Driving style based brake energy recovery control system for pure electric vehicles

Abstract

The application belongs to the technical field of detection control, and provides a brake energy recovery control system of a pure electric vehicle based on driving style, which comprises the following steps: obtaining vehicle state data of a driver in a braking process by an ECU, and performing data analysis to obtain the braking style of the driver, wherein the braking style comprises sudden braking and slow braking; if the braking style of the driver is sudden braking, analyzing the fluctuation of the brake energy recovery rate in the historical braking process of the driver; if the battery of the electric vehicle is in a normal SOC interval, analyzing whether the fluctuation of the brake energy recovery rate will have a fluctuation impact on the recovered brake energy operation in a cliff type descending stage; if an impact is caused, adjusting the brake energy recovery rate in advance to avoid the fluctuation of the brake energy recovery rate when low SOC power is reached, and to avoid damage to the battery caused by the fluctuation of the brake energy recovery rate, so that the application is beneficial to reducing the problem of large fluctuation when the brake energy recovery rate suddenly drops, reducing the impact on the battery, and prolonging the service life of the battery.

Description

Technical Field

[0001] This invention belongs to the field of detection and control, specifically a braking energy recovery control system for pure electric vehicles based on driving style. Background Technology

[0002] In the control of regenerative braking in pure electric vehicles, regenerative braking technology is a core means to improve vehicle range and reduce energy consumption. Its core principle is to convert the kinetic energy generated during vehicle braking into electrical energy and store it in the power battery. It plays a key role in optimizing the energy utilization efficiency of the whole vehicle. However, at present, this technology still has many shortcomings in practical applications, which restrict its effectiveness and safety. Existing regenerative braking systems mostly adopt a uniform control strategy, which does not fully consider the differences in braking styles of different drivers. The uniform strategy cannot adapt to the operating habits of drivers with different styles, and may also affect vehicle stability and driving comfort due to the mismatch between the recovery force and driving action.

[0003] In existing technologies, relatively fixed control strategies are often adopted without fully considering the differences in drivers' braking styles. Existing regenerative braking control systems are difficult to adaptively adjust according to different drivers' braking styles. They also lack analysis of the fluctuations in regenerative braking rate during driving and correlation analysis at low battery state of charge (SOC). Furthermore, in regenerative braking control, there is no smoothing of large fluctuations in the abrupt drop in regenerative braking, which can cause impact on the battery.

[0004] Therefore, the present invention provides a braking energy recovery control system for pure electric vehicles based on driving style. Summary of the Invention

[0005] In order to overcome the shortcomings of the prior art, at least one technical problem raised in the background art is solved.

[0006] The technical solution adopted by this invention to solve its technical problem is: a pure electric vehicle braking energy recovery control system based on driving style, comprising:

[0007] Style determination module: Uses the ECU to acquire vehicle status data during braking and performs data analysis to determine the driver's braking style;

[0008] Among them, braking styles include emergency braking and gentle braking;

[0009] Filtering and extraction module: If the driver's braking style is emergency braking, analyze the fluctuation of the braking energy recovery rate during the driver's historical braking process;

[0010] Analysis and Judgment Module: If the electric vehicle battery is within the normal SOC range, analyze whether the fluctuation of the regenerative braking rate will have a fluctuating impact on the regenerative braking operation during the precipitous decline phase.

[0011] Early warning and adjustment module: If there is an impact, an early warning signal is generated to adjust the braking energy recovery rate in advance to avoid damage to the battery caused by fluctuations in the braking energy recovery rate when the battery reaches a low SOC.

[0012] Furthermore, the process of acquiring vehicle status data during braking is as follows:

[0013] The ECU is used to acquire state data of the driver during braking and the braking process.

[0014] By processing and analyzing the rate of change of brake pedal opening and closing, the rate of change of brake pressure, and braking time, the driver's braking style can be determined.

[0015] Furthermore, the process for determining the driver's braking style is as follows:

[0016] Obtain the rate of change of brake pedal opening and closing degree, the rate of change of brake pressure, and the braking time;

[0017] The opening and closing degree change rate, braking pressure change rate, and braking time are normalized to obtain the normalized values ​​of the opening and closing degree change rate, braking pressure change rate, and braking time. These values ​​are then calculated to obtain the braking style value. The braking style value for each braking action within the time period set by the driver is statistically analyzed, and the summation and average values ​​are obtained to obtain the average braking style value.

[0018] If the average braking style value is greater than or equal to the braking style threshold, the corresponding driver will be marked as having an emergency braking style.

[0019] Furthermore, the analysis of the fluctuations in the brake energy recovery rate during the driver's historical braking process is as follows:

[0020] The driver obtains the changes in the braking energy recovery rate during each driving process, and statistically analyzes the braking energy recovery rate under different battery charge levels. The SOC charge during driving is matched with the braking energy recovery rate to obtain the braking energy fluctuation group.

[0021] The energy recovery rates corresponding to the battery charge level within the normal range during driving are summarized and organized. The mean and standard deviation of the braking energy recovery rate within the normal range are calculated, and the volatility of the braking energy recovery rate within the normal range is judged by calculating the degree of dispersion.

[0022] Furthermore, the process of determining the volatility of the braking energy recovery rate within the normal range by calculating the degree of dispersion is as follows:

[0023] The dispersion analysis was performed by calculating the mean and standard deviation of the braking energy recovery rate and the coefficient of variation.

[0024] If the coefficient of variation is greater than or equal to the coefficient of variation threshold, it indicates that driver A's braking energy recovery rate fluctuates greatly during the driving process within the set time period.

[0025] Furthermore, the analysis determines whether the regenerative braking energy operation during the precipitous descent phase will experience fluctuations, as follows:

[0026] Obtain the brake energy recovery rate fluctuation when there is a precipitous drop in brake energy recovery under low battery power, and count the duration of brake energy recovery rate fluctuation for each precipitous drop.

[0027] The duration period is divided into several equal time periods. The braking energy recovery rate in each time period is statistically analyzed. The obtained braking energy recovery rates are summarized and integrated in chronological order to obtain the declining energy recovery group. The mean and standard deviation of the braking energy recovery rate are calculated, and the cliff-like coefficient of variation is calculated to analyze the fluctuation of braking energy recovery rate during the cliff-like decline.

[0028] If the brake energy recovery rate fluctuates greatly during a cliff-like descent, then analyze whether the large fluctuation in the brake energy recovery rate during a cliff-like descent is caused by the large fluctuation in the brake energy recovery rate.

[0029] Furthermore, the analysis of whether the large fluctuation in the regenerative braking rate during the abrupt descent is caused by the large fluctuation in the regenerative braking rate is as follows:

[0030] Using the coefficient of variation as the independent variable X and the cliff coefficient of variation as the dependent variable Y, the independent variable X and the dependent variable Y within a set time period are combined to obtain a variable group.

[0031] Sort the independent variable X in numerical order and assign it a rank;

[0032] Calculate the grade difference for each data set and perform the sum of squared grade differences to obtain the Spearman rank correlation coefficient. Based on the Spearman rank correlation coefficient, determine whether the fluctuation of the braking energy recovery rate will cause fluctuations when performing braking energy recovery rate operations during a precipitous decline phase.

[0033] Furthermore, if an impact exists, a warning signal is generated, as follows:

[0034] During driving, the ECU monitors the battery level and adjusts the regenerative braking when the battery level is low.

[0035] The ECU detects the braking energy recovery rate within the normal range and generates a warning signal when it identifies significant fluctuations in the braking energy recovery rate.

[0036] Furthermore, the process of adjusting the braking energy recovery rate is as follows:

[0037] When the ECU detects that the battery level is approaching low, it activates a warning mechanism and, based on historical data on the driver's braking energy recovery rate fluctuations under similar battery levels, sends adjustment commands to the motor controller via the CAN bus to reduce the current braking energy recovery intensity and dynamically adjust the braking energy recovery strategy.

[0038] Furthermore, the dynamic adjustment of the regenerative braking strategy is carried out as follows:

[0039] When the ECU detects that the SOC charge has dropped to the buffer point, it sends an adjustment command to the motor controller via the CAN bus to reduce the current regenerative braking intensity.

[0040] Obtain the buffer space, divide the buffer space into two equal parts to obtain the buffer sub-intervals, and mark them as the first buffer sub-interval and the second buffer sub-interval;

[0041] The braking energy recovery rate is adjusted by adopting a uniform degradation method;

[0042] The maximum value of the braking energy recovery rate in the normal range is obtained, and the difference between it and the maximum value of the braking energy recovery rate under low SOC is processed to obtain the braking energy recovery rate change amplitude value. The braking energy recovery rate change amplitude value is then evenly divided according to the number of downgrades to obtain the downgrade ratio value, which is used as the reduction amount of the first buffer sub-interval and the second buffer sub-interval.

[0043] The beneficial effects of this invention are as follows:

[0044] (1) The vehicle status data of the driver during the braking process is obtained by the ECU, and the data is analyzed. The driver's braking style value is calculated by normalization and compared with the braking style threshold. The emergency braking style is screened out, and the braking energy recovery rate of the driver's historical braking process is obtained. The fluctuation of the braking energy recovery rate is judged. By screening out the driver's braking style value, it is beneficial to accurately classify the driver's braking behavior, analyze the fluctuation of the braking energy recovery rate in the historical braking data, and adjust the braking energy recovery rate according to the driver's braking style.

[0045] (2) If the brake energy recovery fluctuation is large during emergency braking, analyze whether it will affect the brake energy recovery rate fluctuation during the cliff drop. The Spearman-level correlation coefficient is used to determine whether there is a correlation. If there is, the brake energy recovery rate is adjusted in advance. According to the correlation analysis, it is beneficial to adjust the fluctuation problem of the brake energy recovery rate during the cliff drop in advance, reduce the impact on the battery and extend the battery life. Attached Figure Description

[0046] The invention will now be further described with reference to the accompanying drawings.

[0047] Figure 1 This is a flowchart of the braking energy recovery control system for pure electric vehicles based on driving style, as described in an embodiment of the present invention.

[0048] Figure 2 This is a logic analysis diagram of the pure electric vehicle braking energy recovery control system based on driving style, as described in an embodiment of the present invention. Detailed Implementation

[0049] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0050] Example 1: Please refer to Figure 1 - Figure 2 As shown in the embodiment of the present invention, the braking energy recovery control system for pure electric vehicles based on driving style includes the following modules:

[0051] Style determination module: Uses the ECU to acquire vehicle status data during braking and performs data analysis to determine the driver's braking style;

[0052] In some instances, the electronic control unit (ECU) is used to obtain the driver's status data during the braking process when encountering a red light while driving in a 40 km / h speed limit zone, and the data is transmitted to a data processing library.

[0053] The transmitted data includes characteristic parameters such as the rate of change of brake pedal opening and closing, the rate of change of brake pressure, and braking time.

[0054] By processing and analyzing the rate of change of brake pedal opening and closing, the rate of change of brake pressure, and braking time, the driver's braking style can be determined.

[0055] Specifically, the braking style is determined based on characteristic parameters such as the rate of change of brake pedal opening and closing, the rate of change of brake pressure, and braking time.

[0056] Among them, the brake pedal opening and closing degree change rate represents the rate of change of the pedal depth per unit time, the brake pressure change rate represents the rate of increase of the brake system pressure, and the braking time represents the time from when the driver presses the pedal to when the vehicle comes to a complete stop.

[0057] Obtain the rate of change of brake pedal opening degree K= Braking pressure change rate Z= And braking time t;

[0058] The opening and closing degree change rate K, the braking pressure change rate Z, and the braking time t were normalized by Z-Score to obtain the normalized values ​​of K, Z, and t.

[0059] The formula is to add the normalized K value and the normalized Z value and then compare them with the normalized t value. The braking style value S is obtained. The braking style value is calculated for each braking action within the time period set by the driver. The average value is obtained by summing the values ​​and comparing it with the braking style threshold to determine the driver's braking style.

[0060] It should be noted that the time period can be set according to the number of times the driver needs to brake and achieve braking effect when encountering a red light within the time period, such as once a week or once a month.

[0061] If the average braking style value is greater than or equal to the braking style threshold, the corresponding driver will be marked as having an emergency braking style.

[0062] If the average braking style value is less than the braking style threshold, the corresponding driver will be marked as having a gentle braking style.

[0063] It should be noted that the purpose of obtaining braking style is to achieve dynamic adaptation and precise optimization of braking energy recovery strategy. By identifying the driver's emergency braking or gentle braking style, it provides personalized decision-making basis for subsequent energy recovery control.

[0064] In addition, it avoids the problem of insufficient energy recovery or poor driving experience in some scenarios due to the inability to adjust the recovery intensity according to the differences in driver operating habits. By introducing driving style as a key variable into the control system, the energy recovery strategy can be more in line with actual driving behavior, reducing the abruptness caused by the mismatch between recovery intensity and driving intention, while improving battery energy utilization.

[0065] The acquisition of driving style laid the data foundation for the volatility analysis and strategy optimization of braking energy recovery rate. It also provided the data foundation for subsequent analysis of the correlation between the cliff-like drop in volatility and driving style through Spearman's rank correlation coefficient. The driving style data also became the core basis for judging the cause of volatility and formulating targeted adjustment strategies.

[0066] Filtering and extraction module: If the driver's braking style is emergency braking, obtain the fluctuation of braking energy recovery rate during the driver's historical braking process;

[0067] In the driver's historical braking data, retrieve the records of brake energy recovery rate under all emergency braking styles to obtain brake recovery rate data, and perform statistical analysis on the brake recovery rate data.

[0068] The driver obtains the changes in the braking energy recovery rate during each driving process, and statistically analyzes the braking energy recovery rate under different battery charge levels. The SOC charge during driving is matched with the braking energy recovery rate to obtain the braking energy fluctuation group.

[0069] The energy recovery rate corresponding to the SOC in the normal range during driving is summarized and organized. The mean and standard deviation of the braking energy recovery rate in the normal range are calculated. The volatility of the braking energy recovery rate in the normal range is judged by calculating the degree of dispersion.

[0070] It should be noted that the normal range means that the SOC battery level is between 20% and 80%.

[0071] Calculate the mean μ and standard deviation σ of the braking energy recovery rate within a set time period, and calculate the coefficient of variation CV. Perform dispersion analysis;

[0072] Compare the calculated coefficient of variation with the coefficient of variation threshold;

[0073] If the coefficient of variation is greater than or equal to the coefficient of variation threshold, it indicates that driver A's braking energy recovery rate fluctuates greatly during the driving process within the set time period.

[0074] If the coefficient of variation is less than the coefficient of variation threshold, it indicates that driver A's braking energy recovery rate fluctuates little during the driving process within the set time period.

[0075] It should be noted that the purpose of screening the driver's brake energy recovery rate fluctuation is to accurately identify the stability characteristics of energy recovery efficiency under different driving styles by quantitatively analyzing the dispersion of brake energy recovery rate within the normal SOC range (20%~80%). This provides data support for the subsequent dynamic adjustment of the recovery strategy by the system. It can extract the energy recovery fluctuation pattern of the driver in the typical charge range from historical driving data, avoid the problem of fluctuating recovery efficiency due to individual driving habits, and thus lay the foundation for brake energy recovery management and control.

[0076] From a problem-solving perspective, screening for volatility can effectively distinguish between stable and volatile recovery characteristics: for drivers with high volatility, the system can predict in advance the risk of a sharp drop in energy recovery efficiency that they may face in the low SOC range (below 20%). At the same time, by quantifying the degree of volatility through indicators such as the coefficient of variation, it can eliminate the interference of occasional data and ensure the accuracy of subsequent analysis of the correlation judgment of a cliff-like drop in volatility.

[0077] Example 2: Please refer to Figure 1 - Figure 2 As shown in the embodiment of the present invention, the braking energy recovery control system for pure electric vehicles based on driving style includes:

[0078] Analysis and Judgment Module: If the electric vehicle battery is within the normal SOC range, analyze whether the fluctuation of the regenerative braking rate will have a fluctuating impact on the regenerative braking operation during the precipitous decline phase.

[0079] It should be noted that a precipitous drop indicates a significant decrease in the regenerative braking rate from the normal range to when the battery is at a low state of charge (SOC).

[0080] The fluctuation of regenerative braking rate when there is a sharp drop in regenerative braking rate under low SOC is obtained, and the duration of the regenerative braking rate fluctuation when each sharp drop occurs is counted as the regenerative braking fluctuation period. Here, low SOC means that the battery charge is less than or equal to 20%.

[0081] The energy recovery cycle is divided into several equal time periods. The braking energy recovery rate in each time period is calculated. The obtained braking energy recovery rates are summarized and integrated in chronological order to obtain the declining energy recovery group.

[0082] The study statistically analyzed all the energy recovery groups of the cliff-like descent during a set time period for driver A, calculated the mean and standard deviation of the braking energy recovery rate, and performed the coefficient of variation (CV) calculation for the time period to obtain the cliff-like coefficient of variation. The results were then compared with the cliff-like coefficient of variation threshold to analyze the fluctuation of the braking energy recovery rate during the cliff-like descent.

[0083] If the cliff-like coefficient of variation is greater than or equal to the cliff-like coefficient of variation threshold, it indicates that the braking energy recovery rate fluctuates greatly during cliff-like descent. Then, we need to analyze whether the large fluctuation of the braking energy recovery rate during cliff-like descent is caused by the large fluctuation of the braking energy recovery rate.

[0084] If the cliff-like coefficient of variation is less than the cliff-like coefficient of variation threshold, it indicates that the braking energy recovery rate fluctuates little during the cliff-like descent, and no action is taken.

[0085] Obtain all coefficients of variation and cliff-like coefficients of variation for driver A within a set time period, and determine whether there is a correlation by calculating the Spearman rank correlation coefficient.

[0086] Using the coefficient of variation as the independent variable X and the cliff coefficient of variation as the dependent variable Y, the independent variable X and the dependent variable Y within a set time period are combined to obtain a variable group.

[0087] It should be noted that when combining variables, one independent variable X corresponds to only one dependent variable Y;

[0088] Sort the independent variable X in numerical order and assign it a level to obtain the corresponding descending order group. For example, the level of the maximum value of the independent variable X is n, and the level of the minimum value of the independent variable X is 1.

[0089] Calculate the rank difference d for each group within the descending order of data, and then perform the sum of squared rank differences. calculate;

[0090] Sum of squared grade differences Substitute into the formula , The Spearman rank correlation coefficient was calculated to obtain the correlation coefficient value, where n is the sample size;

[0091] If the correlation coefficient value is greater than or equal to the correlation coefficient threshold, it indicates that there is a correlation between the large fluctuation of the braking energy recovery rate during the cliff-like drop and the large fluctuation of the braking energy recovery rate. The large fluctuation of the braking energy recovery rate within the normal range will affect the fluctuation of the braking energy recovery rate during the cliff-like drop.

[0092] If the correlation coefficient value is less than the correlation coefficient threshold, it means that there is no correlation between the large fluctuation of the braking energy recovery rate during the cliff-like drop and the large fluctuation of the braking energy recovery rate. The large fluctuation of the braking energy recovery rate within the normal range will not affect the fluctuation that exists when the braking energy recovery rate drops cliff-like.

[0093] It should be noted that the correlation coefficient threshold was derived from the analysis of multiple experimental data and actual driving scenarios. This threshold can effectively distinguish between strong and weak correlations, providing a reliable basis for judging the cause of volatility.

[0094] It should also be noted that the purpose of determining whether the large fluctuations in the regenerative braking rate will affect the volatility that exists when the regenerative braking rate drops sharply is to provide a key basis for adjusting the regenerative braking strategy in the future. If there is a correlation between the two, it means that when the regenerative braking rate itself fluctuates greatly, the risk of a sharp drop will increase. The system will need to take more robust energy recovery strategies in advance, such as appropriately reducing the energy recovery intensity to avoid a sharp drop caused by excessive recovery and to ensure the stability of the braking process.

[0095] If there is no correlation, the system can focus on monitoring and adjusting other factors that may cause a sharp drop in braking energy recovery rate while paying attention to the overall fluctuation of braking energy recovery rate, such as battery status and vehicle load, in order to achieve more precise and efficient braking energy recovery control and further improve the energy utilization efficiency and range of pure electric vehicles.

[0096] Early warning and adjustment module: If there is an impact, an early warning signal is generated to adjust the braking energy recovery rate in advance to avoid damage to the battery caused by fluctuations in the braking energy recovery rate when the battery reaches a low SOC level.

[0097] During driving, the ECU detects the SOC (State of Charge) of the vehicle and adjusts the regenerative braking when the SOC is close to low.

[0098] Specifically, the ECU detects the braking energy recovery rate within the normal range and generates a warning signal when it identifies significant fluctuations in the braking energy recovery rate.

[0099] Based on the warning signal, when the ECU detects that the SOC is close to the buffer point, the warning mechanism is activated, and the braking energy recovery strategy is dynamically adjusted according to the driver's braking energy recovery rate fluctuation at similar SOC levels in historical data.

[0100] It should be noted that the early warning mechanism refers to a series of preventive control measures that are proactively triggered when a risk that may cause a precipitous drop in the braking energy recovery rate is detected.

[0101] Specifically, when the ECU detects that the SOC power has dropped to the buffer point A, it sends an adjustment command to the motor controller via the CAN bus to reduce the current braking energy recovery intensity.

[0102] The calculation method for buffer point A is as follows: Among them, SOC safety refers to the low SOC threshold, which can be 20%;

[0103] It should be noted that CAN bus refers to Controller Area Network, which is the core communication protocol and data transmission network of automotive electronics. Its function is to connect components such as ECU, motor controller, and battery management system, and to transmit data such as SOC and control commands.

[0104] The motor controller refers to the core electronic unit that controls the vehicle's drive motor. Its function is to switch between motor drive and regeneration modes and to precisely adjust the regenerative braking rate according to ECU instructions (such as when the SOC decreases).

[0105] If historical data shows that the driver's regenerative braking rate fluctuates significantly when the battery is at low SOC, the ECU can be used to adjust the intensity of regenerative braking to reduce the large fluctuations in regenerative braking when the battery is low, thereby reducing the impact on the battery.

[0106] Obtain the buffer interval, divide the buffer interval into two equal parts to obtain the buffer sub-intervals, and mark them as the first buffer sub-interval and the second buffer sub-interval. The buffer interval is the range from the SOC power to the low SOC power corresponding to the buffer point.

[0107] Based on the buffer sub-interval, the braking energy recovery rate within the buffer sub-interval is adjusted using a uniform degradation method;

[0108] The maximum value of the braking energy recovery rate in the normal range is obtained, and the difference between it and the maximum value of the braking energy recovery rate under low SOC is processed to obtain the braking energy recovery rate change range value. The braking energy recovery rate change range value is then evenly divided according to the number of downgrades to obtain the downgrade ratio value. For example, if the maximum fluctuation when the braking energy recovery rate drops sharply is 15% and the number of downgrades is three, then the downgrade ratio value can be set to 5%.

[0109] When the SOC (State of Charge) level is detected to reach the first buffer sub-range, the braking energy recovery rate is reduced by a reduction value, which is the downgrade percentage. Similarly, when the SOC level is detected to reach the second buffer sub-range, the braking energy recovery rate is reduced by a reduction value, which is the downgrade percentage. When the SOC level is detected to reach the low charge range, the braking energy recovery rate is reduced by a reduction value, which is the downgrade percentage.

[0110] For example, when the calculated buffer point is 26%, the first buffer sub-interval corresponds to a primary response when the SOC battery level is between 26% and 23%, the second buffer sub-interval corresponds to a medium response when the SOC battery level is between 23% and 20%, and a high response when the SOC battery level is below 20%.

[0111] The primary response is triggered when the SOC drops to 26%, reducing the regenerative braking rate by 5%. The intermediate response is triggered when the SOC drops to 23%, further reducing the regenerative braking rate by 5%. The advanced response is triggered when the SOC drops to 20%, reducing the regenerative braking rate to a preset standard level.

[0112] Specifically, the braking energy recovery rate, which was originally higher when the SOC (State of Charge) was 26%, will be adjusted to a medium level, such as from 15%–30% to 15%–25%. Furthermore, when the SOC drops to 23%, the braking energy recovery rate will be reduced to 10%–20%, and when the SOC drops to 20%, the braking energy recovery rate will be reduced to 5%–15%.

[0113] By reducing the regenerative braking rate in stages, the impact on the battery can be avoided by the sudden drop and large fluctuation of braking energy when the SOC reaches a low level.

[0114] It should be noted that the purpose of adjusting the regenerative braking rate in advance is to ensure that the battery is protected from potential damage caused by drastic fluctuations in the regenerative braking rate when the battery is at a low SOC state. When the system detects that the SOC is close to the buffer point, the adjustment mechanism is immediately activated. Based on the regenerative braking rate fluctuation pattern in the driver's historical data, the regenerative braking intensity is accurately calculated and the gradient adjustment is implemented. Through graded and gradual adjustment of the regenerative braking intensity, the problem of large fluctuations in the regenerative braking rate that cause a cliff drop can be effectively reduced. This avoids the risk of overheating and overcharging of the battery due to instantaneous high-power charging and discharging, thereby extending the battery life and improving the stability and reliability of the vehicle's energy management.

[0115] Working principle of the invention:

[0116] By acquiring vehicle status data from the driver during braking through the ECU, data analysis is performed. Through normalization, the driver's braking style value is calculated and compared with a braking style threshold to filter out emergency braking styles. The brake energy recovery rate (RER) during the driver's historical braking processes is also obtained to assess its volatility. Filtering the driver's braking style value facilitates accurate classification of the driver's braking behavior, analyzes the volatility of RER in historical braking data, and adjusts the RER based on the driver's braking style. If the RER volatility is high during emergency braking, it is analyzed whether this will affect the RER volatility during a sharp drop. The Spearman-level correlation coefficient is calculated to determine if a correlation exists. If one exists, the RER is adjusted in advance. This correlation analysis helps to address the volatility issues during a sharp drop in RER, reducing the impact on the battery.

[0117] The foregoing has shown and described 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 embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A braking energy recovery control system for pure electric vehicles based on driving style, characterized in that: Includes the following modules: Style determination module: Uses the ECU to acquire vehicle status data during braking and performs data analysis to determine the driver's braking style; Among them, braking styles include emergency braking and gentle braking; Filtering and extraction module: If the driver's braking style is emergency braking, analyze the fluctuation of the braking energy recovery rate during the driver's historical braking process; Analysis and Judgment Module: If the electric vehicle battery is within the normal SOC range, analyze whether the fluctuation of the regenerative braking rate will have a fluctuating impact on the regenerative braking operation during the precipitous decline phase. Obtain the brake energy recovery rate fluctuation when there is a precipitous drop in brake energy recovery under low battery power, and count the duration of brake energy recovery rate fluctuation for each precipitous drop. The duration period is divided into several equal time periods. The braking energy recovery rate in each time period is statistically analyzed. The obtained braking energy recovery rates are summarized and integrated in chronological order to obtain the declining energy recovery group. The mean and standard deviation of the braking energy recovery rate are calculated, and the cliff-like coefficient of variation is calculated to analyze the fluctuation of braking energy recovery rate during the cliff-like decline. If the brake energy recovery rate fluctuates greatly during a cliff-like descent, analyze whether the large fluctuation in the brake energy recovery rate during a cliff-like descent is caused by the large fluctuation in the brake energy recovery rate. Using the coefficient of variation as the independent variable X and the cliff coefficient of variation as the dependent variable Y, the independent variable X and the dependent variable Y within a set time period are combined to obtain a variable group. Sort the independent variable X in numerical order and assign it a rank; Calculate the grade difference for each group of data and perform the sum of squares of the grade differences to obtain the Spearman grade correlation coefficient. Based on the Spearman grade correlation coefficient, determine whether the fluctuation of the braking energy recovery rate will cause fluctuations when performing braking energy recovery rate operation during the precipitous decline phase. Early warning and adjustment module: If there is an impact, an early warning signal is generated to adjust the braking energy recovery rate in advance to avoid damage to the battery caused by fluctuations in the braking energy recovery rate when the battery reaches a low SOC.

2. The braking energy recovery control system for pure electric vehicles based on driving style according to claim 1, characterized in that: The process of acquiring vehicle status data during braking is as follows: The ECU is used to acquire state data of the driver during braking and the braking process. By processing and analyzing the rate of change of brake pedal opening and closing, the rate of change of brake pressure, and braking time, the driver's braking style can be determined.

3. The braking energy recovery control system for pure electric vehicles based on driving style according to claim 2, characterized in that: The process for determining the driver's braking style is as follows: Obtain the rate of change of brake pedal opening and closing degree, the rate of change of brake pressure, and the braking time; The opening and closing degree change rate, braking pressure change rate, and braking time are normalized to obtain the normalized values ​​of the opening and closing degree change rate, braking pressure change rate, and braking time. These values ​​are then calculated to obtain the braking style value. The braking style value for each braking action within the time period set by the driver is statistically analyzed, and the summation and average values ​​are obtained to obtain the average braking style value. If the average braking style value is greater than or equal to the braking style threshold, the corresponding driver will be marked as having an emergency braking style.

4. The braking energy recovery control system for pure electric vehicles based on driving style according to claim 1, characterized in that: The analysis of the fluctuations in the brake energy recovery rate during the driver's historical braking process is as follows: The driver obtains the changes in the braking energy recovery rate during each driving process, and statistically analyzes the braking energy recovery rate under different battery charge levels. The SOC charge during driving is matched with the braking energy recovery rate to obtain the braking energy fluctuation group. The energy recovery rates corresponding to the battery charge level within the normal range during driving are summarized and organized. The mean and standard deviation of the braking energy recovery rate within the normal range are calculated, and the volatility of the braking energy recovery rate within the normal range is judged by calculating the degree of dispersion.

5. The braking energy recovery control system for pure electric vehicles based on driving style according to claim 4, characterized in that: The process of determining the volatility of braking energy recovery rate within the normal range by calculating the degree of dispersion is as follows: The dispersion analysis was performed by calculating the mean and standard deviation of the braking energy recovery rate and the coefficient of variation. If the coefficient of variation is greater than or equal to the coefficient of variation threshold, it indicates that driver A's braking energy recovery rate fluctuates greatly during the driving process within the set time period.

6. The braking energy recovery control system for pure electric vehicles based on driving style according to claim 1, characterized in that: If an impact exists, an early warning signal will be generated, as follows: During driving, the ECU monitors the battery level and adjusts the regenerative braking when the battery level is low. The ECU detects the braking energy recovery rate within the normal range and generates a warning signal when it identifies significant fluctuations in the braking energy recovery rate.

7. The braking energy recovery control system for pure electric vehicles based on driving style according to claim 1, characterized in that: The process of adjusting the braking energy recovery rate is as follows: When the ECU detects that the battery level is approaching low, it activates a warning mechanism and, based on historical data on the driver's braking energy recovery rate fluctuations under similar battery levels, sends adjustment commands to the motor controller via the CAN bus to reduce the current braking energy recovery intensity and dynamically adjust the braking energy recovery strategy.

8. The braking energy recovery control system for pure electric vehicles based on driving style according to claim 7, characterized in that: The dynamic adjustment strategy for regenerative braking is as follows: When the ECU detects that the SOC charge has dropped to the buffer point, it sends an adjustment command to the motor controller via the CAN bus to reduce the current regenerative braking intensity. Obtain the buffer space, divide the buffer space into two equal parts to obtain the buffer sub-intervals, and mark them as the first buffer sub-interval and the second buffer sub-interval; The braking energy recovery rate is adjusted by adopting a uniform degradation method; The maximum value of the braking energy recovery rate in the normal range is obtained, and the difference between it and the maximum value of the braking energy recovery rate under low SOC is processed to obtain the braking energy recovery rate change amplitude value. The braking energy recovery rate change amplitude value is then evenly divided according to the number of downgrades to obtain the downgrade ratio value, which is used as the reduction amount of the first buffer sub-interval and the second buffer sub-interval.

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