Electric vehicle self-adaptive energy recovery method and system fusing driver behaviors

Abstract

The invention relates to an electric vehicle self-adaptive energy recovery method and system fusing driver behaviors, and the method comprises the following steps: analyzing the current driving data and historical driving data of a vehicle, judging a driving style, and predicting the occurrence probability of the driving style; according to the current driving data of the vehicle, the mass of the whole vehicle is estimated, and two values of the gradient are calculated through two different algorithms respectively; setting a weight according to a preset vehicle speed critical value, and weighting the two values to obtain a fusion gradient; according to the current driving data of the vehicle, the whole vehicle mass, the fusion gradient and the driving style occurrence probability, corresponding correction factors are determined respectively, the initial braking torque corresponding to the current motor rotating speed is corrected to obtain a reference braking torque, and the reference braking torque is compared with a preset motor torque range; and the optimal braking torque is obtained, braking is executed, and energy recovery is achieved. According to the method, distribution of regenerative braking energy is effectively optimized, and the energy recovery rate is increased.

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 a low energy recovery efficiency.

[0003] To this end, some energy recovery technologies introduce correction coefficients for related factors such as torque, speed, wheel angular velocity, slope, and vehicle mass, and use these coefficients and two sets of torque diagrams for interpolation calculations to determine the optimal regenerative braking torque. Although this method takes into account multiple factors during vehicle driving and improves energy recovery efficiency to a certain extent, there are still some problems: First, the vehicle mass and slope are measured by sensors. When the sensor accuracy is insufficient, the response is not timely, or it is disturbed by the external environment (such as bad weather or extreme temperature), the measured data may be inaccurate; second, the correction coefficients considered are only basic factors such as vehicle speed, slope, and vehicle mass, which are poorly adaptable to the driver's driving habits. Summary of the invention

[0004] In response to the above-mentioned problems existing in the current technology, the present application provides an electric vehicle adaptive energy recovery method and system integrating driver behavior, so as to solve the technical problems of inaccurate energy recovery technical parameters and poor adaptability of the driver's 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 driving data and historical driving data, 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 critical value, 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 probability of driving style, the corresponding correction factors are determined respectively, and 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.

[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 current driving data and historical driving data are analyzed by the support vector machine algorithm 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 in 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 respectively by 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 value, and a fusion 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 current driving data of the vehicle, the vehicle mass, the fusion slope and the probability of the driving style to determine the corresponding correction factors respectively includes:

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

[0020] The correction factor table includes: real-time vehicle speed, pedal opening, vehicle mass, fusion 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] Further, in one embodiment, the 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 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 of 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 according to the current driving data of the vehicle, and calculate two values ​​of the slope respectively through two different algorithms.

[0031] The weighting module is used to set the weight according to the preset vehicle speed critical value, and obtain the fusion 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 probability of driving style, correct the initial braking torque corresponding to the current motor speed to obtain a reference braking torque, compare the reference braking torque with a preset motor torque range, obtain the optimal braking torque and perform braking to achieve energy recovery.

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

[0034] This application analyzes the vehicle's current driving data and historical driving data, determines the driving style and predicts the probability of the driving style occurring, adjusts the braking response based on the driving style and its probability of occurrence to match personal driving habits, and improves the adaptability of energy recovery technology to the driver's driving habits.

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

[0036] The corresponding correction factors are determined based on the vehicle's current driving data, vehicle mass, fusion slope, and probability of occurrence of driving style. 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 It is a schematic diagram of the optimal braking torque determination process of an embodiment of the present application.

[0039] Figure 3 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 solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, 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 the present application more clear, the implementation methods of the present 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. The vehicle mass is estimated based on the current driving data of the vehicle, and two values ​​of the slope are calculated respectively by two different algorithms.

[0046] S3. Setting a weight according to a preset vehicle speed critical value, and obtaining a fusion slope by weighting the two values.

[0047] S4. Determine the corresponding correction factors based on the vehicle's current driving data, vehicle mass, integrated slope, and probability of occurrence of driving style, correct the initial braking torque corresponding to the current motor speed to obtain a reference braking torque, compare the reference braking torque with a preset motor torque range, obtain the optimal braking torque, and perform braking to achieve energy recovery.

[0048] The embodiment of the present application analyzes the current driving data and historical driving data of the vehicle, determines the driving style and predicts the probability of the driving style, adjusts the braking response according to the driving style and its probability of occurrence to match the individual driving habits, and improves the adaptability of the energy recovery technology to the driver's driving habits. According to the current driving data of the vehicle, the vehicle mass is estimated, and two values ​​of the slope are calculated respectively by two different algorithms. The weight is set by the preset vehicle speed critical value, and the two values ​​are weighted to obtain the fused slope, which reduces the dependence on sensors and improves the accuracy of the data 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, analyzing the current driving data and historical driving data, such as the frequency of use of the accelerator and brake, steering amplitude, driving speed, acceleration change rate, etc., through a support vector machine algorithm to determine the driver's corresponding driving style. The driving styles include: aggressive, stable, and economical, predicting the probability of the driver's corresponding driving style appearing in all driving styles, and transmitting the driving style related data to the braking 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, the accuracy and availability of the data can be ensured, and different types of data can 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 in 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 step S1 mainly involves collecting 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, so as to 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, use radial basis kernel support vector machine for training, select appropriate C value and γ value to optimize the model through cross-validation, and train a classifier that can accurately predict the driver's driving style. By analyzing the current driving data and historical driving data of the vehicle, the driver's driving style is accurately predicted, and the output of the support vector machine algorithm is set as a probability output, that is, the probability of the driver's corresponding driving style appearing in all driving styles.

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

[0058] S201. According to the real-time information collected by the gyroscope and acceleration in the current driving data of the vehicle, 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, respectively calculating two values ​​of the slope by using Kalman filtering and least squares estimation method.

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

[0062] Set the critical speed value v1. When the collected current speed v>v1, the calculation formula of the fusion slope is:

[0063] Fusion slope = θ1*M+(1-M)*θ2,

[0064] Among them, θ1 is the slope value calculated by Kalman filtering, θ2 is the slope value calculated by the least squares estimation method, and M is the weighting coefficient, which is the maximum value of the slope fusion dynamics weighting coefficient.

[0065] When the collected current vehicle speed v≤v1, the calculation formula for the fused slope is:

[0066] Fusion slope = θ1*(av 2 +bv)+[1-(av 2 +bv)]*θ2,

[0067] Among them, av 2 +bv is the weighting coefficient, and the values ​​of a and b are given by the formula v1=-b / 2a and the formula Calculated.

[0068] In this embodiment, the algorithm-based slope and mass estimation reduces the reliance on sensors, improves the accuracy and efficiency of data processing, and can achieve speed ramp-up control when going down a long slope, so that the vehicle speed can always be maintained within a preset safety range, which can not only significantly improve the driver's sense of security and enable him to maintain higher driving confidence under complex road conditions, but also ensure the stability and comfort of vehicle driving. Under the premise of ensuring safety, full use is made of brake energy recovery to efficiently convert the kinetic energy generated by the vehicle's deceleration into electrical energy storage, thereby improving energy utilization efficiency.

[0069] Further, in one embodiment, in the above step S4, the corresponding correction factors are determined respectively according to the current driving data of the vehicle, the vehicle mass, the fusion slope and the probability of the driving style, including:

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

[0071] S402, querying a pre-set correction factor table to obtain correction factors corresponding to the required current vehicle speed, pedal opening, vehicle mass, integrated 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 the driving style, and correction factors corresponding to each probability.

[0072] Further, in one embodiment, in the above 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 the required correction factor (ie, the 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 and the preset motor torque range. If the reference braking torque is within the preset motor torque range (including being equal to 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 value of the motor torque as the optimal braking torque.

[0081] S409, taking the maximum value of the motor torque as the optimal braking torque.

[0082] Furthermore, in one embodiment, in the above step S4, before executing the braking, the step further includes: limiting the output of the torque signal by controlling the slope change of the braking torque and filtering technology, thereby avoiding the impact and abnormal noise generated by the vehicle during braking to a certain extent and improving 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 range of each correction factor in the correction factor table is given, including a vehicle speed correction factor, a brake pedal opening correction factor, a vehicle mass correction factor, a slope correction factor, and a 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, the 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 is dominant, 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 and energy recovery is enhanced.

[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. Secondly, the driving style correction factors of drivers with the same driving style are different at different speeds, different vehicle weights, and different slopes. The specific data can be obtained by looking up the table. For example: First, the support vector machine algorithm is used to obtain that the driver is 70% aggressive, 20% stable, and 10% economical. Secondly, according to the speed, vehicle weight, and slope obtained at this time, the table is looked up according to the three styles of aggressive, stable, and economical, and the driving style correction factor A_F under 100% aggressive, the driving style correction factor C_F under 100% stable, and the driving style correction factor E_F under 100% economical are obtained. Then, the final driving style correction factor D_F=0.7*A_F+0.2*C_F+0.1*E_F is obtained through weighted calculation.

[0089] In a second aspect, based on the above-mentioned embodiment of the electric vehicle adaptive energy recovery method integrating driver behavior, the present application provides an embodiment of an energy recovery system of the electric vehicle adaptive energy recovery method integrating driver behavior. Figure 3 As 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 according to the current driving data of the vehicle, and calculate two values ​​of the slope respectively through two different algorithms.

[0092] The weighting module is used to set the weight according to the preset vehicle speed critical value, and obtain the fusion 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 probability of driving style, correct the initial braking torque corresponding to the current motor speed to obtain a reference braking torque, compare the reference braking torque with a preset motor torque range, obtain the optimal braking torque and perform braking to achieve energy recovery.

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

[0095] Furthermore, in this embodiment, based on the driving style prediction results and the real-time vehicle status data (such as vehicle speed, slope, etc.), the energy recovery module calculates the correction factor and dynamically adjusts the braking torque to meet the energy recovery requirements and maintain the stability of the braking process. Compared with most current technologies that only consider vehicle physical parameters and ignore the impact of driving style on torque adjustment, the energy recovery system adopts a dynamic control strategy based on the driver's driving style and multiple factors, comprehensively considering factors such as the driver's driving style, slope, quality, etc., significantly improving the application scope and energy recovery 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 a subsequent braking energy recovery module to optimize the braking feedback torque and braking response.

[0097] Furthermore, in one embodiment, a user interaction interface specifically for the electric vehicle brake energy recovery system can also be provided, and its design follows the principles of simplicity, intuitiveness, and ease of use. Through this interface, the user can clearly and intuitively view the brake energy recovery status of the current vehicle, including important information such as real-time energy recovery rate, distribution ratio of regenerative braking and mechanical braking, etc. The user can also set and adjust the relevant parameters of brake energy recovery within a certain range, such as setting the intensity level of energy recovery (low, medium, high), selecting different braking modes (economic mode, comfort mode, etc.), so as to customize the brake energy recovery strategy according to their own needs and driving habits. At the same time, the interface will provide real-time feedback on the effects of the user's settings, such as displaying the expected energy recovery rate changes after adjusting the parameters, the impact on the cruising range, etc., so that the user can clearly understand the impact of their operations on the vehicle performance, and then more actively participate in the brake energy recovery process.

[0098] The present application combines the vehicle's current driving data, vehicle mass, fusion slope and probability of driving style occurrence to determine the corresponding correction factors, respectively, corrects the initial braking torque corresponding to the current motor speed to obtain a reference braking torque, compares the reference braking torque with a preset motor torque range, obtains the optimal braking torque and performs braking to achieve energy recovery, effectively optimizes the distribution of regenerative braking energy, and increases 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 only for description 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 "first", "second" and "third" to different types.

[0101] In the description of the embodiments of the present application, "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 the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary", "for example" or "for example" is intended to present related concepts in a specific way.

[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. The “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 exist 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 that appear in a specific order are included, but it should be understood that these operations or steps may not be executed in the order in which they appear in the embodiments of the present application or in parallel, and the sequence number of the operation is only used to distinguish the different operations, and the sequence number itself does not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed 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 a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RSM, disk, CD) as described above, and includes a number of instructions for a terminal device to execute the methods described in each embodiment of the present application.

[0105] The above are only preferred embodiments of the present application, and are not intended to 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. An electric vehicle adaptive energy recovery method integrating driver behavior, characterized in that: The method comprises: Analyze the vehicle's current driving data and historical driving data to determine the driving style and predict the probability of the driving style occurring; According to the current driving data of the vehicle, the vehicle mass is estimated, and two values ​​of the slope are calculated respectively through 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, fusion slope and probability of driving style, the corresponding correction factors are determined respectively, and 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.

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 the driving style occurring, including: Perform pre-processing operations on driving data, including filtering and normalization; The current driving data and historical driving data are analyzed by the support vector machine algorithm 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 in all driving styles.

3. The electric vehicle adaptive energy recovery method integrating driver behavior as claimed in claim 1, characterized in that: According to the current driving data of the vehicle, the vehicle mass is estimated, and two values ​​of the slope are calculated respectively through 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 the preset vehicle speed threshold value, and the fusion slope is obtained by weighting the two values, including: The vehicle speed critical value 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, respectively, including: Query the pre-set correction factor table to obtain the correction factors corresponding to the required current vehicle speed, pedal opening, vehicle mass, integrated slope, and probability of occurrence of the driving style; The correction factor table includes: real-time vehicle speed, pedal opening, vehicle mass, fusion 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 the initial braking torque corresponding to the current motor speed to obtain the reference braking torque includes: The initial braking torque is obtained 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 step of comparing the reference braking torque with a preset motor torque range to obtain an 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 current driving data and historical driving data of the vehicle, determine the driving style and predict the probability of the driving style occurring; An estimation module is used to estimate the vehicle mass according to the current driving data of the vehicle and calculate two values ​​of the slope respectively through two different algorithms; A weighting module, which is used to set a weight according to a preset vehicle speed critical value, and obtain a fusion 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 probability of driving style, correct the initial braking torque corresponding to the current motor speed to obtain a reference braking torque, compare the reference braking torque with a preset motor torque range, obtain the optimal braking torque and perform braking to achieve energy recovery.

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