A method for optimizing battery charge and discharge power control accuracy for BMS system

Abstract

The present invention discloses a method for optimizing the accuracy of battery charge and discharge power control for a BMS system. The method relates to the technical field of charge and discharge parameter control, including: constructing a second-order RC network equivalent circuit model and fitting the model parameters through pulse charge and discharge experiments; identifying error sources and quantifying the current BMS charge and discharge power control error; selecting a Hall effect sensor and a voltage sampling chip for experimental data acquisition; establishing a temperature compensation model for charge and discharge efficiency; introducing an adaptive control algorithm; employing a multi-stage constant current-constant voltage charging algorithm; integrating a capacity decay model and an internal resistance growth model; and employing a real-time operating system and a time-triggered task scheduling mechanism to control the cycle of charge and discharge power control instructions, and performing dynamic resource allocation and delay control. By employing a second-order RC network equivalent circuit model and other technical means, the accuracy and stability of battery charge and discharge power control are significantly 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 booming development of the new energy industry, batteries, as core components of these systems, have a performance that directly determines the overall system's operating efficiency, endurance, and safety. In the electric vehicle sector in particular, the accuracy of battery charge and discharge power control not only affects the vehicle's range and acceleration performance, but also directly impacts the battery's lifespan and safety. Therefore, developing efficient and accurate battery charge and discharge power control methods is crucial.

[0003] From the perspective of electric vehicles, as consumer demand for increased range and charging speed continues to rise, precise control of charge and discharge power by battery management systems has become a key technology. Traditional charge and discharge control methods, often based on simple models or empirical formulas, struggle to accurately reflect the dynamic characteristics of batteries under complex operating conditions, leading to problems such as low charge and discharge 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 objects, the technical solution adopted by the present invention is:

[0006] A method for optimizing battery charge and discharge power control accuracy for a BMS system, comprising:

[0007] Construct a second-order RC network equivalent circuit model and fit the model parameters through pulse charge and discharge experiments;

[0008] Through experimental testing and data analysis, we identify and quantify the error sources of the current BMS charge and discharge power control error, including the hardware, software, and system levels.

[0009] Hall effect sensors and voltage sampling chips were selected for experimental data acquisition. The sensors were calibrated regularly using a standard signal source, and a temperature compensation circuit was introduced to reduce temperature drift errors.

[0010] Deploy at least one temperature sensor at the tab and center of the battery module, establish a temperature compensation model for charge and discharge efficiency, and adjust power based on temperature feedback;

[0011] Adaptive control algorithm is introduced to dynamically adjust the proportional, integral and differential coefficients according to the error;

[0012] Adopt a multi-stage constant current-constant voltage charging algorithm and optimize the charging and discharging power parameters based on battery characteristics;

[0013] Integrate capacity attenuation model and internal resistance growth model to dynamically adjust power limit and design redundant control loops to trigger safety protection for abnormal power fluctuations;

[0014] At the system software architecture level, a real-time operating system and time-triggered task scheduling mechanism are used to control the cycle of charging and discharging power control instructions, and perform dynamic resource allocation and delay control.

[0015] Preferably, the constructing of a second-order RC network equivalent circuit model and fitting model parameters through a pulse charge and discharge experiment specifically includes:

[0016] The second-order RC network equivalent circuit model uses a RC loop to simulate the electrochemical and concentration polarization phenomena inside the battery;

[0017] 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;

[0018] 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 by adding them together to obtain the ohmic internal resistance value.

[0019] The resistance and capacitance links are divided into polarization resistance and polarization capacitance to simulate the process of battery voltage slowly changing after discharge due to concentration polarization and diffusion effect.

[0020] 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;

[0021] 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;

[0022] Connect the battery to a charge and discharge tester and perform a pulse charge and discharge experiment;

[0023] 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;

[0024] Use the fitted model to predict the experimental data and compare the differences between the predicted results and the actual measured data;

[0025] Determine 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, readjust the model structure and refit the model parameters.

[0026] Preferably, identifying the error source and quantifying the current BMS charge and discharge power control error through experimental testing and data analysis specifically includes:

[0027] The collected data is processed to calculate the difference between the actual charge and discharge power and the BMS control target power, and output as an error;

[0028] Analyze the error data and preliminarily determine whether the error comes from the hardware level, software level, or system level;

[0029] Output errors caused by insufficient sensor accuracy, circuit component aging, and unstable performance as hardware-level errors;

[0030] Output errors caused by inaccurate control algorithms and errors in data acquisition and processing codes as software-level errors;

[0031] Output the errors caused by sudden changes in system load and ambient temperature fluctuations as system-level errors;

[0032] For the identified error sources, further quantify their specific impact on the charge and discharge power control error;

[0033] For hardware-level errors, quantify the errors by calibrating sensors and replacing components;

[0034] For software-level errors, simulation and code review methods are used to quantify the errors;

[0035] 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.

[0036] The errors from different levels are summarized to comprehensively evaluate the total error of BMS charging and discharging power control.

[0037] Preferably, the selection of a Hall effect sensor and a voltage sampling chip for experimental data acquisition, the use of a standard signal source to regularly calibrate the sensor, and the introduction of a temperature compensation circuit to reduce temperature drift errors specifically include:

[0038] Use a Hall effect sensor to measure the magnetic field change and convert it into a voltage signal, and use a voltage sampling chip to collect the voltage signal output by the Hall effect sensor;

[0039] 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 to correct the deviation caused by temperature in real time.

[0040] Connecting a standard signal source to a Hall effect sensor and setting parameters of the Hall effect sensor in a supporting device, wherein the parameters of the Hall effect sensor include a range and a unit;

[0041] Record the output value of the sensor under the action of the standard signal source and subtract it from the theoretical value. The output result is the sensor error.

[0042] 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;

[0043] 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;

[0044] storing the collected data in a computer-readable storage medium;

[0045] During the experiment, the operating temperature of the circuit was monitored in real time;

[0046] The temperature compensation circuit automatically adjusts the circuit parameters according to the real-time monitored operating temperature to correct the deviation caused by temperature;

[0047] Check the working effect of the temperature compensation circuit to ensure that the temperature drift error is within the preset acceptable range.

[0048] Preferably, the steps 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 charge and discharge efficiency, and adjusting the power according to temperature feedback specifically include:

[0049] The tab is a key part of the battery module for current input and output. Its temperature change reflects the thermal effect of the battery module during the charging and discharging process. Temperature sensors are deployed here to monitor the temperature changes of the battery module in real time during charging and discharging.

[0050] The center area is where the temperature inside the battery module is the highest. 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.

[0051] Temperature sensors deployed on the tabs and center areas collect temperature data of the battery module during the charge and discharge process, as well as the corresponding charge and discharge efficiency data.

[0052] Analyze the collected temperature data and charge-discharge efficiency data and establish a linear model of the relationship between temperature and charge-discharge efficiency;

[0053] Based on the data analysis results, a compensation model for the effect of temperature on charge and discharge efficiency is established. Based on the real-time temperature data, changes in charge and discharge efficiency caused by temperature changes are predicted and compensated.

[0054] Feedback the monitored temperature data to the BMS system;

[0055] 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 operates in the best condition;

[0056] During the power regulation process, the BMS system monitors the voltage and current status of the battery module in real time and takes protective measures when abnormal conditions are detected, including overcharging, over-discharging and overheating.

[0057] 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 battery characteristics, specifically including:

[0058] The current value of the initial constant current charging stage is set according to the maximum charging current and capacity of the battery;

[0059] Monitor the battery voltage and enter the constant voltage charging phase when the voltage reaches a preset ratio of the battery's rated voltage;

[0060] Set the charging voltage to the battery's charge termination voltage;

[0061] During the constant voltage charging phase, the charging current gradually decreases until it reaches the preset cut-off current value, and the charging process ends;

[0062] At each stage, the charging current is adjusted based on the battery's real-time state of charge to optimize charging efficiency;

[0063] Dynamically adjust the charging termination conditions based on the real-time voltage, current and temperature information of the battery. When the battery voltage reaches the charging termination voltage and the charging current drops below the cut-off current value, the charging process ends.

[0064] The charge termination conditions are adjusted based on individual battery differences and aging conditions.

[0065] 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 charge and discharge power control instructions, and to perform dynamic resource allocation and delay control, specifically including:

[0066] 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;

[0067] According to the requirements of the battery management system, a fixed execution cycle is set for each task;

[0068] 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;

[0069] 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.

[0070] Adopting a priority preemptive scheduling strategy, when the priority of a new task is higher than that of the current task, the new task preempts the processor time of the current task;

[0071] Generate charge and discharge power control instructions based on battery status information and preset charge and discharge strategies;

[0072] Dynamically adjust the cycle of charge and discharge power control instructions according to battery status changes and external temperature or load demand environmental factors;

[0073] By adjusting the time slice size and task cycle, the time for task switching and task waiting can be reduced.

[0074] Compared with the prior art, the present invention has the following beneficial effects:

[0075] 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 maintains 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 use of a real-time operating system and a 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

[0076] Figure 1 A diagram of a method for optimizing battery charge and discharge power control accuracy for a BMS system according to the present invention;

[0077] Figure 2 This is a flow chart of the method for constructing a second-order RC network equivalent circuit model of the present invention;

[0078] Figure 3 This is a flow chart of the method for identifying error sources and quantifying the current BMS charge and discharge power control error of the present invention;

[0079] Figure 4This is a flow chart of the method for selecting a Hall effect sensor and a voltage sampling chip to collect experimental data according to the present invention;

[0080] Figure 5 Flowchart of the method for establishing a temperature compensation model for charge and discharge efficiency according to the present invention;

[0081] Figure 6 This is a flow chart of the multi-stage constant current-constant voltage charging algorithm method of the present invention;

[0082] Figure 7 This is a flow chart of the method for dynamic resource allocation and delay control of the present invention. DETAILED DESCRIPTION

[0083] The following description is intended to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are merely examples, and those skilled in the art may conceive of other obvious variations.

[0084] Reference Figure 1 As shown, a method for optimizing battery charge and discharge power control accuracy for a BMS system includes:

[0085] Construct a second-order RC network equivalent circuit model and fit the model parameters through pulse charge and discharge experiments;

[0086] Through experimental testing and data analysis, we identify and quantify the error sources of the current BMS charge and discharge power control error, including the hardware, software, and system levels.

[0087] Hall effect sensors and voltage sampling chips were selected for experimental data acquisition. The sensors were calibrated regularly using a standard signal source, and a temperature compensation circuit was introduced to reduce temperature drift errors.

[0088] Deploy at least one temperature sensor at the tab and center of the battery module, establish a temperature compensation model for charge and discharge efficiency, and adjust power based on temperature feedback;

[0089] Adaptive control algorithm is introduced to dynamically adjust the proportional, integral and differential coefficients according to the error;

[0090] Adopt a multi-stage constant current-constant voltage charging algorithm and optimize the charging and discharging power parameters based on battery characteristics;

[0091] Integrate capacity attenuation model and internal resistance growth model to dynamically adjust power limit and design redundant control loops to trigger safety protection for abnormal power fluctuations;

[0092] At the system software architecture level, a real-time operating system and time-triggered task scheduling mechanism are used to control the cycle of charging and discharging power control instructions, and perform dynamic resource allocation and delay control.

[0093] 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:

[0094] The second-order RC network equivalent circuit model uses a RC loop to simulate the electrochemical and concentration polarization phenomena inside the battery;

[0095] 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;

[0096] 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 by adding them together to obtain the ohmic internal resistance value.

[0097] The resistance and capacitance links are divided into polarization resistance and polarization capacitance to simulate the process of battery voltage slowly changing after discharge due to concentration polarization and diffusion effect.

[0098] 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;

[0099] 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;

[0100] Connect the battery to a charge and discharge tester and perform a pulse charge and discharge experiment;

[0101] 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;

[0102] Use the fitted model to predict the experimental data and compare the differences between the predicted results and the actual measured data;

[0103] Determine 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, readjust the model structure and refit the model parameters.

[0104] 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. Based on 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.

[0105] Reference Figure 3 As shown in the figure, through experimental testing and data analysis, the error sources are identified and quantified. Specifically, the error of the current BMS charge and discharge power control is:

[0106] The collected data is processed to calculate the difference between the actual charge and discharge power and the BMS control target power, and output as an error;

[0107] Analyze the error data and preliminarily determine whether the error comes from the hardware level, software level, or system level;

[0108] Output errors caused by insufficient sensor accuracy, circuit component aging, and unstable performance as hardware-level errors;

[0109] Output errors caused by inaccurate control algorithms and errors in data acquisition and processing codes as software-level errors;

[0110] Output the errors caused by sudden changes in system load and ambient temperature fluctuations as system-level errors;

[0111] For the identified error sources, further quantify their specific impact on the charge and discharge power control error;

[0112] For hardware-level errors, quantify the errors by calibrating sensors and replacing components;

[0113] For software-level errors, simulation and code review methods are used to quantify the errors;

[0114] 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.

[0115] The errors from different levels are summarized to comprehensively evaluate the total error of BMS charging and discharging power control.

[0116] 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.

[0117] Reference Figure 4 As shown in the figure, Hall effect sensors and voltage sampling chips are selected for experimental data acquisition. The sensors are calibrated regularly using a standard signal source, and a temperature compensation circuit is introduced to reduce the temperature drift error. Specifically, the following steps are involved:

[0118] Use a Hall effect sensor to measure the magnetic field change and convert it into a voltage signal, and use a voltage sampling chip to collect the voltage signal output by the Hall effect sensor;

[0119] 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 to correct the deviation caused by temperature in real time.

[0120] Connecting a standard signal source to a Hall effect sensor and setting parameters of the Hall effect sensor in a supporting device, wherein the parameters of the Hall effect sensor include a range and a unit;

[0121] Record the output value of the sensor under the action of the standard signal source and subtract it from the theoretical value. The output result is the sensor error.

[0122] 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;

[0123] 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;

[0124] storing the collected data in a computer-readable storage medium;

[0125] During the experiment, the operating temperature of the circuit was monitored in real time;

[0126] The temperature compensation circuit automatically adjusts the circuit parameters according to the real-time monitored operating temperature to correct the deviation caused by temperature;

[0127] Check the working effect of the temperature compensation circuit to ensure that the temperature drift error is within the preset acceptable range.

[0128] 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 sensor's performance parameters 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.

[0129] 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 the effect of temperature on charge and discharge efficiency is established, and power is adjusted based on temperature feedback. Specifically, the following steps are involved:

[0130] The tab is a key part of the battery module for current input and output. Its temperature change reflects the thermal effect of the battery module during the charging and discharging process. Temperature sensors are deployed here to monitor the temperature changes of the battery module in real time during charging and discharging.

[0131] The center area is where the temperature inside the battery module is the highest. 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.

[0132] Temperature sensors deployed on the tabs and center areas collect temperature data of the battery module during the charge and discharge process, as well as the corresponding charge and discharge efficiency data.

[0133] Analyze the collected temperature data and charge-discharge efficiency data and establish a linear model of the relationship between temperature and charge-discharge efficiency;

[0134] Based on the data analysis results, a compensation model for the effect of temperature on charge and discharge efficiency is established. Based on the real-time temperature data, changes in charge and discharge efficiency caused by temperature changes are predicted and compensated.

[0135] Feedback the monitored temperature data to the BMS system;

[0136] 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 operates in the best condition;

[0137] During the power regulation process, the BMS system monitors the voltage and current status of the battery module in real time and takes protective measures when abnormal conditions are detected, including overcharging, over-discharging and overheating.

[0138] 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.

[0139] 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:

[0140] The current value of the initial constant current charging stage is set according to the maximum charging current and capacity of the battery;

[0141] Monitor the battery voltage and enter the constant voltage charging phase when the voltage reaches a preset ratio of the battery's rated voltage;

[0142] Set the charging voltage to the battery's charge termination voltage;

[0143] During the constant voltage charging phase, the charging current gradually decreases until it reaches the preset cut-off current value, and the charging process ends;

[0144] At each stage, the charging current is adjusted based on the battery's real-time state of charge to optimize charging efficiency;

[0145] Dynamically adjust the charging termination conditions based on the real-time voltage, current and temperature information of the battery. When the battery voltage reaches the charging termination voltage and the charging current drops below the cut-off current value, the charging process ends.

[0146] The charge termination conditions are adjusted based on individual battery differences and aging conditions.

[0147] When it is monitored that the battery voltage reaches a certain 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. Due to individual differences between batteries and aging phenomena that occur with increasing 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 parameters such as the cut-off current value to ensure safe and efficient charging.

[0148] Reference Figure 7 As 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. Specifically, the following are included:

[0149] 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;

[0150] According to the requirements of the battery management system, a fixed execution cycle is set for each task;

[0151] 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;

[0152] 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.

[0153] Adopting a priority preemptive scheduling strategy, when the priority of a new task is higher than that of the current task, the new task preempts the processor time of the current task;

[0154] Generate charge and discharge power control instructions based on battery status information and preset charge and discharge strategies;

[0155] Dynamically adjust the cycle of charge and discharge power control instructions according to battery status changes and external temperature or load demand environmental factors;

[0156] By adjusting the time slice size and task cycle, the time for task switching and task waiting can be reduced.

[0157] Based on the importance and urgency of the tasks, different priorities are assigned to each of the three tasks. The charge and discharge control task may have the highest priority to ensure the safe and efficient operation of the battery; the condition 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 anomaly is detected.

[0158] Furthermore, the present solution also proposes a computer-readable storage medium having a computer-readable program stored thereon. When the computer-readable program is called, the above-mentioned method for optimizing battery charge and discharge power control accuracy for a BMS system is executed.

[0159] 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 disk (SSD).

[0160] In summary, the advantages of the present invention are that: 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 maintains the optimal 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 use of a real-time operating system and a 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 performance and stability of the BMS system under complex working conditions and improve the response speed and operating efficiency of the entire system.

[0161] 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 merely illustrate the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for optimizing battery charge and discharge 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, we identify and quantify the error sources of the current BMS charge and discharge power control error, including the hardware, software, and system levels. Hall effect sensors and voltage sampling chips were selected for experimental data acquisition. The sensors were calibrated regularly using a standard signal source, and a temperature compensation circuit was introduced to reduce temperature drift errors. Deploy at least one temperature sensor at the tab and center of the battery module, establish a temperature compensation model for charge and discharge efficiency, and adjust power based on temperature feedback; Adaptive control algorithm is introduced to dynamically adjust the proportional, integral and differential coefficients according to the error; Adopt a multi-stage constant current-constant voltage charging algorithm and optimize the charging and discharging power parameters based on battery characteristics; Integrate capacity attenuation model and internal resistance growth model to dynamically adjust power limit and design redundant control loops to trigger safety protection for abnormal power fluctuations; At the system software architecture level, a real-time operating system and 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. The construction of a second-order RC network equivalent circuit model and fitting model parameters through pulse charge and discharge experiments 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 by adding them together to obtain the ohmic internal resistance value. The resistance and capacitance links are divided into polarization resistance and polarization capacitance to simulate the process of battery voltage slowly changing 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 a charge and discharge tester and perform a pulse charge and discharge experiment; 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 differences between the predicted results and the actual measured data; Determine 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, readjust the model structure and refit the model parameters. The method of deploying at least one temperature sensor at the tab and center of the battery module, establishing a compensation model for the effect of temperature on charge and discharge efficiency, and adjusting power based on temperature feedback specifically includes: The tab is a key part of the battery module for current input and output. Its temperature change reflects the thermal effect of the battery module during the charging and discharging process. Temperature sensors are deployed here to monitor the temperature changes of the battery module in real time during charging and discharging. The center area is where the temperature inside the battery module is the highest. 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. Temperature sensors deployed on the tabs and center areas collect temperature data of the battery module during the charge and discharge process, as well as the corresponding charge and discharge 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 charge and discharge efficiency is established. Based on the real-time temperature data, changes in charge and discharge 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 operates in the best condition; During the power regulation process, the BMS system monitors the voltage and current status of the battery module in real time and takes protective measures when abnormal conditions are detected, including overcharging, over-discharging and overheating.

2. The method for optimizing battery charge and discharge power control accuracy for a BMS system according to claim 1, characterized in that: The experimental testing and data analysis to identify the error sources and quantify the current BMS charge and discharge power control error specifically include: The collected data is processed to calculate the difference between the actual charge and discharge power and the BMS control target power, and output 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.

3. The method for optimizing battery charge and discharge power control accuracy for a BMS system according to claim 2, characterized in that: The selection of Hall effect sensors and voltage sampling chips for experimental data acquisition, 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: Use a Hall effect sensor to measure the magnetic field change and convert it into a voltage signal, and use a voltage sampling chip 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 to correct the deviation caused by temperature in real time. Connecting a standard signal source to a Hall effect sensor and setting parameters of the Hall effect sensor in a supporting device, 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 subtract it from 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 operating 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.

4. The method for optimizing battery charge and discharge power control accuracy for a BMS system according to claim 3, characterized in that: The multi-stage constant current-constant voltage charging algorithm is used, and the charging and discharging power parameter optimization is performed in combination with 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 enter the constant voltage charging phase when the voltage reaches a preset ratio of the battery's rated voltage; Set the charging voltage to the battery's charge termination voltage; During the constant voltage charging phase, 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 battery's real-time state of charge to optimize charging efficiency; Dynamically adjust the charging termination conditions based on the real-time voltage, current and temperature information of the battery. 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 conditions.

5. The method for optimizing battery charge and discharge power control accuracy for a BMS system according to claim 4, 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, the following are included: 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 a priority preemptive scheduling strategy, when the priority of a new task is higher than that of the current task, the new task preempts the processor time of the current task; Generate charge and discharge power control instructions based on battery status information and preset charge and discharge 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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