A tram adaptive energy recovery method and system integrating driver behavior

Abstract

The present application relates to an adaptive energy recovery method and system for electric vehicles that integrates driver behavior. The method includes the following steps: analyzing the vehicle's current and historical driving data to determine the driving style and predict the probability of the driving style occurring; estimating the vehicle's mass based on the vehicle's current driving data, and calculating two slope values ​​using two different algorithms; setting a weight based on a preset vehicle speed threshold, and weighting the two values ​​to obtain a fused slope; and determining corresponding correction factors based on the vehicle's current driving data, vehicle mass, fused slope, and the probability of the driving style occurring, correcting the initial braking torque corresponding to the current motor speed to obtain a baseline braking torque, comparing the baseline braking torque with a preset motor torque range, obtaining the optimal braking torque, and executing braking to achieve energy recovery. The present application effectively optimizes the distribution of regenerative braking energy and improves the energy recovery rate.

Description

Technical Field

[0001] The present application relates to the field of vehicle energy recovery technology, and in particular to an electric vehicle adaptive energy recovery method and system integrating driver behavior. Background Art

[0002] Traditional vehicle energy recovery technology mainly relies on electric motor braking to achieve energy recovery, and most of them adopt a relatively single control strategy. They fail to fully consider the various complex factors in the vehicle driving process, such as vehicle driving data, road conditions, etc., resulting in low energy recovery efficiency.

[0003] To this end, some energy recovery technologies incorporate correction coefficients for factors such as torque, speed, wheel angular velocity, slope, and vehicle mass. These coefficients, along with two sets of torque maps, are interpolated to determine the optimal regenerative braking torque. While this approach takes into account multiple factors during vehicle operation and improves energy recovery efficiency to a certain extent, it still presents some issues. First, vehicle mass and slope are measured using sensors. When sensors are inaccurate, respond in a delayed manner, or are interfered with by external factors (such as inclement weather or extreme temperatures), the measured data may be inaccurate. Second, the correction coefficients considered are limited to basic factors such as speed, slope, and vehicle mass, making them less adaptable to the driver's driving habits. Summary of the Invention

[0004] In response to the above-mentioned problems existing in current technology, this application provides an electric vehicle adaptive energy recovery method and system that integrates driver behavior to solve the technical problems of inaccurate energy recovery technical parameters and poor adaptability of drivers' driving habits.

[0005] To achieve the above objectives, in a first aspect, the present application provides an electric vehicle adaptive energy recovery method integrating driver behavior, the method comprising:

[0006] Analyze the vehicle's current and historical driving data to determine the driving style and predict the probability of the driving style occurring.

[0007] Based on the vehicle's current driving data, the vehicle mass is estimated, and two values ​​of the slope are calculated using two different algorithms.

[0008] The weight is set by a preset vehicle speed threshold, and the fused slope is obtained by weighting the two values.

[0009] Based on the vehicle's current driving data, vehicle mass, fusion slope, and the probability of the driving style occurring, the corresponding correction factors are determined. The initial braking torque corresponding to the current motor speed is corrected to obtain a reference braking torque. The reference braking torque is compared with the preset motor torque range to obtain the optimal braking torque and perform braking to achieve energy recovery.

[0010] Furthermore, in one embodiment, analyzing the current driving data and historical driving data of the vehicle, determining the driving style and predicting the probability of the driving style occurring includes:

[0011] Perform preprocessing operations on driving data, including filtering and normalization.

[0012] The support vector machine algorithm is used to analyze current and historical driving data to determine the driver's corresponding driving style, which includes aggressive, stable, and economical driving styles, and predict the probability of the driver's corresponding driving style appearing among all driving styles.

[0013] Furthermore, in one embodiment, the vehicle mass is estimated based on the current driving data of the vehicle, and two values ​​of the slope are calculated using two different algorithms, including:

[0014] According to the current driving data of the vehicle, the vehicle mass is estimated by the least square estimation method, and the two values ​​of the slope are calculated respectively by Kalman filtering and the least square estimation method.

[0015] Furthermore, in one embodiment, a weight is set by a preset vehicle speed threshold, and the fused slope is obtained by weighting the two values, including:

[0016] The vehicle speed threshold is determined according to calibration.

[0017] The weighting coefficient is determined by the critical value of vehicle speed, and the two values ​​of the slope are weighted to obtain the fused slope.

[0018] Furthermore, in one embodiment, the combination of the vehicle's current driving data, vehicle mass, fused slope, and probability of occurrence of the driving style to determine the corresponding correction factors includes:

[0019] The pre-set correction factor table is queried to obtain the correction factors corresponding to the required current vehicle speed, pedal opening, vehicle mass, fusion slope and probability of occurrence of the driving style.

[0020] The correction factor table includes: real-time vehicle speed, pedal opening, vehicle mass, integrated slope, and probability of occurrence of driving style, as well as correction factors corresponding to each probability.

[0021] Furthermore, in one embodiment, the correcting the initial braking torque corresponding to the current motor speed to obtain the reference braking torque includes:

[0022] The initial braking torque is obtained from the current motor speed.

[0023] Multiply the initial braking torque by the required correction factor to obtain the reference braking torque.

[0024] Furthermore, in one embodiment, comparing the reference braking torque with a preset motor torque range to obtain the optimal braking torque includes:

[0025] If the reference braking torque is within the preset motor torque range, the reference braking torque is taken as the optimal braking torque. If the reference braking torque is less than the minimum motor torque, the minimum motor torque is taken as the optimal braking torque. If the reference braking torque is greater than the maximum motor torque, the maximum motor torque is taken as the optimal braking torque.

[0026] Furthermore, in one embodiment, before performing braking, the method further includes: limiting the output of the torque signal by controlling the slope change of the braking torque and using filtering technology.

[0027] Furthermore, in one embodiment, the vehicle's driving data includes: vehicle speed, acceleration, brake pedal opening, vehicle posture information, and battery remaining power.

[0028] In a second aspect, based on the above-mentioned electric vehicle adaptive energy recovery method integrating driver behavior, the present application provides an energy recovery system for the electric vehicle adaptive energy recovery method integrating driver behavior, the energy recovery system comprising:

[0029] The analysis module is used to analyze the current driving data and historical driving data of the vehicle, determine the driving style and predict the probability of the driving style occurring.

[0030] The estimation module is used to estimate the vehicle mass based on the vehicle's current driving data and calculate two values ​​of the slope using two different algorithms.

[0031] The weighting module is used to set a weight based on a preset vehicle speed threshold value, and obtain a fused slope by weighting the two values.

[0032] The energy recovery module is used to determine the corresponding correction factors based on the vehicle's current driving data, vehicle mass, integrated slope, and the probability of the driving style. The initial braking torque corresponding to the current motor speed is corrected to obtain a reference braking torque. The reference braking torque is compared with the preset motor torque range to obtain the optimal braking torque and perform braking to achieve energy recovery.

[0033] The beneficial effects of the technical solutions provided in the embodiments of the present application include:

[0034] This application analyzes the vehicle's current driving data and historical driving data to determine the driving style and predict the probability of the driving style occurring. Based on the driving style and its probability of occurrence, the braking response is adjusted to match the individual's driving habits, thereby improving the adaptability of the energy recovery technology to the driver's driving habits.

[0035] Based on the vehicle's current driving data, the vehicle's mass is estimated, and two slope values ​​are calculated using two different algorithms. The weights are set by a preset vehicle speed threshold, and the two values ​​are weighted to obtain a fused slope. This reduces reliance on sensors and improves data accuracy and the reliability of the energy recovery system.

[0036] Based on the vehicle's current driving data, vehicle mass, fusion slope, and the probability of the driving style occurring, the corresponding correction factors are determined. The initial braking torque corresponding to the current motor speed is corrected to obtain the reference braking torque. The reference braking torque is compared with the preset motor torque range to obtain the optimal braking torque and perform braking to achieve energy recovery, effectively optimizing the distribution of regenerative braking energy and increasing the energy recovery rate by 5%-10%. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 This is a flow chart of an electric vehicle adaptive energy recovery method integrating driver behavior according to an embodiment of the present application.

[0038] Figure 2 This is a schematic diagram of the optimal braking torque determination process according to an embodiment of the present application.

[0039] Figure 3 This is a block diagram of an energy recovery system that integrates driver behavior in an embodiment of the present application. DETAILED DESCRIPTION

[0040] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0041] In order to make the objectives, technical solutions and advantages of this application clearer, the implementation methods of this application will be further described in detail below with reference to the accompanying drawings.

[0042] In a first aspect, an embodiment of the present application provides an electric vehicle adaptive energy recovery method that integrates driver behavior.

[0043] In one embodiment, see Figure 1 As shown, the above method includes:

[0044] S1. Analyze the current driving data and historical driving data of the vehicle, determine the driving style and predict the probability of the driving style occurring.

[0045] S2. Estimate the vehicle mass based on the vehicle's current driving data, and calculate two values ​​of the slope using two different algorithms.

[0046] S3. Setting a weight based on a preset vehicle speed threshold value, and obtaining a fusion slope by weighting the two values.

[0047] S4. Based on the vehicle's current driving data, vehicle mass, integrated slope, and the probability of the driving style occurring, the corresponding correction factors are determined. The initial braking torque corresponding to the current motor speed is corrected to obtain a reference braking torque. The reference braking torque is compared with a preset motor torque range to obtain the optimal braking torque and perform braking to achieve energy recovery.

[0048] The present embodiment analyzes the vehicle's current and historical driving data to determine driving style and predict the probability of that style occurring. Based on the driving style and its probability of occurrence, the braking response is adjusted to match the driver's individual driving habits, improving the adaptability of energy recovery technology to the driver's driving habits. The vehicle's current driving data is used to estimate the vehicle's mass, and two different algorithms are used to calculate two slope values. These two values ​​are weighted by a preset vehicle speed threshold to produce a fused slope. This reduces reliance on sensors and improves data accuracy and the reliability of the energy recovery system.

[0049] Furthermore, in one embodiment, the above step S1 includes:

[0050] S101 : Preprocessing the driving data, including filtering and normalization.

[0051] Among them, driving data can be collected in advance through various types of sensors, such as vehicle speed sensor to collect vehicle speed, acceleration sensor to collect acceleration, pedal opening sensor to collect pedal opening, gyroscope to collect vehicle posture information, SOC (System on Chip) sensor to collect battery remaining power, etc.

[0052] S102: Analyze current and historical driving data using a support vector machine algorithm, such as accelerator and brake usage frequency, steering range, driving speed, acceleration rate, etc., to determine the driver's driving style. Driving styles include aggressive, conservative, and economical. The probability of the driver's driving style occurring among all driving styles is predicted, and the driving style-related data is transmitted to the brake energy recovery module.

[0053] The driving data of the above-mentioned vehicle (including current driving data and historical driving data) includes: vehicle speed, acceleration, brake pedal opening, vehicle posture information and battery remaining power; other data can also be added according to actual conditions.

[0054] In this embodiment, by preprocessing the driving data, noise and erroneous signals can be removed, ensuring the accuracy and availability of the data, and enabling different types of data to be effectively integrated in subsequent analysis. By judging the driver's driving style and predicting the probability of the driver's corresponding driving style appearing among all driving styles, the adaptability of the energy recovery technology to the driver's driving habits is improved, providing a more comfortable and personalized driving experience.

[0055] Furthermore, in one embodiment, the driving data collection and preparation in the above-mentioned step S1 mainly collects driving data such as speed, acceleration, braking intensity, steering angle, etc. through various sensors of the vehicle. The driving data is cleaned, normalized and balanced sampled, and can provide high-quality input for subsequent processes.

[0056] The above step S1 can be achieved through feature engineering and support vector machine training: extract key features from driving data, such as average speed and acceleration rate, train support vector machine using radial basis kernel, and select appropriate Value and The model is optimized through cross-validation to train a classifier that can accurately predict the driver's driving style. The driver's driving style is accurately predicted by analyzing the vehicle's current and historical driving data. The output of the support vector machine algorithm is set as a probability output, that is, the probability that the driver's corresponding driving style will appear among all driving styles.

[0057] Furthermore, in one embodiment, the above step S2 includes:

[0058] S201. Based on the real-time information collected by the gyroscope and accelerometer in the vehicle's current driving data, key signals such as X-axis acceleration, Y-axis acceleration, Z-axis angular acceleration, vehicle speed, pedal opening, and vehicle suspension system (such as suspension travel, shock absorber pressure, etc.) are obtained, and the signals are filtered.

[0059] S202: Estimate the vehicle mass by using the least squares estimation method.

[0060] S203. Calculate two values ​​of the slope respectively by using Kalman filtering and least squares estimation method.

[0061] Furthermore, in one embodiment, in the above step S3, first, a vehicle speed threshold is determined according to calibration; then, a weighting coefficient is determined based on the vehicle speed threshold, and the two values ​​of the slope are weighted to obtain a fused slope. Specifically:

[0062] Set vehicle speed threshold , when the current vehicle speed is collected > When , the calculation formula of the fusion slope is:

[0063] Blending Slope = ,

[0064] in, is the slope value calculated by Kalman filtering, is the slope value calculated by the least squares estimation method, is the weighting coefficient, and its value is the maximum value of the slope fusion dynamics weighting coefficient.

[0065] When the current vehicle speed is collected ≤ When , the calculation formula of the fusion slope is:

[0066] Blending Slope = ,

[0067] in, is the weighting coefficient, and The value of is given by the formula and formula Calculated.

[0068] In this embodiment, algorithm-based slope and mass estimation reduces reliance on sensors, improves data processing accuracy and efficiency, and enables speed ramp-up control when descending long slopes, maintaining the vehicle speed within a preset safety range. This significantly enhances the driver's sense of security and confidence in complex road conditions while also ensuring smooth and comfortable driving. While ensuring safety, brake energy regeneration is fully utilized to efficiently convert the vehicle's kinetic energy generated by deceleration into stored electrical energy, improving energy efficiency.

[0069] Furthermore, in one embodiment, the above step S4 combines the current driving data of the vehicle, the vehicle mass, the integrated slope, and the probability of the driving style to determine the corresponding correction factors, including:

[0070] S401. Receive key control and status data such as accelerator pedal and brake pedal opening, vehicle speed, mass gradient, SOC, motor speed, motor torque, and the probability of occurrence of a driving style from the vehicle's current driving data.

[0071] S402: Query a pre-set correction factor table to obtain the correction factors corresponding to the current vehicle speed, pedal opening, vehicle mass, integrated slope, and driving style probability. The correction factor table includes the real-time vehicle speed, pedal opening, vehicle mass, integrated slope, and driving style probability, along with the correction factors corresponding to each probability.

[0072] Furthermore, in one embodiment, in step S4, correcting the initial braking torque corresponding to the current motor speed to obtain the reference braking torque includes:

[0073] S403 , obtaining an initial braking torque from the current motor speed by querying a preset table of motor speeds and target braking torques.

[0074] S404: Multiply the initial braking torque by a required correction factor (i.e., a correction factor corresponding to the current vehicle speed, pedal opening, vehicle mass, integrated slope, and probability of occurrence of the driving style) to obtain a reference braking torque.

[0075] In this embodiment, the initial braking torque is adjusted by a correction factor to obtain a reference braking torque to adapt to current driving conditions and road conditions.

[0076] Further, in one embodiment, see Figure 2 As shown, in the above step S4, the reference braking torque is compared with the preset motor torque range to obtain the optimal braking torque, including:

[0077] S405: Preset the motor torque range.

[0078] S406. Compare the reference braking torque with the preset motor torque range. If the reference braking torque is within the preset motor torque range (including the end value of the range), proceed to S407; if the reference braking torque is less than the minimum motor torque, proceed to S408; if the reference braking torque is greater than the maximum motor torque, proceed to S409.

[0079] S407: Take the reference braking torque as the optimal braking torque.

[0080] S408: Take the minimum motor torque as the optimal braking torque.

[0081] S409: Take the maximum value of the motor torque as the optimal braking torque.

[0082] Furthermore, in one embodiment, before performing braking, in step S4, the process further includes: limiting the output of the torque signal by controlling the slope of the braking torque and using filtering techniques. This, to a certain extent, avoids shock and abnormal noise during braking and improves the smoothness of the braking transition.

[0083] In addition, each correction factor value included in the correction factor table has a preset range, which is determined by calibration. Here, an embodiment of a calibration strategy for the preset ranges of each correction factor in the correction factor table is provided, including the vehicle speed correction factor, brake pedal opening correction factor, vehicle mass correction factor, slope correction factor, and driving style correction factor. Specifically:

[0084] Vehicle speed correction factor: When the vehicle speed is low, the vehicle speed correction factor is small and the energy recovery efficiency is low; when driving at high speed, kinetic energy increases and more energy can be recovered during braking.

[0085] Brake pedal opening correction factor: When the pedal opening is large, the mechanical braking force dominates, the electric braking force compensation is reduced, and energy recovery is reduced; when the opening is small, the electric braking force plays a greater role, enhancing energy recovery.

[0086] Vehicle mass correction factor: Vehicles with greater mass have more potential and kinetic energy converted into electrical energy during braking, which improves energy recovery efficiency.

[0087] Slope correction factor: When driving downhill, increase the slope factor to utilize the potential energy conversion during the downhill process to enhance energy recovery efficiency.

[0088] Driving Style Correction Factor: The size of this factor depends on multiple factors. First, it depends on the probability of the driving style output by the support vector machine algorithm. Second, the driving style correction factor for drivers with the same driving style varies at different speeds, vehicle masses, and slopes. Specific data can be obtained through table lookup. For example, the support vector machine algorithm determines that the driver is 70% aggressive, 20% conservative, and 10% economical. Then, based on the speed, vehicle mass, and slope obtained at this time, a table lookup is performed for each of the three driving styles: aggressive, conservative, and economical. The resulting driving style correction factor A_F for 100% aggressive, C_F for 100% conservative, and E_F for 100% economical is obtained. A weighted calculation is then performed to obtain the final driving style correction factor D_F = 0.7*A_F + 0.2*C_F + 0.1*E_F.

[0089] In the second aspect, based on the above embodiment of the electric vehicle adaptive energy recovery method integrating driver behavior, the present application provides an embodiment of an energy recovery system for the electric vehicle adaptive energy recovery method integrating driver behavior. Figure 3As shown, the energy recovery system includes an analysis module, an estimation module, a weighting module and an energy recovery module. Specifically:

[0090] The analysis module is used to analyze the current driving data and historical driving data of the vehicle, determine the driving style and predict the probability of the driving style occurring.

[0091] The estimation module is used to estimate the vehicle mass based on the vehicle's current driving data and calculate two values ​​of the slope using two different algorithms.

[0092] The weighting module is used to set a weight based on a preset vehicle speed threshold value, and obtain a fused slope by weighting the two values.

[0093] The energy recovery module is used to determine the corresponding correction factors based on the vehicle's current driving data, vehicle mass, integrated slope, and the probability of the driving style. The initial braking torque corresponding to the current motor speed is corrected to obtain a reference braking torque. The reference braking torque is compared with the preset motor torque range to obtain the optimal braking torque and perform braking to achieve energy recovery.

[0094] In this embodiment, the energy recovery system combines driver driving style prediction with factors such as vehicle mass, slope, speed, and pedal stroke to optimize the braking energy recovery efficiency of pure electric and hybrid vehicles in real time through intelligent algorithms. This energy recovery system uses a support vector machine algorithm to analyze historical and real-time driving data to accurately predict the driver's driving style. Based on the predicted results and the vehicle's real-time status, it dynamically adjusts the braking torque to optimize energy recovery and enhance driving safety and comfort.

[0095] Furthermore, in this embodiment, the energy recovery module calculates a correction factor based on driving style prediction and real-time vehicle status data (such as speed and slope), dynamically adjusting the braking torque to meet energy recovery requirements while maintaining a smooth braking process. Compared to most existing technologies that only consider vehicle physical parameters and ignore the impact of driving style on torque adjustment, this energy recovery system utilizes a dynamic control strategy based on the driver's driving style and multiple factors, comprehensively considering factors such as driving style, slope, and vehicle weight. This significantly improves the applicability and efficiency of the energy recovery system.

[0096] Furthermore, in one embodiment, the energy recovery system also includes a transmission module, which is used to transmit the real-time estimated slope and vehicle mass information and the vehicle speed and brake pedal opening information collected by the sensor to the subsequent braking energy recovery module to optimize the braking feedback torque and braking response.

[0097] Furthermore, one embodiment may also provide a user interface specifically designed for electric vehicle brake energy recovery systems, designed to be simple, intuitive, and easy to use. Through this interface, users can clearly and intuitively view the current vehicle's brake energy recovery status, including important information such as the real-time energy recovery rate and the distribution ratio between regenerative braking and mechanical braking. Users can also set and adjust brake energy recovery parameters within a certain range, such as setting the energy recovery intensity level (low, medium, or high) and selecting different braking modes (economy mode, comfort mode, etc.), thereby customizing the brake energy recovery strategy based on their needs and driving habits. The interface also provides real-time feedback on the effects of user settings, such as the expected change in energy recovery rate and the impact on driving range after adjusting parameters. This allows users to clearly understand the impact of their operations on vehicle performance and proactively participate in the brake energy recovery process.

[0098] This application combines the vehicle's current driving data, vehicle mass, fusion slope and the probability of the driving style to determine the corresponding correction factors, correct the initial braking torque corresponding to the current motor speed to obtain the reference braking torque, compare the reference braking torque with the preset motor torque range, obtain the optimal braking torque and perform braking to achieve energy recovery, effectively optimize the distribution of regenerative braking energy, and increase the energy recovery rate by 5%-10%.

[0099] It should be noted that the serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0100] The terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices. The terms "first", "second" and "third" are used to distinguish different objects, etc., and do not represent a sequence, nor do they limit the "first", "second" and "third" to different types.

[0101] In the description of the embodiments of this application, the words "exemplary," "for example," or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary," "for example," or "for example" in the embodiments of this application should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary," "for example," or "for example" is intended to present the relevant concepts in a concrete manner.

[0102] In the description of the embodiments of the present application, unless otherwise specified, “ / ” means or, for example, S / B can mean S or B; “and / or” in the text is merely a description of the association relationship of associated objects, indicating that three relationships may exist, for example, S and / or B can mean: S exists alone, S and B exists at the same time, and B exists alone. In addition, in the description of the embodiments of the present application, “multiple” refers to two or more than two.

[0103] In some processes described in the embodiments of the present application, multiple operations or steps are included that appear in a specific order. However, it should be understood that these operations or steps may not be performed in the order in which they appear in the embodiments of the present application or may be performed in parallel. The sequence numbers of the operations are only used to distinguish between different operations, and the sequence numbers themselves do not represent any order of execution. In addition, these processes may include more or fewer operations, and these operations or steps may be performed in sequence or in parallel, and these operations or steps may be combined.

[0104] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, or the part that contributes to the existing technology, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RSM, disk, CD) as described above, and includes a number of instructions for enabling a terminal device to execute the methods described in each embodiment of this application.

[0105] The above are only preferred embodiments of the present application and do not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A method for adaptive energy recovery of electric vehicles integrating driver behavior, characterized in that: The method comprises: Analyze the vehicle's current and historical driving data to determine the driving style and predict the probability of that driving style occurring; Based on the vehicle's current driving data, the vehicle mass is estimated, and two values ​​of the slope are calculated using two different algorithms. The weight is set by a preset vehicle speed threshold value, and the fusion slope is obtained by weighting the two values; Based on the vehicle's current driving data, vehicle mass, blended slope, and the probability of a driving style, a corresponding correction factor is determined. The initial braking torque corresponding to the current motor speed is corrected to obtain a baseline braking torque. The baseline braking torque is compared with a preset motor torque range to obtain the optimal braking torque and then brake to achieve energy recovery. The support vector machine algorithm is used to analyze current and historical driving data to determine the driver's corresponding driving style, which includes aggressive, conservative, and economical driving styles. The probability of the driver's corresponding driving style appearing in all driving styles is predicted. Then, based on the vehicle speed, vehicle mass, and slope obtained at this time, a table is looked up according to the three styles of aggressive, conservative, and economical. The driving style correction factor A_F under 100% aggressiveness, the driving style correction factor C_F under 100% conservativeness, and the driving style correction factor E_F under 100% economicalness are obtained. The final driving style correction factor is then obtained through weighted calculation.

2. The electric vehicle adaptive energy recovery method integrating driver behavior as claimed in claim 1, characterized in that: Analyze the vehicle's current and historical driving data to determine the driving style and predict the probability of that driving style occurring, including: Perform preprocessing operations on driving data, including filtering and normalization.

3. The electric vehicle adaptive energy recovery method integrating driver behavior as claimed in claim 1, characterized in that: Based on the vehicle's current driving data, the vehicle mass is estimated, and two slope values ​​are calculated using two different algorithms, including: According to the current driving data of the vehicle, the vehicle mass is estimated by the least square estimation method, and the two values ​​of the slope are calculated respectively by Kalman filtering and the least square estimation method.

4. The electric vehicle adaptive energy recovery method integrating driver behavior as claimed in claim 1, characterized in that: The weight is set by a preset vehicle speed threshold, and the fusion slope is obtained by weighting the two values, including: The vehicle speed threshold is determined according to calibration; The weighting coefficient is determined by the critical value of vehicle speed, and the two values ​​of the slope are weighted to obtain the fused slope.

5. The electric vehicle adaptive energy recovery method integrating driver behavior as claimed in claim 1, characterized in that: The corresponding correction factors are determined by combining the current driving data of the vehicle, the vehicle mass, the fusion slope, and the probability of the driving style, including: Query a pre-set correction factor table to obtain the correction factors corresponding to the required current vehicle speed, pedal opening, vehicle mass, blended slope, and probability of occurrence of the driving style; The correction factor table includes: real-time vehicle speed, pedal opening, vehicle mass, integrated slope, and probability of occurrence of driving style, as well as correction factors corresponding to each probability.

6. The electric vehicle adaptive energy recovery method integrating driver behavior as claimed in claim 1, characterized in that: The correcting of the initial braking torque corresponding to the current motor speed to obtain the reference braking torque includes: Obtain the initial braking torque from the current motor speed; Multiply the initial braking torque by the required correction factor to obtain the reference braking torque.

7. The electric vehicle adaptive energy recovery method integrating driver behavior as claimed in claim 1, characterized in that: The comparing the reference braking torque with a preset motor torque range to obtain the optimal braking torque includes: If the reference braking torque is within the preset motor torque range, the reference braking torque is taken as the optimal braking torque; if the reference braking torque is less than the minimum motor torque, the minimum motor torque is taken as the optimal braking torque; if the reference braking torque is greater than the maximum motor torque, the maximum motor torque is taken as the optimal braking torque.

8. The electric vehicle adaptive energy recovery method integrating driver behavior as claimed in claim 1, characterized in that: Before executing the braking, the method further includes: limiting the output of the torque signal by controlling the slope change of the braking torque and using filtering technology.

9. The electric vehicle adaptive energy recovery method integrating driver behavior according to any one of claims 1 to 8 is characterized in that: The vehicle's driving data includes: vehicle speed, acceleration, brake pedal opening, vehicle posture information and battery remaining power.

10. An energy recovery system based on the electric vehicle adaptive energy recovery method integrating driver behavior as described in any one of claims 1 to 8, characterized in that: The energy recovery system comprises: An analysis module is used to analyze the vehicle's current and historical driving data, determine the driving style, and predict the probability of the driving style occurring; An estimation module, which is used to estimate the vehicle mass based on the vehicle's current driving data and calculate two values ​​of the slope using two different algorithms; A weighting module, configured to set a weight based on a preset vehicle speed threshold value, and obtain a fused slope by weighting the two values; The energy recovery module is used to determine the corresponding correction factors based on the vehicle's current driving data, vehicle mass, integrated slope, and the probability of the driving style. The initial braking torque corresponding to the current motor speed is corrected to obtain a reference braking torque. The reference braking torque is compared with the preset motor torque range to obtain the optimal braking torque and perform braking to achieve energy recovery.

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