery module, a compensation model for temperature on charging and discharging efficiency is established, and the power is adjusted according to temperature feedback, specifically including: The tab is a key part of the current input and output in the battery module. Its temperature change reflects the thermal effect of the battery module during the charging and discharging process. The temperature sensor is deployed here to monitor the temperature change of the battery module during charging and discharging in real time. The central area is the part with the highest temperature inside the battery module. Temperature sensors are deployed here to monitor the temperature inside the battery module in real time and provide data support for the establishment of the temperature compensation model. The temperature sensors deployed on the tabs and the center area collect the temperature data of the battery module during the charging and discharging process, as well as the corresponding charging and discharging efficiency data; Analyze the collected temperature data and charge-discharge efficiency data, and establish a linear model of the relationship between temperature and charge-discharge efficiency; Based on the data analysis results, a compensation model for the effect of temperature on charging and discharging efficiency is established, and based on the real-time temperature data, changes in charging and discharging efficiency caused by temperature changes are predicted and compensated; Feedback the monitored temperature data to the BMS system; Based on the temperature compensation model, the optimal charge and discharge power at the current temperature is calculated, and the charge and discharge power is adjusted in real time through the BMS system to ensure that the battery module works in the best condition; In the process of regulating power, the BMS system monitors the voltage and current status of the battery module in real time, and takes protective measures when abnormal conditions are found, including overcharging, over-discharging and overheating.

[0023] The collected temperature data and charge and discharge efficiency data are analyzed, and a linear model of the relationship between temperature and charge and discharge efficiency is established using statistical methods and machine learning algorithms. Based on the results of the linear model, a compensation model for temperature on charge and discharge efficiency is further established. This model can predict and compensate for changes in charge and discharge efficiency caused by temperature changes based on real-time temperature data.

[0024] Reference Figure 6 As shown, a multi-stage constant current-constant voltage charging algorithm is used, and the charging and discharging power parameters are optimized in combination with battery characteristics, including: The current value of the initial constant current charging stage is set according to the maximum charging current and capacity of the battery; Monitor the battery voltage, and when the voltage reaches a preset ratio of the battery's rated voltage, enter the constant voltage charging stage; Set the charging voltage to the battery's charge termination voltage; During the constant voltage charging stage, the charging current gradually decreases until it reaches the preset cut-off current value, and the charging process ends; At each stage, the charging current is adjusted based on the real-time state of charge of the battery to optimize charging efficiency; According to the real-time voltage, current and temperature information of the battery, the charging termination condition is dynamically adjusted. When the battery voltage reaches the charging termination voltage and the charging current drops below the cut-off current value, the charging process ends; The charge termination conditions are adjusted based on individual battery differences and aging.

[0025] When it is monitored that the battery voltage reaches a preset ratio of its rated voltage, the system will transition from the constant current charging stage to the constant voltage charging stage. This preset ratio is usually set based on the battery's safe charging characteristics and efficiency considerations. The cut-off current value is usually set to a smaller value to ensure that the battery is fully charged and will not be damaged by overcharging. Considering the individual differences between batteries and the aging phenomenon that occurs with the increase in usage time, the charging termination conditions should be personalized. This can be achieved through a learning algorithm, which gradually optimizes the charging strategy based on the battery's charging history, performance changes, and the battery aging model provided by the manufacturer. For severely aged batteries, it may be necessary to reduce the maximum charging current, increase the charging termination voltage, or adjust the cut-off current value and other parameters to ensure safe and effective charging.

[0026] Reference Figure 7As shown in the figure, at the system software architecture level, a real-time operating system and a time-triggered task scheduling mechanism are used to control the cycle of charge and discharge power control instructions, and to perform dynamic resource allocation and delay control, including: The functions of the battery management system are divided into three tasks: charge and discharge control, status monitoring, and fault diagnosis, and each task is given a priority; According to the requirements of the battery management system, a fixed execution cycle is set for each task; In a real-time operating system, the processor time is divided into at least one time slice, and each time slice is allocated to each task; The task runs in its own time slice. When the time slice ends, the task is suspended and waits for the next time slice to arrive. Adopting the priority preemptive scheduling strategy, when the priority of the new task is higher than the priority of the current task, the new task preempts the processor time of the current task; Generate charging and discharging power control instructions based on battery status information and preset charging and discharging strategies; Dynamically adjust the cycle of charge and discharge power control instructions according to battery status changes and external temperature or load demand environmental factors; By adjusting the time slice size and task cycle, the time for task switching and task waiting can be reduced.

[0027] Different priorities are assigned to these three tasks according to their importance and urgency. The charge and discharge control task may have the highest priority to ensure the safe and efficient operation of the battery; the status monitoring task is second and is used to track the battery status in real time; the fault diagnosis task may be given a higher priority when an abnormality is detected.

[0028] Furthermore, the present solution also proposes a computer-readable storage medium on which a computer-readable program is stored. When the computer-readable program is called, the above-mentioned battery charging and discharging power control accuracy optimization method for the BMS system is executed.

[0029] It is understandable that the storage medium may be a magnetic medium, such as a floppy disk, a hard disk, or a magnetic tape; an optical medium, such as a DVD; or a semiconductor medium, such as a solid state drive (SSD).

[0030] In summary, the advantages of the present invention are: the method can more accurately simulate the dynamic behavior of the battery during the charging and discharging process, which not only improves the BMS system's ability to predict the battery status, but also enhances the accuracy and adaptability of the control strategy, making the charging and discharging process more efficient and stable, and can ensure that the battery remains in the best state during the charging and discharging process, which helps to reduce the internal stress of the battery, extend the battery life, and improve the charging efficiency and safety. The real-time operating system and time-triggered task scheduling mechanism can accurately control the cycle of the charging and discharging power control instructions, and perform dynamic resource allocation and delay control, which helps to ensure the real-time and stability of the BMS system under complex working conditions and improve the response speed and operation efficiency of the entire system.

[0031] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for optimizing battery charging and discharging power control accuracy for a BMS system, characterized in that: include: Construct a second-order RC network equivalent circuit model and fit the model parameters through pulse charge and discharge experiments; Through experimental testing and data analysis, identify the error sources and quantify the current BMS charge and discharge power control errors, including the hardware level, software level and system level; Hall effect sensors and voltage sampling chips were selected for experimental data collection, the sensors were calibrated regularly using standard signal sources, and temperature compensation circuits were introduced to reduce temperature drift errors; Deploy at least one temperature sensor at the tab and center area of ​​the battery module, establish a compensation model for temperature on charging and discharging efficiency, and adjust the power based on temperature feedback; Introducing an adaptive control algorithm to dynamically adjust the proportional, integral and differential coefficients according to the error; Adopt multi-stage constant current-constant voltage charging algorithm, and optimize charging and discharging power parameters in combination with battery characteristics; Integrate capacity attenuation model and internal resistance growth model, dynamically adjust power limit, and design redundant control loop to trigger safety protection for abnormal power fluctuations; At the system software architecture level, a real-time operating system and a time-triggered task scheduling mechanism are used to control the cycle of charging and discharging power control instructions, and to perform dynamic resource allocation and delay control.

2. The method for optimizing battery charging and discharging power control accuracy for a BMS system according to claim 1, characterized in that: The construction of a second-order RC network equivalent circuit model and fitting model parameters through a pulse charge and discharge experiment specifically include: The second-order RC network equivalent circuit model uses a RC loop to simulate the electrochemical and concentration polarization phenomena inside the battery. The potential difference across the voltage source is the open circuit voltage of the battery, and the output is a nonlinear function of the battery state; The ohmic internal resistance is generated by the internal structure of the battery. The resistance generated by the battery electrode material, diaphragm and electrolyte, as well as the contact resistance generated by the mechanical connection of each part are obtained, and the resistance value of the ohmic internal resistance is obtained by adding them together; The resistance and capacitance links are divided into polarization resistance and polarization capacitance to simulate the process of slow voltage change of the battery after discharge due to concentration polarization and diffusion effect; Connect the voltage source, ohmic internal resistance and two resistance and capacitance links in series according to the circuit principle to form a second-order resistance and capacitance network equivalent circuit model; Use a charge and discharge tester to realize the pulse charge and discharge process, and determine the current size, pulse width and interval time parameters of the pulse charge and discharge; Connect the battery to the charge and discharge tester to perform pulse charge and discharge experiments; Use nonlinear least squares optimization function to fit circuit parameters and adjust the parameters in the second-order RC network equivalent circuit model by minimizing the residual error. Use the fitted model to predict the experimental data and compare the predicted results with the actual measured data; It is determined whether the error between the predicted result and the actual measured data is lower than the preset error threshold. If so, the output second-order RC network equivalent circuit model has high reliability. If not, the model structure is readjusted and the model parameters are refitted.

3. The method for optimizing battery charging and discharging power control accuracy for a BMS system according to claim 2, characterized in that: The above-mentioned identification of the error source and quantification of the current BMS charge and discharge power control error through experimental testing and data analysis specifically includes: Process the collected data, calculate the difference between the actual charging and discharging power and the BMS control target power, and output it as an error; Analyze the error data and preliminarily determine whether the error comes from the hardware level, software level or system level; Output errors caused by insufficient sensor accuracy, circuit component aging, and unstable performance as hardware-level errors; Output errors caused by inaccurate control algorithms and errors in data acquisition and processing codes as software-level errors; Output the errors caused by sudden changes in system load and ambient temperature fluctuations as system-level errors; For the identified error sources, further quantify their specific impact on the charge and discharge power control error; For hardware-level errors, quantify the errors by calibrating sensors and replacing components; For software-level errors, simulation and code review methods are used to quantify the errors; For system-level errors, statistical analysis is used to quantify system dynamic errors and environmental interference errors based on system dynamic characteristics and environmental factors. The errors from different levels are summarized to comprehensively evaluate the total error of BMS charging and discharging power control.

4. The method for optimizing battery charging and discharging power control accuracy for a BMS system according to claim 3, characterized in that: The Hall effect sensor and the voltage sampling chip are selected for experimental data collection, the sensor is calibrated regularly using a standard signal source, and a temperature compensation circuit is introduced to reduce the temperature drift error, specifically including: A Hall effect sensor is used to measure the change of magnetic field and convert it into a voltage signal, and a voltage sampling chip is used to collect the voltage signal output by the Hall effect sensor; Connect the Hall effect sensor to the voltage sampling chip to build a data acquisition circuit, integrate the temperature compensation circuit into the data acquisition circuit, and correct the deviation caused by temperature in real time; Connecting the standard signal source to the Hall effect sensor, and setting parameters of the Hall effect sensor in the supporting equipment, wherein the parameters of the Hall effect sensor include a range and a unit; Record the output value of the sensor under the action of the standard signal source and make a difference with the theoretical value. The output result is the sensor error. Based on the error situation, adjust the zero point and amplification parameters of the sensor until the sensor error is within the preset allowable range; Start the experimental equipment to make the Hall effect sensor start working, monitor the voltage signal output by the Hall effect sensor in real time through the voltage sampling chip, and record the data; storing the collected data in a computer-readable storage medium; During the experiment, the operating temperature of the circuit was monitored in real time; The temperature compensation circuit automatically adjusts the circuit parameters according to the real-time monitored working temperature to correct the deviation caused by temperature; Check the working effect of the temperature compensation circuit to ensure that the temperature drift error is within the preset acceptable range.

5. The method for optimizing battery charging and discharging power control accuracy for a BMS system according to claim 4, characterized in that: The method of deploying at least one temperature sensor at the tab and the center area of ​​the battery module, establishing a compensation model for the effect of temperature on the charging and discharging efficiency, and adjusting the power according to the temperature feedback specifically includes: The tab is a key part of the current input and output in the battery module. Its temperature change reflects the thermal effect of the battery module during the charging and discharging process. The temperature sensor is deployed here to monitor the temperature change of the battery module during charging and discharging in real time. The central area is the part with the highest temperature inside the battery module. Temperature sensors are deployed here to monitor the temperature inside the battery module in real time and provide data support for the establishment of the temperature compensation model. The temperature sensors deployed on the tabs and the center area collect the temperature data of the battery module during the charging and discharging process, as well as the corresponding charging and discharging efficiency data; Analyze the collected temperature data and charge-discharge efficiency data, and establish a linear model of the relationship between temperature and charge-discharge efficiency; Based on the data analysis results, a compensation model for the effect of temperature on charging and discharging efficiency is established, and based on the real-time temperature data, changes in charging and discharging efficiency caused by temperature changes are predicted and compensated; Feedback the monitored temperature data to the BMS system; Based on the temperature compensation model, the optimal charge and discharge power at the current temperature is calculated, and the charge and discharge power is adjusted in real time through the BMS system to ensure that the battery module works in the best condition; In the process of regulating power, the BMS system monitors the voltage and current status of the battery module in real time, and takes protective measures when abnormal conditions are found, including overcharging, over-discharging and overheating.

6. The method for optimizing battery charging and discharging power control accuracy for a BMS system according to claim 5, characterized in that: The multi-stage constant current-constant voltage charging algorithm is adopted, and the charging and discharging power parameter optimization is performed in combination with the battery characteristics, specifically including: The current value of the initial constant current charging stage is set according to the maximum charging current and capacity of the battery; Monitor the battery voltage, and when the voltage reaches a preset ratio of the battery's rated voltage, enter the constant voltage charging stage; Set the charging voltage to the battery's charge termination voltage; During the constant voltage charging stage, the charging current gradually decreases until it reaches the preset cut-off current value, and the charging process ends; At each stage, the charging current is adjusted based on the real-time state of charge of the battery to optimize charging efficiency; According to the real-time voltage, current and temperature information of the battery, the charging termination condition is dynamically adjusted. When the battery voltage reaches the charging termination voltage and the charging current drops below the cut-off current value, the charging process ends; The charge termination conditions are adjusted based on individual battery differences and aging.

7. A method for optimizing battery charging and discharging power control accuracy for a BMS system according to claim 6, characterized in that: At the system software architecture level, a real-time operating system and a time-triggered task scheduling mechanism are used to control the cycle of charge and discharge power control instructions, and to perform dynamic resource allocation and delay control, specifically including: The functions of the battery management system are divided into three tasks: charge and discharge control, status monitoring, and fault diagnosis, and each task is given a priority; According to the requirements of the battery management system, a fixed execution cycle is set for each task; In a real-time operating system, the processor time is divided into at least one time slice, and each time slice is allocated to each task; The task runs in its own time slice. When the time slice ends, the task is suspended and waits for the next time slice to arrive. Adopting the priority preemptive scheduling strategy, when the priority of the new task is higher than the priority of the current task, the new task preempts the processor time of the current task; Generate charging and discharging power control instructions based on battery status information and preset charging and discharging strategies; Dynamically adjust the cycle of charge and discharge power control instructions according to battery status changes and external temperature or load demand environmental factors; By adjusting the time slice size and task cycle, the time for task switching and task waiting can be reduced.

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