Battery intelligent regulation method and system based on charge-discharge terminal SOC dynamic calibration

Abstract

The application proposes a battery intelligent regulation method and system based on charge-discharge terminal SOC dynamic calibration, the method comprising: collecting battery operation data in real time through a sensor, and performing pretreatment operation on the battery operation data; constructing an SOC estimation model based on the pretreated battery operation data to obtain an SOC estimation value; updating the SOC estimation value through an error compensation mechanism according to the SOC estimation value, and introducing a temperature compensation factor to optimize the updated SOC estimation value to obtain an optimized SOC estimation value; and dynamically adjusting the charge-discharge power through a PID control algorithm based on the optimized SOC estimation value. The technical scheme proposed by the application significantly improves the accuracy and environmental adaptability of SOC estimation through the double mechanisms of error compensation and temperature compensation, and realizes intelligent, smooth and safe regulation of the battery charge-discharge process by combining adaptive PID control and nonlinear power mapping, effectively prolongs the service life of the battery and improves the energy management efficiency.

Description

Technical Field

[0001] This application relates to the field of battery charging and discharging technology, and in particular to a battery intelligent control method and system based on dynamic calibration of SOC at the end of charging and discharging. Background Technology

[0002] Batteries, as key devices for energy storage and conversion, are widely used in electric vehicles, renewable energy storage, mobile electronic devices, and backup power systems. The battery management system (BMS), as the core of safe and efficient battery operation, has one of its core functions: accurately estimating the battery's state of charge (SOC). SOC not only directly reflects the battery's remaining usable energy but is also a fundamental parameter for optimizing battery control, preventing overcharging and over-discharging, and extending battery life.

[0003] Currently, common SOC estimation methods mainly include the open-circuit voltage method, the ampere-hour integral method (Coulomb counting method), the Kalman filter method, and the neural network method. Among them, the ampere-hour integral method is widely used due to its simplicity and low computational cost. However, this method has significant drawbacks in practical applications: First, its estimation accuracy heavily depends on the accuracy of the initial SOC value and the accuracy of the current measurement. Over long-term operation, the current integration error will accumulate, causing the SOC estimation to deviate from the actual value. Second, this method does not fully consider the impact of temperature on battery capacity, internal resistance, and charge / discharge efficiency. In application scenarios with large ambient temperature variations, the estimation error will be further amplified. In addition, traditional battery charge / discharge control strategies often use fixed-parameter PID control or simple threshold control, which is difficult to adapt to the nonlinear dynamic characteristics of batteries under different SOCs and temperatures. This can easily lead to problems such as control response lag and large power fluctuations, affecting the stability and safety of the battery system.

[0004] While some existing technologies attempt to compensate for State of Charge (SOC) through voltage correction or temperature lookup tables, most methods suffer from simple compensation models and limited adaptability, failing to achieve dynamic closed-loop error correction and nonlinear temperature coupling compensation. Furthermore, at the power control level, there is a lack of adaptive regulation mechanisms based on real-time SOC calibration, making it difficult to achieve refined energy management while ensuring safety. Therefore, there is an urgent need for a battery management method capable of achieving dynamic high-precision SOC calibration and intelligent adaptive regulation based on the calibration results to improve the performance, safety, and reliability of battery systems under various operating conditions. Summary of the Invention

[0005] This application provides a battery intelligent control method and system based on dynamic calibration of SOC at the end of charge and discharge, in order to at least solve the technical problems in the prior art, such as low SOC estimation accuracy, poor environmental adaptability, unstable charge and discharge control, and delayed safety response caused by temperature changes, error accumulation and fixed control parameters.

[0006] The first aspect of this application proposes a battery intelligent control method based on dynamic calibration of SOC at the end of charge and discharge, the method comprising: The battery operation data is collected in real time by sensors, and the battery operation data is preprocessed. A SOC estimation model is constructed based on the preprocessed battery operating data to obtain the estimated SOC value. The SOC estimate is updated using an error compensation mechanism based on the SOC estimate, and a temperature compensation factor is introduced to optimize the updated SOC estimate to obtain the optimized SOC estimate. Based on the optimized SOC estimate, the charging and discharging power is dynamically adjusted using a PID control algorithm.

[0007] Preferably, the method further includes; When there is a risk of short circuit, the power outage protection is set through the Bang-Bang control and an alarm signal is issued. The battery operating data includes voltage data, current data, battery capacity data, and temperature data.

[0008] Furthermore, the preprocessing operation of the battery operating data includes: Outliers in the battery operating data are identified and removed using statistical methods, and missing values ​​are filled in the battery operating data after outlier removal using interpolation. Low-pass filtering is applied to the processed battery operating data; Z-Score normalization was performed on the filtered battery operating data.

[0009] Furthermore, the step of constructing a SOC estimation model based on preprocessed battery operating data to obtain an estimated SOC value includes: Based on the preprocessed battery operating data, features are extracted using a time-domain feature method to obtain battery operating features, which include: current value and coulombic efficiency at each time point. A SOC estimation model is constructed using the coulomb counting method, and the battery operating characteristics are input into the SOC estimation model to obtain the estimated SOC value. The calculation formula for the SOC estimation model is as follows:

[0010] In the formula, For the current moment SOC estimate This is the initial value of SOC. This refers to the battery's nominal capacity. For time The current value at that point, For Coulomb efficiency, This represents the integral variable.

[0011] Furthermore, the step of updating the SOC estimate based on the SOC estimate through an error compensation mechanism, and introducing a temperature compensation factor to optimize the updated SOC estimate to obtain an optimized SOC estimate, includes: The SOC difference is determined based on the estimated SOC value and the collected battery capacity data. Based on the SOC difference, an updated SOC estimate is obtained through linear correction; A temperature compensation factor is introduced to optimize the updated SOC estimate, resulting in an optimized SOC estimate. The temperature compensation factor is calculated as follows:

[0012] In the formula, For temperature compensation factor, It has a linear temperature coefficient. It is a second-order temperature coefficient. The current battery temperature. This is a reference temperature.

[0013] The optimized SOC estimate is calculated as follows:

[0014] In the formula, This is the optimized SOC estimate. This is the updated SOC estimate.

[0015] Furthermore, the PID control algorithm includes: Obtain the target SOC value and calculate the error between the optimized SOC estimate and the target SOC value; Based on the optimized SOC estimate and the error, the PID parameters are dynamically adjusted to generate a control signal; The control signal is converted into charging and discharging power through a Sigmoid nonlinear mapping.

[0016] Furthermore, the formula for adjusting the PID parameters is as follows:

[0017]

[0018]

[0019] In the formula, This is the initial proportional gain. This is the initial integral gain. For the initial differential gain, The proportional gain adjusted at time t. The integral gain adjusted at time t. The differential gain adjusted at time t; in, , The center point of the Sigmoid curve, The slope factor is used; the PID parameters satisfy... , , , for The minimum value of the parameter. for The maximum value of the parameter, for The minimum value of the parameter. for The maximum value of the parameter, for The minimum value of the parameter. for The maximum value of the parameter.

[0020] Furthermore, the conversion of the control signal into charge / discharge power via the Sigmoid nonlinear mapping includes: Using formula The control signal is converted into charging power, where, The actual charging power at time t. Maximum charging power, For the threshold, The control signal at time t; Using formula The control signal is converted into discharge power, where, Let be the actual discharge power at time t. This represents the maximum discharge power.

[0021] Furthermore, the actual charging power satisfies , The charging power after time t is limited; The actual discharge power satisfies , Let t be the discharge power after the time limit.

[0022] The second aspect of this application proposes a battery intelligent control system based on dynamic SOC calibration at the end of charge and discharge, comprising: The preprocessing module is used to collect battery operating data in real time through sensors and perform preprocessing operations on the battery operating data. The module is used to build a SOC estimation model based on preprocessed battery operating data and obtain an estimated SOC value. The optimization module is used to update the SOC estimate based on the SOC estimate through an error compensation mechanism, and to introduce a temperature compensation factor to optimize the updated SOC estimate to obtain an optimized SOC estimate. The adjustment module is used to dynamically adjust the charging and discharging power based on the optimized SOC estimate using a PID control algorithm.

[0023] The technical solutions provided by the embodiments of this application bring at least the following beneficial effects: This application proposes a battery intelligent control method and system based on dynamic calibration of SOC at the end of charge and discharge. The method includes: acquiring battery operating data in real time through sensors and preprocessing the battery operating data; constructing an SOC estimation model based on the preprocessed battery operating data to obtain an estimated SOC value; updating the estimated SOC value through an error compensation mechanism and introducing a temperature compensation factor to optimize the updated SOC value to obtain an optimized SOC value; and dynamically adjusting the charge and discharge power based on the optimized SOC value using a PID control algorithm. The technical solution proposed in this application significantly improves the accuracy and environmental adaptability of SOC estimation through a dual mechanism of error compensation and temperature compensation. Combined with adaptive PID control and nonlinear power mapping, it achieves intelligent, stable, and safe control of the battery charge and discharge process, effectively extending battery life and improving energy management efficiency.

[0024] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0025] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart of a battery intelligent control method based on dynamic calibration of SOC at the end of charge and discharge, according to an embodiment of this application; Figure 2 This is a structural diagram of a battery intelligent control system based on dynamic calibration of SOC at the end of charge and discharge, according to an embodiment of this application. Detailed Implementation

[0026] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0027] This application proposes a battery intelligent control method and system based on dynamic calibration of SOC at the end of charge and discharge. The method includes: real-time acquisition of battery operating data through sensors and preprocessing the battery operating data; constructing an SOC estimation model based on the preprocessed battery operating data to obtain an estimated SOC value; updating the estimated SOC value through an error compensation mechanism and introducing a temperature compensation factor to optimize the updated SOC value to obtain an optimized SOC value; and dynamically adjusting the charge and discharge power based on the optimized SOC value using a PID control algorithm. The technical solution proposed in this application significantly improves the accuracy and environmental adaptability of SOC estimation through a dual mechanism of error compensation and temperature compensation. Combined with adaptive PID control and nonlinear power mapping, it achieves intelligent, stable, and safe control of the battery charge and discharge process, effectively extending battery life and improving energy management efficiency.

[0028] The following describes, with reference to the accompanying drawings, a battery intelligent control method and system based on dynamic calibration of SOC at the end of charge and discharge.

[0029] Example 1 Figure 1 This is a flowchart illustrating a battery intelligent control method based on dynamic SOC calibration at the end of charge / discharge according to an embodiment of this application. Figure 1 As shown, the method includes: Step 1: Collect battery operating data in real time using sensors and perform preprocessing operations on the battery operating data; In this embodiment of the disclosure, the preprocessing operation of the battery operating data includes: Outliers in the battery operating data are identified and removed using statistical methods, and missing values ​​are filled in the battery operating data after outlier removal using interpolation. Low-pass filtering is applied to the processed battery operating data; Z-Score normalization was performed on the filtered battery operating data.

[0030] It should be noted that the specific process of step 1 is as follows: S1.1 Identify and remove outliers using statistical methods, and fill in missing values ​​using interpolation. S1.2, Based on S1.1, the battery operation data reduces the impact of noise on battery environmental measurements through low-pass filtering technology; S1.3. Based on the filtered battery operating data, Z-Score is used for normalization to convert data of different scales to the same range.

[0031] Step 2: Construct a SOC estimation model based on the preprocessed battery operating data to obtain the estimated SOC value; In this embodiment of the disclosure, step 2 specifically includes: Based on the preprocessed battery operating data, features are extracted using a time-domain feature method to obtain battery operating features, which include: current value and coulombic efficiency at each time point. A SOC estimation model is constructed using the coulomb counting method, and the battery operating characteristics are input into the SOC estimation model to obtain the estimated SOC value. The calculation formula for the SOC estimation model is as follows:

[0032] In the formula, For the current moment SOC estimate This is the initial value of SOC. This refers to the battery's nominal capacity. For time The current value at that point, For Coulomb efficiency, This represents the integral variable.

[0033] It should be noted that the specific process of step 2 is as follows: S2.1. Based on the preprocessed battery operating data, feature extraction is performed using time-domain feature methods to obtain battery operating features; S2.2. Construct a SOC estimation model using the Coulomb counting method, and input the battery operating characteristics extracted in S2.1 into the SOC estimation model to obtain the estimated SOC value. The mathematical expression of the SOC estimation model is: ; In the formula, Indicates the current time The estimated SOC value is the proportion of the battery's current remaining charge to its total capacity; This represents the initial SOC value (state of charge at the initial moment), which is usually calibrated at the beginning of each complete charge-discharge cycle; This indicates the battery's nominal capacity, which is the battery's rated capacity as specified by the manufacturer. Indicates time The current value at the point is positive when charging and negative when discharging; Coulomb efficiency is the ratio of the actual effective charge / discharge to the theoretical value. This represents the integration variable (representing time), used as the time variable in the integration process.

[0034] Step 3: Update the SOC estimate based on the SOC estimate through an error compensation mechanism, and introduce a temperature compensation factor to optimize the updated SOC estimate to obtain the optimized SOC estimate; In this embodiment of the disclosure, step 3 specifically includes: The SOC difference is determined based on the estimated SOC value and the collected battery capacity data. Based on the SOC difference, an updated SOC estimate is obtained through linear correction; A temperature compensation factor is introduced to optimize the updated SOC estimate, resulting in an optimized SOC estimate. The temperature compensation factor is calculated as follows:

[0035] In the formula, The temperature compensation factor is calculated based on the current battery temperature, and its value range is [value range missing]. ; It is a linear temperature coefficient that describes the first-order effect of temperature change on battery performance. This is the second-order temperature coefficient, describing the second-order effect of temperature changes on battery performance; This is the current battery temperature, which is the battery operating temperature measured in real time. This is a reference temperature, typically the temperature under standard test conditions.

[0036] The optimized SOC estimate is calculated as follows:

[0037] In the formula, The optimized SOC estimate is a more accurate SOC estimate that takes into account the effect of temperature. This is the updated SOC estimate.

[0038] It should be noted that traditional methods such as coulomb counting accumulate errors over long periods of operation, causing the SOC estimate to deviate from the actual value. Introducing an error compensation mechanism can dynamically adjust and correct these errors. The battery's operating environment and conditions (such as temperature and load variations) affect its internal characteristics and performance, thus impacting the SOC estimate. The error compensation mechanism can dynamically adjust the SOC estimate based on actual conditions to adapt to different operating conditions; accurate SOC estimation is the foundation for efficient energy management. By compensating for errors in the SOC estimate, the performance of the entire system can be optimized, including but not limited to extending battery life, improving energy utilization, and preventing overcharging / over-discharging issues.

[0039] It should be noted that when the SOC value exceeds a reasonable range (e.g., less than 0 or greater than 1), a cropping process can be performed. The mathematical expression involved in cropping the SOC value is as follows: .

[0040] Introducing a temperature compensation factor to optimize the SOC estimate can eliminate SOC estimation bias caused by temperature changes, improve the adaptability of the SOC estimation model under different working environments, extend battery life, and avoid overcharging / over-discharging behavior caused by misjudgment.

[0041] It should be noted that the specific process of step 3 is as follows: S3.1. Based on the difference between the estimated SOC and the actual calculated SOC, obtain the SOC difference. Then calculate the SOC difference. The mathematical expressions involved are: ; In the formula, This represents the SOC estimation error, which is the deviation between the estimated value and the true value. Indicates the actual SOC, which is the theoretically true percentage of remaining battery capacity. S3.2, SOC difference obtained based on S3.1 The corrected SOC estimate is obtained through the linear correction formula. Then the mathematical expression involved in updating the SOC estimate is: ; In the formula, This represents the corrected SOC estimate, which is the updated SOC value after error compensation. This represents the correction coefficient, which is the strength of the control error correction. A larger value indicates a faster correction. The value range is... ; S3.3. Considering the impact of battery temperature on the SOC estimate, a temperature compensation factor is introduced to optimize the SOC estimate, resulting in an optimized SOC estimate. .

[0042] Traditional methods such as coulomb counting accumulate errors over long periods, causing State of Charge (SOC) estimates to deviate from actual values. Introducing an error compensation mechanism can dynamically adjust and correct these errors. The battery's operating environment and conditions (e.g., temperature, load variations) affect its internal characteristics and performance, thus influencing SOC estimates. The error compensation mechanism dynamically adjusts the SOC estimate based on actual conditions to adapt to different operating conditions; accurate SOC estimation is fundamental to efficient energy management. By compensating for errors in the SOC estimate, the performance of the entire system can be optimized, including but not limited to extending battery life, improving energy efficiency, and preventing overcharging / over-discharging.

[0043] In this embodiment S3.3, considering the impact of battery temperature on the SOC estimate, the specific steps for optimizing the SOC estimate by introducing a temperature compensation factor are as follows: S3.3.1 Calculate the temperature compensation factor based on the current battery temperature. Then calculate the temperature compensation factor. The mathematical expressions involved are: ; In the formula, This represents the temperature compensation factor, which is calculated based on the current battery temperature, and its value range is... ; It represents the linear temperature coefficient, describing the first-order effect of temperature changes on battery performance; It represents the second-order temperature coefficient, which describes the second-order effect of temperature changes on battery performance; This indicates the current battery temperature, which is the battery operating temperature measured in real time. This indicates the reference temperature, which is usually the temperature under standard test conditions. S3.3.2 Temperature compensation factor obtained based on S3.3.1 Once the temperature-compensated SOC value is calculated, the mathematical expression involved in calculating the temperature-compensated SOC value is as follows: ; In the formula, This represents the SOC value after temperature compensation, which is a more accurate SOC estimate after taking into account the effect of temperature.

[0044] When the SOC value exceeds a reasonable range (e.g., less than 0 or greater than 1), a cropping process can be performed. The mathematical expression involved in cropping the SOC value is as follows: .

[0045] Introducing a temperature compensation factor to optimize the SOC estimate can eliminate SOC estimation bias caused by temperature changes, improve the adaptability of the SOC estimation model under different working environments, extend battery life, and avoid overcharging / over-discharging behavior caused by misjudgment.

[0046] Step 4: Based on the optimized SOC estimate, dynamically adjust the charging and discharging power using a PID control algorithm.

[0047] It should be noted that the PID control algorithm includes: Obtain the target SOC value and calculate the error between the optimized SOC estimate and the target SOC value; Based on the optimized SOC estimate and the error, the PID parameters are dynamically adjusted to generate a control signal; The control signal is converted into charging and discharging power through a Sigmoid nonlinear mapping.

[0048] It should be noted that the formula for adjusting the PID parameters is as follows:

[0049]

[0050]

[0051] In the formula, This is the initial proportional gain. This is the initial integral gain. For the initial differential gain, The proportional gain adjusted at time t. The integral gain adjusted at time t. The differential gain adjusted at time t; in, , The center point of the Sigmoid curve, The slope factor is used; the PID parameters satisfy... , , , for The minimum value of the parameter. for The maximum value of the parameter, for The minimum value of the parameter. for The maximum value of the parameter, for The minimum value of the parameter. for The maximum value of the parameter.

[0052] It should be noted that the conversion of the control signal into charging and discharging power via the Sigmoid nonlinear mapping includes: Using formula The control signal is converted into charging power, where, The actual charging power at time t. Maximum charging power, For the threshold, The control signal at time t; Using formula The control signal is converted into discharge power, where, Let be the actual discharge power at time t. This represents the maximum discharge power.

[0053] It should be noted that the actual charging power meets the requirements. , The charging power after time t is limited; The actual discharge power satisfies , Let t be the discharge power after the time limit.

[0054] It should be noted that step 4 specifically includes the following process: S4.1 Based on the updated SOC estimate Error in calculation of target SOC value Then the calculation error The mathematical expressions involved are: ; In the formula, Indicates time The error value at this point is the difference between the current SOC and the target SOC; the target SOC value represents the ideal state of charge that the control system hopes to achieve. S4.2, the obtained error The input is fed into the PID control algorithm, and the obtained SOC estimate is used... By dynamically adjusting the PID parameters, a control signal for adjusting the charging and discharging power can be obtained. Then calculate the control signal. The mathematical expressions involved are: ; In the formula, This indicates a control signal that adjusts the charging and discharging power. This represents the proportional term, based on the current error. Adjust the controller output; This represents the integral term, which adjusts the controller output based on the accumulated error. This represents the differential term, which adjusts the controller output based on the rate of change of the error. S4.3 Control signals obtained based on S4.2 The control signal is controlled through a nonlinear relationship. It is converted into charging and discharging power.

[0055] In this embodiment S4.2, the obtained SOC estimate is used... The specific steps for dynamically adjusting PID parameters are as follows: S4.2.1, Using the obtained SOC estimate When dynamically adjusting PID parameters, the mathematical expression involved in adjusting the PID parameters is: ; ; ; in, ; In the formula, Indicates the initial proportional gain; Indicates the initial integral gain; Indicates the initial differential gain; This represents the nonlinear adjustment function of SOC, used to adjust the P, I, and D gains. near When the function value is close to ;when much smaller When the function value approaches ;when Much larger When the function value approaches ; This represents the center point of the Sigmoid curve, i.e., the point where the function value is 0.5. value; This represents the slope factor, which controls the steepness of the curve; The larger the value, the steeper the curve. The smaller the value, the flatter the curve.

[0056] S4.2.2 If upper and lower limit constraints are set for each PID parameter, then the mathematical expression for the constraint of each PID parameter is: ; ; ; In the formula, express The minimum value of the parameter; express The maximum value of the parameter; express The minimum value of the parameter; express The maximum value of the parameter; express The minimum value of the parameter; express The maximum value of the parameter.

[0057] In this embodiment, S4.3, the control signal obtained in S4.2 is... The control signal is controlled through a nonlinear relationship. The specific steps for converting power into charge / discharge power are as follows: S4.3.1, Based on the Sigmoid nonlinear mapping, the control signal Converted into charging and discharging power, the control signal will be... The mathematical expression involved in converting this into charging power: ; In the formula, Indicates the actual charging power over time. The actual output charging power value; This indicates the maximum charging power, which is the maximum charging power allowed by the system and is usually determined by the limitations of the battery or charging equipment. This represents the threshold value when the input... near When the time is right, the output of the sigmoid function is approximately That is, the power is half of the maximum value; control signal The mathematical expression involved in converting it into discharge power: ; In the formula, Indicates the actual discharge power; S4.3.2 To set upper and lower limits for charging and discharging power, the mathematical expression for setting the upper limit constraint for charging power is: ; In the formula, This indicates the limited charging power over time. The actual allowable charging power value is the result after limitation processing; The mathematical expression involved in setting the lower limit constraint of discharge power is: ; In the formula, This represents the limited discharge power over time. The actual allowable discharge power value is the result after limiting the discharge.

[0058] In this embodiment of the disclosure, the method further includes: When there is a risk of short circuit, the power outage protection is set through the Bang-Bang control and an alarm signal is issued. The battery operating data includes voltage data, current data, battery capacity data, and temperature data.

[0059] It should be noted that the specific steps for setting up power failure protection via Bang-Bang control to protect the circuit from electrical faults are as follows: Based on the equipment's rated operating power and safety margin, a maximum allowable charging and discharging power is set. If the actual charging and discharging power... If the maximum permissible charging and discharging power is exceeded, a short circuit risk is considered to exist, and the power-off mechanism will be triggered immediately to quickly cut off the power supply to the charging and discharging components to prevent further energy input from causing more serious damage or fire.

[0060] The intelligent battery control method based on dynamic SOC calibration at the end of charge and discharge proposed in this embodiment has the following beneficial effects: 1. In this intelligent battery control method based on dynamic calibration of SOC at the end of charging and discharging, a SOC estimation model is constructed to achieve a basic estimation of the battery's state of charge and track the changes in charge during the charging and discharging process. This solves the problem that the traditional SOC estimation method is affected by initial error and current integral drift, which leads to inaccurate estimation.

[0061] 2. In this intelligent battery control method based on dynamic calibration of SOC at the end of charge and discharge, an error compensation mechanism is introduced to correct the SOC estimation error and improve the real-time performance and stability of the estimation results.

[0062] 3. In this intelligent battery control method based on dynamic calibration of SOC at the end of charge and discharge, a temperature compensation factor is introduced to consider the influence of ambient temperature on battery capacity, thereby improving the environmental adaptability of SOC estimation.

[0063] In summary, the intelligent battery control method proposed in this embodiment, based on dynamic calibration of SOC at the end of charging and discharging, significantly improves the accuracy of SOC estimation and environmental adaptability through a dual mechanism of error compensation and temperature compensation. Combined with adaptive PID control and nonlinear power mapping, it realizes intelligent, stable and safe control of the battery charging and discharging process, effectively extending battery life and improving energy management efficiency.

[0064] Example 2 Figure 2This is a structural diagram of a battery intelligent control system based on dynamic SOC calibration at the end of charge and discharge, according to an embodiment of this application. Figure 2 As shown, the system includes: The preprocessing module 100 is used to collect battery operating data in real time through sensors and perform preprocessing operations on the battery operating data. The battery operating data includes voltage data, current data, battery capacity data, and temperature data.

[0065] Module 200 is used to build a SOC estimation model based on preprocessed battery operating data and obtain an estimated SOC value. The optimization module 300 is used to update the SOC estimate value according to the SOC estimate value through an error compensation mechanism, and to introduce a temperature compensation factor to optimize the updated SOC estimate value to obtain an optimized SOC estimate value. The adjustment module 400 is used to dynamically adjust the charging and discharging power based on the optimized SOC estimate using a PID control algorithm.

[0066] In this embodiment of the disclosure, the adjustment module 400 is further configured to: When there is a risk of short circuit, the power outage protection is set up through the Bang-Bang control and an alarm signal is issued.

[0067] In this embodiment of the disclosure, the preprocessing module 100 is further configured to: Outliers in the battery operating data are identified and removed using statistical methods, and missing values ​​are filled in the battery operating data after outlier removal using interpolation. Low-pass filtering is applied to the processed battery operating data; Z-Score normalization was performed on the filtered battery operating data.

[0068] In this embodiment of the disclosure, the construction module 200 is further configured to: Based on the preprocessed battery operating data, features are extracted using a time-domain feature method to obtain battery operating features, which include: current value and coulombic efficiency at each time point. A SOC estimation model is constructed using the coulomb counting method, and the battery operating characteristics are input into the SOC estimation model to obtain the estimated SOC value. The calculation formula for the SOC estimation model is as follows:

[0069] In the formula, For the current moment SOC estimate This is the initial value of SOC. This refers to the battery's nominal capacity. For time The current value at that point, For Coulomb efficiency, This represents the integral variable.

[0070] In this embodiment of the disclosure, the optimization module 300 is further configured to: The SOC difference is determined based on the estimated SOC value and the collected battery capacity data. Based on the SOC difference, an updated SOC estimate is obtained through linear correction; A temperature compensation factor is introduced to optimize the updated SOC estimate, resulting in an optimized SOC estimate. The temperature compensation factor is calculated as follows:

[0071] In the formula, For temperature compensation factor, It has a linear temperature coefficient. It is a second-order temperature coefficient. The current battery temperature. This is a reference temperature.

[0072] The optimized SOC estimate is calculated as follows:

[0073] In the formula, This is the optimized SOC estimate. This is the updated SOC estimate.

[0074] In this embodiment of the disclosure, the adjustment module 400 is further configured to: Obtain the target SOC value and calculate the error between the optimized SOC estimate and the target SOC value; Based on the optimized SOC estimate and the error, the PID parameters are dynamically adjusted to generate a control signal; The control signal is converted into charging and discharging power through a Sigmoid nonlinear mapping.

[0075] The formula for adjusting the PID parameters is as follows:

[0076]

[0077]

[0078] In the formula, This is the initial proportional gain. This is the initial integral gain. For the initial differential gain, The proportional gain adjusted at time t. The integral gain adjusted at time t. The differential gain adjusted at time t; in, , The center point of the Sigmoid curve, The slope factor is used; the PID parameters satisfy... , , , for The minimum value of the parameter. for The maximum value of the parameter, for The minimum value of the parameter. for The maximum value of the parameter, for The minimum value of the parameter. for The maximum value of the parameter.

[0079] Furthermore, the adjustment module 400 is also used for: Using formula The control signal is converted into charging power, where, The actual charging power at time t. Maximum charging power, For the threshold, The control signal at time t; Using formula The control signal is converted into discharge power, where, Let be the actual discharge power at time t. This represents the maximum discharge power.

[0080] Wherein, the actual charging power satisfies , The charging power after time t is limited; The actual discharge power satisfies , Let t be the discharge power after the time limit.

[0081] In summary, the battery intelligent control system proposed in this embodiment, based on dynamic calibration of SOC at the end of charging and discharging, significantly improves the accuracy of SOC estimation and environmental adaptability through a dual mechanism of error compensation and temperature compensation. Combined with adaptive PID control and nonlinear power mapping, it realizes intelligent, stable and safe control of the battery charging and discharging process, effectively extending battery life and improving energy management efficiency.

[0082] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0083] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0084] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A battery intelligent control method based on dynamic calibration of SOC at the end of charge and discharge, characterized in that, The method includes: The battery operation data is collected in real time by sensors, and the battery operation data is preprocessed. A SOC estimation model is constructed based on the preprocessed battery operating data to obtain the estimated SOC value. The SOC estimate is updated using an error compensation mechanism based on the SOC estimate, and a temperature compensation factor is introduced to optimize the updated SOC estimate, resulting in an optimized SOC estimate, including: The SOC difference is determined based on the estimated SOC value and the collected battery capacity data. Based on the SOC difference, an updated SOC estimate is obtained through linear correction; A temperature compensation factor is introduced to optimize the updated SOC estimate, resulting in an optimized SOC estimate. The temperature compensation factor is calculated as follows: In the formula, This is the temperature compensation factor, with a value range of 0-1. It has a linear temperature coefficient. It is a second-order temperature coefficient. The current battery temperature. For reference temperature; The optimized SOC estimate is calculated as follows: In the formula, The optimized SOC estimate is... For the updated SOC estimate; Based on the optimized SOC estimate, the charging and discharging power is dynamically adjusted using a PID control algorithm.

2. The method as described in claim 1, characterized in that, The method further includes; When there is a risk of short circuit, the power outage protection is set through the Bang-Bang control and an alarm signal is issued. The battery operating data includes voltage data, current data, battery capacity data, and temperature data.

3. The method as described in claim 2, characterized in that, The preprocessing operation of the battery operating data includes: Outliers in the battery operating data are identified and removed using statistical methods, and missing values ​​are filled in the battery operating data after outlier removal using interpolation. Low-pass filtering is applied to the processed battery operating data; Z-Score normalization was performed on the filtered battery operating data.

4. The method as described in claim 3, characterized in that, The SOC estimation model is constructed based on the preprocessed battery operating data to obtain the estimated SOC value, including: Based on the preprocessed battery operating data, features are extracted using a time-domain feature method to obtain battery operating features, which include: current value and coulombic efficiency at each time point. A SOC estimation model is constructed using the coulomb counting method, and the battery operating characteristics are input into the SOC estimation model to obtain the estimated SOC value. The calculation formula for the SOC estimation model is as follows: In the formula, For the current moment SOC estimate This is the initial value of SOC. This refers to the battery's nominal capacity. For time The current value at that point, For Coulomb efficiency, This represents the integral variable.

5. The method as described in claim 4, characterized in that, The PID control algorithm includes: Obtain the target SOC value and calculate the error between the optimized SOC estimate and the target SOC value; Based on the optimized SOC estimate and the error, the PID parameters are dynamically adjusted to generate a control signal; The control signal is converted into charging and discharging power through a Sigmoid nonlinear mapping.

6. The method as described in claim 5, characterized in that, The formula for adjusting the PID parameters is as follows: In the formula, This is the initial proportional gain. This is the initial integral gain. For the initial differential gain, The proportional gain adjusted at time t. The integral gain adjusted at time t. The differential gain adjusted at time t; in, , The center point of the Sigmoid curve, The slope factor is used; the PID parameters satisfy... , , , for The minimum value of the parameter. for The maximum value of the parameter, for The minimum value of the parameter. for The maximum value of the parameter, for The minimum value of the parameter. for The maximum value of the parameter.

7. The method as described in claim 6, characterized in that, The process of converting the control signal into charge / discharge power via a Sigmoid nonlinear mapping includes: Using formula The control signal is converted into charging power, where, The actual charging power at time t. Maximum charging power, For the threshold, The control signal at time t; Using formula The control signal is converted into discharge power, where, The actual discharge power at time t. This represents the maximum discharge power.

8. The method as described in claim 7, characterized in that, The actual charging power satisfies , The charging power after time t is limited; The actual discharge power satisfies , Let t be the discharge power after the time limit.

9. A battery intelligent control system based on dynamic calibration of SOC at the end of charge and discharge, characterized in that, The system implements the method as described in claim 1, the system comprising: The preprocessing module is used to collect battery operating data in real time through sensors and perform preprocessing operations on the battery operating data. The module is used to build a SOC estimation model based on preprocessed battery operating data and obtain an estimated SOC value. The optimization module is used to update the SOC estimate based on the SOC estimate through an error compensation mechanism, and to introduce a temperature compensation factor to optimize the updated SOC estimate to obtain an optimized SOC estimate. The adjustment module is used to dynamically adjust the charging and discharging power based on the optimized SOC estimate using a PID control algorithm.

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