Energy recovery intensity control method, device, vehicle and readable medium

Abstract

Embodiments of the present invention provide a method, device, vehicle, and readable medium for controlling energy recovery intensity. The method includes: determining a first reverse torque of the vehicle when the accelerator pedal is released while the vehicle is in an automatic energy recovery intensity control mode; obtaining driving style information specific to the driver; the driving style information is obtained through deep learning based on the driver's driving habit data; using the driving style information to modify the first reverse torque to obtain a target reverse torque; and controlling the energy recovery intensity of the vehicle using the target reverse torque. In this embodiment of the present invention, the driver only needs to release the accelerator pedal, and the vehicle automatically modifies the reverse torque according to the driver's driving style, thereby controlling the energy recovery intensity of the vehicle, thereby meeting the driver's deceleration requirements and reducing the frequency of driver intervention in the vehicle's deceleration process due to the driver's driving style not matching the preset energy recovery intensity.

Description

Technical Field

[0001] The present invention relates to the field of energy recovery technology, and in particular to a method for controlling energy recovery intensity, a device for controlling energy recovery intensity, a vehicle, and a computer-readable medium. Background Art

[0002] Energy recovery means that after the driver releases the accelerator pedal, the vehicle is decelerated by outputting torque in the opposite direction of travel, while the kinetic energy lost by deceleration is recovered and used to charge the battery, thereby improving the vehicle's energy utilization and endurance.

[0003] The current energy recovery scheme controls the energy recovery intensity of the vehicle based on the vehicle ahead. However, it cannot meet the different driving styles of different drivers. When the driver's driving style does not match the preset energy recovery intensity, the driver needs to intervene more in the vehicle's deceleration process. Summary of the Invention

[0004] In view of the above problems, embodiments of the present invention are proposed to provide a method for controlling energy recovery intensity that overcomes the above problems or at least partially solves the above problems.

[0005] The embodiment of the present invention further provides an energy recovery intensity control device, a vehicle, and a storage medium to ensure the implementation of the above method.

[0006] In order to solve the above problems, an embodiment of the present invention discloses a method for controlling energy recovery intensity, the method comprising:

[0007] When the vehicle is in an automatic energy recovery intensity control mode, if it is detected that the accelerator pedal is released, determining a first reverse torque of the vehicle;

[0008] Acquiring driving style information for a driver; the driving style information is obtained by deep learning based on the driver's driving habit data;

[0009] Using the driving style information, correcting the first reverse torque to obtain a target reverse torque;

[0010] The target reverse torque is used to control the energy recovery intensity of the vehicle.

[0011] Optionally, when the vehicle is in the automatic energy recovery intensity control mode and it is detected that the accelerator pedal is released, determining the first reverse torque of the vehicle includes:

[0012] When the vehicle is in the automatic control energy recovery intensity mode, if it is detected that the accelerator pedal is released, the initial energy recovery intensity of the vehicle is calculated based on the distance between the vehicle and the vehicle ahead, the speed of the vehicle, and the speed of the vehicle ahead;

[0013] collecting vehicle parameters of the vehicle;

[0014] A first reverse torque of the vehicle is determined according to the initial energy recovery intensity and the vehicle parameters.

[0015] Optionally, the adopting the driving style information to correct the first reverse torque to obtain a target reverse torque includes:

[0016] determining a target driving style of the driver based on the driving style information;

[0017] determining a target driving style coefficient according to the target driving style;

[0018] A product value of the first reverse torque and the target driving style coefficient is calculated, and the product value is determined as a target reverse torque.

[0019] Optionally, the target driving style includes a general style; and determining a target driving style coefficient according to the target driving style includes:

[0020] When the target driving style is a general style, the target driving style coefficient is determined to be a first coefficient; wherein the first coefficient is 1.

[0021] Optionally, the target driving style includes an aggressive style; and determining a target driving style coefficient according to the target driving style includes:

[0022] When the target driving style is an aggressive style, and the distance between the vehicle and the preceding vehicle is greater than a first preset distance, and the speed difference between the vehicle and the preceding vehicle is greater than the first preset speed difference, determining the target driving style coefficient to be a second coefficient; wherein the second coefficient is less than 1;

[0023] Until the distance between the vehicle and the vehicle ahead is less than or equal to a first preset distance and the speed difference between the vehicle and the vehicle ahead is less than or equal to the first preset speed difference, the target driving style coefficient is determined to be a third coefficient; wherein the third coefficient is greater than 1.

[0024] Optionally, the target driving style includes a conservative style; and determining a target driving style coefficient according to the target driving style includes:

[0025] When the target driving style is a conservative style, and the distance between the vehicle and the preceding vehicle is greater than a second preset distance, and the speed difference between the vehicle and the preceding vehicle is greater than the second preset speed difference, determining the target driving style coefficient to be a fourth coefficient; wherein the fourth coefficient is greater than 1;

[0026] Until the distance between the vehicle and the vehicle ahead is less than or equal to a second preset distance and the speed difference between the vehicle and the vehicle ahead is less than or equal to a second preset speed difference, the target driving style coefficient is determined to be a fifth coefficient; wherein the fifth coefficient is less than 1.

[0027] Optionally, obtaining the driving style information of the driver includes:

[0028] Acquiring driving habit data of the driver;

[0029] generating an actual deceleration curve for the driver based on the driving habit data;

[0030] Fitting the actual deceleration curve with a preset deceleration curve;

[0031] Driving style information for the driver is generated according to the fitting result.

[0032] Optionally, obtaining the driver's driving habit data includes:

[0033] When the vehicle is in manual control energy recovery intensity mode, collect the accelerator pedal opening;

[0034] determining a second reverse torque of the vehicle according to the accelerator pedal opening;

[0035] controlling the energy recovery intensity of the vehicle by using the second reverse torque;

[0036] During the deceleration process based on the second reverse torque, the driver's driving habit data is collected; the driving habit data includes the distance between the vehicle and a preceding vehicle, the speed of the vehicle, and the speed of the preceding vehicle.

[0037] Optionally, generating an actual deceleration curve for the driver based on the driving habit data includes:

[0038] determining a speed difference between the vehicle and the preceding vehicle based on the speed of the vehicle and the speed of the preceding vehicle;

[0039] Establishing a relationship curve with the distance between the vehicle and the preceding vehicle as the abscissa and the speed difference between the vehicle and the preceding vehicle as the ordinate;

[0040] The relationship curve is determined as an actual deceleration curve for the driver.

[0041] Optionally, the actual deceleration curve includes a plurality of deceleration curves, the preset deceleration curve includes a plurality of deceleration curves, and each preset deceleration curve corresponds to a preset driving style; and fitting the actual deceleration curve with the preset deceleration curve includes:

[0042] Fitting each actual deceleration curve with each preset deceleration curve;

[0043] Generating driving style information for the driver according to the fitting result includes:

[0044] Determining, from a plurality of preset deceleration curves, a target preset deceleration curve whose degree of fit with the actual deceleration curves satisfies a preset condition;

[0045] The preset driving style corresponding to the target preset deceleration curve is used to generate driving style information for the driver.

[0046] An embodiment of the present invention further discloses a device for controlling energy recovery intensity, the device comprising:

[0047] a first reverse torque determination module, configured to determine a first reverse torque of the vehicle if it is detected that the accelerator pedal is released when the vehicle is in an automatic energy recovery intensity control mode;

[0048] A driving style information acquisition module, configured to acquire driving style information specific to a driver; the driving style information is obtained by deep learning based on the driver's driving habit data;

[0049] a first reverse torque correction module, configured to correct the first reverse torque using the driving style information to obtain a target reverse torque;

[0050] An energy recovery intensity control module is configured to control the energy recovery intensity of the vehicle using the target reverse torque.

[0051] Optionally, the first reverse torque determination module includes:

[0052] an initial energy recovery intensity calculation submodule, configured to calculate the vehicle's initial energy recovery intensity based on the distance between the vehicle and a preceding vehicle, the vehicle's speed, and the preceding vehicle's speed when the accelerator pedal is detected to be released while the vehicle is in an automatic energy recovery intensity control mode;

[0053] A vehicle parameter acquisition submodule, configured to acquire vehicle parameters of the vehicle;

[0054] The first reverse torque determination submodule is configured to determine a first reverse torque of the vehicle according to the initial energy recovery intensity and the vehicle parameters.

[0055] Optionally, the first reverse torque correction module includes:

[0056] a target driving style determination submodule, configured to determine a target driving style of the driver based on the driving style information;

[0057] a target driving style coefficient determination submodule, configured to determine a target driving style coefficient according to the target driving style;

[0058] The target reverse torque determination submodule is configured to calculate a product value of the first reverse torque and the target driving style coefficient, and determine the product value as the target reverse torque.

[0059] Optionally, the target driving style includes a general style; and the target driving style coefficient determination submodule includes:

[0060] The first target driving style coefficient determining unit is configured to determine the target driving style coefficient as a first coefficient when the target driving style is a general style; wherein the first coefficient is 1.

[0061] Optionally, the target driving style includes an aggressive style; and the target driving style coefficient determination submodule includes:

[0062] a second target driving style coefficient determining unit, configured to determine, when the target driving style is an aggressive style and the distance between the vehicle and the preceding vehicle is greater than a first preset distance and the speed difference between the vehicle and the preceding vehicle is greater than the first preset speed difference, the target driving style coefficient to be a second coefficient; wherein the second coefficient is less than 1;

[0063] and a third target driving style coefficient determining unit, configured to determine the target driving style coefficient to be a third coefficient until the distance between the vehicle and the preceding vehicle is less than or equal to a first preset distance and the speed difference between the vehicle and the preceding vehicle is less than or equal to the first preset speed difference; wherein the third coefficient is greater than 1.

[0064] Optionally, the target driving style includes a conservative style; and the target driving style coefficient determination submodule includes:

[0065] a fourth target driving style coefficient determining unit, configured to, when the target driving style is a conservative style, determine the target driving style coefficient to be a fourth coefficient when the distance between the vehicle and the preceding vehicle is greater than a second preset distance and the speed difference between the vehicle and the preceding vehicle is greater than the second preset speed difference; wherein the fourth coefficient is greater than 1;

[0066] and a fifth target driving style coefficient determining unit, configured to determine the target driving style coefficient as a fifth coefficient until the distance between the vehicle and the preceding vehicle is less than or equal to a second preset distance and the speed difference between the vehicle and the preceding vehicle is less than or equal to the second preset speed difference; wherein the fifth coefficient is less than 1.

[0067] Optionally, the driving style information acquisition module includes:

[0068] A driving habit data acquisition submodule, configured to acquire the driver's driving habit data;

[0069] an actual deceleration curve generating submodule, configured to generate an actual deceleration curve for the driver based on the driving habit data;

[0070] A fitting submodule, configured to fit the actual deceleration curve with a preset deceleration curve;

[0071] The driving style information generating submodule is configured to generate driving style information for the driver according to the fitting result.

[0072] Optionally, the driving habit data acquisition submodule includes:

[0073] An accelerator pedal opening acquisition unit, used to acquire the accelerator pedal opening when the vehicle is in a manual control energy recovery intensity mode;

[0074] a second reverse torque determining unit, configured to determine a second reverse torque of the vehicle according to the accelerator pedal opening;

[0075] an energy recovery intensity control unit, configured to control the energy recovery intensity of the vehicle using the second reverse torque;

[0076] A driving habit data collection unit is used to collect the driver's driving habit data during the deceleration process based on the second reverse torque; the driving habit data includes the distance between the vehicle and the vehicle in front, the speed of the vehicle, and the speed of the vehicle in front.

[0077] Optionally, the actual deceleration curve generation submodule includes:

[0078] a speed difference determining unit, configured to determine a speed difference between the vehicle and the preceding vehicle based on the speed of the vehicle and the speed of the preceding vehicle;

[0079] a relationship curve establishing unit, configured to establish a relationship curve with the distance between the vehicle and the preceding vehicle as the abscissa and the speed difference between the vehicle and the preceding vehicle as the ordinate;

[0080] The actual deceleration curve determining unit is configured to determine the relationship curve as an actual deceleration curve for the driver.

[0081] Optionally, the actual deceleration curve includes a plurality of curves, the preset deceleration curve includes a plurality of curves, and each preset deceleration curve corresponds to a preset driving style; and the fitting submodule includes:

[0082] A fitting unit, used for fitting each actual deceleration curve with each preset deceleration curve;

[0083] The driving style information generation submodule includes:

[0084] a target preset deceleration curve determining unit, configured to determine, from a plurality of preset deceleration curves, a target preset deceleration curve whose degree of fit with the actual deceleration curves satisfies a preset condition;

[0085] The driving style information generating unit is configured to generate driving style information for the driver by using a preset driving style corresponding to the target preset deceleration curve.

[0086] An embodiment of the present invention also provides a vehicle comprising a memory and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by one or more processors, and the one or more programs include a method for controlling the intensity of energy recovery as described in any one of the embodiments of the present invention.

[0087] An embodiment of the present invention further provides a readable storage medium. When instructions in the storage medium are executed by a processor of an electronic device, the electronic device can execute the method for controlling the intensity of energy recovery as described in any one of the embodiments of the present invention.

[0088] Compared with the prior art, the embodiments of the present invention have the following advantages:

[0089] In an embodiment of the present invention, when a vehicle is in an automatic regenerative braking intensity control mode and the accelerator pedal is released, a first reverse torque of the vehicle is determined; driving style information specific to the driver is obtained; the driving style information is obtained through deep learning based on the driver's driving habit data; the first reverse torque is modified using the driving style information to obtain a target reverse torque; and the target reverse torque is used to control the regenerative braking intensity of the vehicle. This embodiment of the present invention allows the driver to simply release the accelerator pedal, and the vehicle automatically modifies the reverse torque based on the driver's driving style, thereby controlling the regenerative braking intensity of the vehicle, thereby meeting the driver's deceleration requirements and reducing the frequency of driver intervention in the vehicle's deceleration process due to driving style inconsistencies with the preset regenerative braking intensity. BRIEF DESCRIPTION OF THE DRAWINGS

[0090] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0091] Figure 1 This is a flowchart of a method for controlling energy recovery intensity provided by an embodiment of the present invention;

[0092] Figure 2 This is a general style curve graph provided by an embodiment of the present invention;

[0093] Figure 3 This is a radical style curve graph provided by an embodiment of the present invention;

[0094] Figure 4 This is a conservative style curve graph provided by an embodiment of the present invention;

[0095] Figure 5 This is a structural block diagram of an energy recovery intensity control device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0096] 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 them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0097] Reference Figure 1 , shows a flowchart of a method for controlling energy recovery intensity provided by an embodiment of the present invention. The method may specifically include the following steps:

[0098] Step 101 : When a vehicle is in an automatic energy recovery intensity control mode and it is detected that an accelerator pedal is released, a first reverse torque of the vehicle is determined.

[0099] In an embodiment of the present invention, the vehicle is configured with an automatic regenerative braking mode. In this mode, if the driver releases the accelerator pedal, the vehicle automatically assists the driver in making decisions. For example, the vehicle automatically determines the appropriate deceleration rate based on the vehicle ahead and then adjusts the deceleration based on the driver's driving style.

[0100] Deceleration can be used to characterize the energy regeneration intensity. In other words, the energy regeneration intensity is physically equivalent to deceleration. When the vehicle decelerates quickly, the deceleration is greater, and the energy regeneration intensity is correspondingly greater; when the vehicle decelerates slowly, the deceleration is smaller, and the energy regeneration intensity is correspondingly smaller.

[0101] The energy recovery intensity is controlled by the vehicle's output reverse torque. When the vehicle outputs a larger reverse torque, the energy recovery intensity is correspondingly larger; when the vehicle outputs a smaller reverse torque, the energy recovery intensity is correspondingly smaller.

[0102] Therefore, when the vehicle is in the automatic control energy recovery intensity mode, if it is detected that the decelerator pedal is released, the first reverse torque of the vehicle can be determined first, so that the first reverse torque can be corrected later to control the energy recovery intensity of the vehicle.

[0103] In an optional embodiment of the present invention, step 101 may include the following sub-steps:

[0104] Sub-step S11, when the vehicle is in the automatic control energy recovery intensity mode, if it is detected that the accelerator pedal is released, calculating the initial energy recovery intensity of the vehicle based on the distance between the vehicle and the vehicle ahead, the speed of the vehicle, and the speed of the vehicle ahead;

[0105] Sub-step S12, collecting vehicle parameters of the vehicle;

[0106] Sub-step S13: determining a first reverse torque of the vehicle according to the initial energy recovery intensity and the vehicle parameters.

[0107] During driving, the vehicle's autonomous driving controller can detect the distance between the vehicle and the vehicle ahead, the speed of the vehicle ahead, and the vehicle's electronic stability control system based on radar or cameras. The vehicle's speed can be determined by detecting the vehicle ahead by sending two waves from the vehicle's radar after the vehicle ahead enters the vehicle's radar detection range. When both waves are received, the vehicle can calculate the vehicle ahead's speed using the time difference.

[0108] The distance between the vehicle and the vehicle ahead, the vehicle's speed, and the speed of the vehicle ahead can be detected in real time. When the vehicle is in automatic control of energy recovery intensity mode and the accelerator pedal is detected to be released, the distance between the vehicle and the vehicle ahead, the vehicle's speed, and the speed of the vehicle ahead can be obtained, and the vehicle's initial energy recovery intensity can be calculated based on these parameters. Specifically, the vehicle's initial energy recovery intensity can be calculated using the following kinematic formula:

[0109]

[0110] Where a represents the vehicle's deceleration, i.e., the vehicle's initial energy recovery intensity, v1 represents the vehicle's speed, v2 represents the speed of the vehicle ahead, and s 12 Indicates the distance between the vehicle and the vehicle in front, s0 indicates the safety distance, which is the minimum distance to prevent the vehicle from colliding with the vehicle in front. The safety distance can be a fixed value.

[0111] At this time, the vehicle can collect vehicle parameters and then determine the first reverse torque of the vehicle by combining the initial energy recovery intensity with the vehicle parameters. The vehicle parameters may include at least one of vehicle weight, reduction ratio, tire radius, and motor speed.

[0112] Before calculating the vehicle's initial energy recovery intensity, the vehicle can first determine whether energy recovery is necessary. Specifically, when the vehicle is in automatic energy recovery intensity control mode and the accelerator pedal is detected to be released, the distance between the vehicle and the vehicle ahead can be compared with a distance threshold. If the distance between the vehicle and the vehicle ahead is greater than the distance threshold, it can be determined that there are no other vehicles ahead of the vehicle or that the vehicle has moved away from the vehicle ahead. In this case, the vehicle does not need to decelerate, that is, the vehicle's first reverse torque is 0, and energy recovery is not required. If the distance between the vehicle and the vehicle ahead is less than or equal to the distance threshold, it can be determined that the vehicle is approaching the vehicle ahead and deceleration is required. In this case, the vehicle's first reverse torque cannot be 0, and energy recovery is required.

[0113] When the vehicle approaches the vehicle ahead, the vehicle's deceleration can be increased until it slows down to the same speed as the vehicle ahead before reaching a safe distance.

[0114] In addition, to ensure that the vehicle detects the accelerator pedal being released while in motion, the vehicle's vehicle controller can detect whether the gear is in D and whether the driver has fully released the accelerator pedal. D refers to the driving gear, and being in driving gear indicates that the vehicle is in motion. When it is detected that the current gear is in driving gear and the driver has fully released the accelerator pedal, the vehicle can further determine whether energy recovery is required. If energy recovery is not required, the vehicle's first reverse torque is determined to be 0; if energy recovery is required, the vehicle's initial energy recovery intensity is calculated based on the distance between the vehicle and the vehicle ahead, the vehicle's speed, and the speed of the vehicle ahead. The vehicle's first reverse torque is determined based on the initial energy recovery intensity and vehicle parameters.

[0115] Step 102 : Acquire driving style information for the driver; the driving style information is obtained by deep learning based on the driver's driving habit data.

[0116] Different drivers have different driving styles, so the calculated first reverse torque may not necessarily meet the driver's deceleration needs. That is, using the first reverse torque for deceleration may not conform to the driver's driving habits, which may cause the driver to intervene more in the vehicle's deceleration process. Therefore, after determining the vehicle's first reverse torque, the driver's driving style information can be obtained, and the final target reverse torque can be obtained by combining the driving style information. Since the driving style information is obtained through deep learning based on the driver's driving habit data, the final target reverse torque can be more in line with the driver's driving habits.

[0117] The deep learning task can be completed by a machine learning system. The machine learning system can be deployed on a vehicle or on a cloud platform. This is not limited in the embodiments of the present invention.

[0118] In an optional embodiment of the present invention, step 102 may include the following sub-steps:

[0119] Sub-step S21, obtaining the driver's driving habit data;

[0120] Sub-step S22, generating an actual deceleration curve for the driver based on the driving habit data;

[0121] Sub-step S23, fitting the actual deceleration curve with a preset deceleration curve;

[0122] Sub-step S24: generating driving style information for the driver according to the fitting result.

[0123] When the machine learning system is deployed in a vehicle, the vehicle can regularly obtain the driver's driving habit data within a preset time period, and then input the driving habit data into the machine learning system. The machine learning system performs deep learning based on the driving habit data to obtain driving style information for the driver. The vehicle stores the driving style information regularly. When the driving style information is needed, the vehicle can obtain the corresponding driving style information based on the timestamp.

[0124] When the machine learning system is deployed on a cloud platform, the vehicle can regularly obtain the driver's driving habit data within a preset time period, and then regularly upload the driving habit data to the cloud platform. The cloud platform inputs the driving habit data into the machine learning system. The machine learning system performs deep learning based on the driving habit data to obtain driving style information for the driver. The cloud platform regularly transmits the driving style information to the vehicle, and the vehicle regularly stores the driving style information. When the driving style information needs to be obtained, the vehicle can obtain the corresponding driving style information based on the timestamp.

[0125] In a machine learning system, an actual deceleration curve for the driver can be generated based on driving habit data, and then the actual deceleration curve can be fitted with a preset deceleration curve to generate driving style information for the driver based on the fitting result.

[0126] In an optional embodiment of the present invention, sub-step S21 may include the following sub-steps:

[0127] Sub-step S211, when the vehicle is in the manual control energy recovery intensity mode, collecting the accelerator pedal opening;

[0128] Sub-step S212, determining a second reverse torque of the vehicle according to the accelerator pedal opening;

[0129] Sub-step S213, controlling the energy recovery intensity of the vehicle using the second reverse torque;

[0130] Sub-step S214 , during the deceleration process based on the second reverse torque, collecting the driver's driving habit data; the driving habit data includes the distance between the vehicle and the vehicle in front, the speed of the vehicle, and the speed of the vehicle in front.

[0131] In this embodiment of the present invention, the vehicle is equipped with a manual control mode for energy recovery intensity. This indicates that the vehicle has two energy recovery modes: manual control mode and automatic control mode. Manual control mode allows the driver to make decisions on energy recovery intensity, while automatic control mode allows the vehicle to assist the driver in making decisions. Driving habit data refers to actual vehicle usage data generated by the driver repeatedly decelerating the vehicle while in manual control mode.

[0132] In other words, manual regenerative mode is activated before automatic regenerative mode. Typically, when a vehicle leaves the factory or is delivered to its individual user (driver), no data about the driver's driving habits is available. Therefore, automatic regenerative mode is unavailable, and the driver must activate manual regenerative mode. Automatic regenerative mode becomes available only after the machine learning system has learned sufficient driving habit data.

[0133] When the vehicle is in manual regenerative mode, the accelerator pedal opening can be measured. This represents the driver's accelerator pedal input. Accelerator pedal opening is negatively correlated with deceleration: smaller accelerator pedal opening increases vehicle deceleration, while larger accelerator pedal opening decreases vehicle deceleration.

[0134] The vehicle can determine a second reverse torque based on the accelerator pedal position. Specifically, the vehicle can calculate the second reverse torque based on the accelerator pedal position and the vehicle speed using a table lookup. The vehicle's motor controller can then control the motor to output the second reverse torque, thereby decelerating the vehicle based on the second reverse torque and recovering energy during deceleration, thereby controlling the vehicle's energy recovery intensity. This shows that the driver can adjust the vehicle's deceleration within a certain range by adjusting the accelerator pedal position.

[0135] During the deceleration process based on the second reverse torque, the vehicle can collect the driver's driving habit data, which may include the distance between the vehicle and the vehicle in front, the speed of the vehicle, the speed of the vehicle in front, and the accelerator pedal opening.

[0136] Compared with the prior art which controls the energy recovery intensity of the vehicle based on the vehicle in front, the manual control energy recovery intensity mode of the embodiment of the present invention controls the energy recovery intensity of the vehicle based on the input of the driver stepping on the accelerator pedal (accelerator pedal opening), which is unrelated to the vehicle in front. Therefore, the manual control energy recovery intensity mode can be more in line with the driver's driving habits.

[0137] In an optional embodiment of the present invention, sub-step S22 may include the following sub-steps:

[0138] Sub-step S221, determining a speed difference between the vehicle and the preceding vehicle based on the speed of the vehicle and the preceding vehicle;

[0139] Sub-step S222, establishing a relationship curve with the distance between the vehicle and the preceding vehicle as the horizontal coordinate and the speed difference between the vehicle and the preceding vehicle as the vertical coordinate;

[0140] Sub-step S223: determining the relationship curve as an actual deceleration curve for the driver.

[0141] After the driving habit data is input into the machine learning system, the machine learning system can determine the speed difference between the vehicle and the vehicle in front based on the speed of the vehicle and the speed of the vehicle in front, and then establish a relationship curve with the distance between the vehicle and the vehicle in front as the horizontal coordinate and the speed difference between the vehicle and the vehicle in front as the vertical coordinate. This relationship curve is the actual deceleration curve for the driver.

[0142] In an optional embodiment of the present invention, the actual deceleration curve includes a plurality of deceleration curves, the preset deceleration curve includes a plurality of deceleration curves, and each preset deceleration curve corresponds to a preset driving style; sub-step S23 may include the following sub-steps:

[0143] Sub-step S231 : fitting each actual deceleration curve with each preset deceleration curve.

[0144] Driving habit data is actual vehicle usage data generated by the driver's multiple attempts to decelerate the vehicle within a preset time period. Therefore, the driving habit data contains multiple data. Each driving habit data corresponds to an actual deceleration curve. Therefore, the actual deceleration curve generated by the machine learning system contains multiple data.

[0145] In an embodiment of the present invention, multiple preset deceleration curves can be pre-set, wherein the horizontal and vertical coordinates of the preset deceleration curve are consistent with the actual deceleration curve, that is, the horizontal coordinate of the preset deceleration curve is the distance between the vehicle and the vehicle in front, and the vertical coordinate of the preset deceleration curve is the speed difference between the vehicle and the vehicle in front.

[0146] Each preset deceleration curve may correspond to a preset driving style, and the preset driving styles may include a general style, an aggressive style, and a conservative style. That is, the preset deceleration curves include three, namely, a preset deceleration curve for a general style, a preset deceleration curve for an aggressive style, and a preset deceleration curve for a conservative style.

[0147] In one example, referring to Figure 2 , shows a general style curve diagram provided by an embodiment of the present invention. The characteristic of a general style preset deceleration curve is a constant slope. The slope can be used to represent deceleration when the speed of the preceding vehicle is constant. Specifically, when the speed of the preceding vehicle is constant, the vehicle decelerates uniformly as the distance from the preceding vehicle decreases until it reaches the same speed as the preceding vehicle.

[0148] In one example, referring to Figure 3 , shows an aggressive style curve diagram provided by an embodiment of the present invention. The characteristic of the preset deceleration curve for an aggressive style is that its slope gradually increases. Specifically, when the distance between the vehicle and the vehicle ahead is greater than a first preset distance ΔS1 and the speed difference between the vehicle and the vehicle ahead is greater than a first preset speed difference ΔV1, the slope is less than 1. When the distance between the vehicle and the vehicle ahead is less than or equal to the first preset distance ΔS1 and the speed difference between the vehicle and the vehicle ahead is less than or equal to the first preset speed difference ΔV1, the slope is greater than 1. The slope can be used to represent deceleration when the speed of the vehicle ahead is constant. That is, when the speed of the vehicle ahead is constant, as the distance from the vehicle ahead decreases, the vehicle first slows down, then speeds up, until it reaches the same speed as the vehicle ahead.

[0149] In one example, referring to Figure 4, shows a conservative style curve diagram provided by an embodiment of the present invention. The characteristic of the conservative style preset deceleration curve is that the slope changes from large to small. Specifically, when the distance between the vehicle and the vehicle ahead is greater than the second preset distance ΔS2 and the speed difference between the vehicle and the vehicle ahead is greater than the second preset speed difference ΔV2, the slope is greater than 1; when the distance between the vehicle and the vehicle ahead is less than or equal to the second preset distance ΔS2 and the speed difference between the vehicle and the vehicle ahead is less than or equal to the second preset speed difference ΔV2, the slope is less than 1. The slope can be used to represent deceleration when the speed of the vehicle ahead is constant. That is, when the speed of the vehicle ahead is constant, as the distance from the vehicle ahead decreases, the vehicle first decelerates faster, then decelerates slower, until it reaches the same speed as the vehicle ahead.

[0150] The machine learning system can fit each actual deceleration curve to each preset deceleration curve. For example, assuming there are 10 actual deceleration curves A to J, and the preset deceleration curves include three preset deceleration curves: a general style deceleration curve, an aggressive style deceleration curve, and a conservative style deceleration curve, then the machine learning system can fit the actual deceleration curve A to the preset deceleration curves of the general style, the aggressive style, and the conservative style, respectively, and can fit the actual deceleration curve B to the preset deceleration curves of the general style, the aggressive style, and the conservative style, respectively, ... (the actual deceleration curves C to I are omitted here), and can fit the actual deceleration curve J to the preset deceleration curves of the general style, the aggressive style, and the conservative style, respectively. Thus, 3*10 fitting results can be obtained, and driving style information for the driver can be generated based on these 30 fitting results.

[0151] In an optional embodiment of the present invention, sub-step S24 may include the following sub-steps:

[0152] Sub-step S241, determining, from a plurality of preset deceleration curves, a target preset deceleration curve whose degree of fit with the actual deceleration curves satisfies a preset condition;

[0153] Sub-step S242 : generating driving style information specific to the driver using the preset driving style corresponding to the target preset deceleration curve.

[0154] Each actual deceleration curve can correspond to multiple fitting results. That is, if there are three preset deceleration curves, each actual deceleration curve can correspond to three fitting results. Then, based on the fitting results, a target preset deceleration curve can be determined from the multiple preset deceleration curves, whose fit with each actual deceleration curve meets preset conditions. Since each preset deceleration curve can correspond to a preset driving style, after finding a target preset deceleration curve that meets the preset conditions, the preset driving style corresponding to the target preset deceleration curve can be used to generate driving style information tailored to the driver.

[0155] In a specific implementation, the machine learning system can determine whether each fitting result for each actual deceleration curve is within the error range (0-Y%). Y% is a very small value, so for the same actual deceleration curve, no more than two fitting results will be within the error range. In other words, an actual deceleration curve may have one fitting result within the error range, or multiple fitting results may not be within the error range.

[0156] If the fit result between an actual deceleration curve and one of the preset deceleration curves is within the error range, then the driving style associated with the actual deceleration curve can be determined to be within the preset driving style for that preset deceleration curve. For example, if the fit result between actual deceleration curve A and the preset aggressive deceleration curve is within the error range, then the driving style associated with actual deceleration curve A can be determined to be aggressive.

[0157] If the fitting results of an actual deceleration curve and all preset deceleration curves are within the error range, it can be determined that the driving style of the actual deceleration curve does not belong to any of the preset driving styles, that is, the driving style of the actual deceleration curve is undefined. For example, if the fitting results of actual deceleration curve B and the preset deceleration curves for three driving styles are all within the error range, the driving style of actual deceleration curve B can be determined to be undefined.

[0158] After determining the driving style of each actual deceleration curve (general style / aggressive style / conservative style / undefinable style), the machine learning system can determine the proportion of each driving style. Specifically, if there are 10 actual deceleration curves, of which 0 actual deceleration curves belong to the general style, 8 actual deceleration curves belong to the aggressive style, 1 actual deceleration curve belongs to the conservative style, and 1 actual deceleration curve belongs to the undefinable style, then the proportion of the general style is 0, the proportion of the aggressive style is 8 / 10, the proportion of the conservative style is 1 / 10, and the proportion of the undefinable style is 1 / 10. It should be noted that the actual deceleration curve belonging to the undefinable style is used as the denominator and cannot be eliminated.

[0159] After determining the proportion of each driving style, the proportion of each driving style can be compared with a preset proportion A%. A% is a higher threshold, such as 70%, so that only one driving style has a proportion greater than the preset proportion, or all driving styles have a proportion less than the preset proportion.

[0160] If the proportion of a certain driving style exceeds a preset proportion, the preset deceleration curve for that driving style can be determined to be the final target preset deceleration curve, and thus, that driving style can be used to generate driving style information specific to the driver. For example, assuming the preset proportion is 70%, if the proportion of the aggressive style is 8 / 10, or 80%, then the proportion of the aggressive style exceeds the preset proportion, and therefore, the aggressive style can be used to generate driving style information specific to the driver.

[0161] If the proportion of any driving style is less than a preset proportion, it indicates that the driver's habits are likely variable. Since no single driving style can meet the driver's needs in most scenarios, it is better to select a moderate style (general style) that minimizes deviation from the driver's actual style. Therefore, when the proportion of any driving style is less than the preset proportion, the predetermined deceleration curve for the general style can be directly determined as the final target predetermined deceleration curve, and the general style can be used to generate driving style information specific to the driver. For example, assuming the preset proportion is 70%, if the proportion of the general style is 0, the proportion of the aggressive style is 30%, the proportion of the conservative style is 20%, and the proportion of the undefined style is 50%, then it can be determined that the proportions corresponding to the general style, aggressive style, conservative style, and undefined style are all less than the preset proportion. Although no actual deceleration curve belongs to the general style, because the driver's habits are variable, the general style can be directly used to generate driving style information specific to the driver.

[0162] Step 103 : Using the driving style information, correct the first reverse torque to obtain a target reverse torque.

[0163] After the driving style information of the driver is acquired, the driving style information may be used to correct the first reverse torque calculated previously, thereby obtaining the target reverse torque.

[0164] Compared with the prior art which controls the energy recovery intensity of the vehicle based on the vehicle in front, the automatic control energy recovery intensity mode of the embodiment of the present invention adds driving style information to the first reverse torque for correction, thereby obtaining the target reverse torque. Since the driving style information is obtained through deep learning based on the driver's driving habit data, the energy recovery intensity of the vehicle is controlled based on the target reverse torque, so that the automatic control energy recovery intensity mode can be more in line with the driver's driving habits.

[0165] In an optional embodiment of the present invention, step 103 may include the following sub-steps:

[0166] Sub-step S31, determining the target driving style of the driver according to the driving style information;

[0167] Sub-step S32, determining a target driving style coefficient according to the target driving style;

[0168] Sub-step S33 : calculating a product value of the first reverse torque and the target driving style coefficient, and determining the product value as a target reverse torque.

[0169] The driving style information can be used to indicate the driver's driving style. Therefore, based on the driving style information, the driver's target driving style can be determined. Then, based on the target driving style, a target driving style coefficient K can be determined. Different driving styles have different driving style coefficients K.

[0170] After the target driving style coefficient K is determined, the product value of the first reverse torque and the target driving style coefficient K may be calculated, and the product value is the final target reverse torque.

[0171] In an optional embodiment of the present invention, the target driving style includes a general driving style; sub-step S32 may include the following sub-steps:

[0172] Sub-step S3211: When the target driving style is a general style, determining the target driving style coefficient as a first coefficient; wherein the first coefficient is 1.

[0173] The target driving style coefficient K of the general driving style is the first coefficient. The first coefficient can be set to 1, that is, the value of the target driving style coefficient K of the general driving style can be always 1.

[0174] The driving style coefficient K may correspond to the slope of the preset deceleration curve. Figure 2As shown, the slope of the preset deceleration curve for the general driving style is always 1. Accordingly, the value of the target driving style coefficient K for the general driving style is also always 1. Therefore, when the target driving style is the general driving style, the vehicle decelerates uniformly as the distance to the vehicle ahead decreases until it reaches the same speed as the vehicle ahead.

[0175] In an optional embodiment of the present invention, the target driving style includes an aggressive style; sub-step S32 may include the following sub-steps:

[0176] Sub-step S3221: when the target driving style is an aggressive style, and the distance between the vehicle and the preceding vehicle is greater than a first preset distance, and the speed difference between the vehicle and the preceding vehicle is greater than the first preset speed difference, determining the target driving style coefficient to be a second coefficient; wherein the second coefficient is less than 1;

[0177] Sub-step S3222, until the distance between the vehicle and the vehicle ahead is less than or equal to the first preset distance, and the speed difference between the vehicle and the vehicle ahead is less than or equal to the first preset speed difference, determine the target driving style coefficient as a third coefficient; wherein the third coefficient is greater than 1.

[0178] The target driving style coefficient K for the aggressive style includes two coefficients, namely a second coefficient and a third coefficient. The second coefficient can be set to be less than 1, and the third coefficient can be set to be greater than 1.

[0179] In an embodiment of the present invention, a target driving style coefficient K for the aggressive style can be obtained by looking up a table based on the distance between the vehicle and the vehicle ahead and the speed difference between the vehicle and the vehicle ahead: when the distance between the vehicle and the vehicle ahead is greater than a first preset distance ΔS1 and the speed difference between the vehicle and the vehicle ahead is greater than a first preset speed difference ΔV1, the target driving style coefficient can be determined to be the second coefficient (K<1); until the distance between the vehicle and the vehicle ahead is less than or equal to the first preset distance ΔS1 and the speed difference between the vehicle and the vehicle ahead is less than or equal to the first preset speed difference ΔV1, the target driving style coefficient can be determined to be the third coefficient (K>1).

[0180] The driving style coefficient K may correspond to the slope of the preset deceleration curve. Figure 3 As shown, the slope of the pre-set deceleration curve for the aggressive driving style increases from small to large, and accordingly, the value of the target driving style coefficient K for the aggressive driving style also increases from small to large. Therefore, when the target driving style is aggressive, the vehicle first decelerates, then accelerates, as the distance to the vehicle ahead decreases, until it reaches the same speed as the vehicle ahead.

[0181] In an optional embodiment of the present invention, the target driving style includes a conservative style; sub-step S32 may include the following sub-steps:

[0182] Sub-step S3231: when the target driving style is a conservative style, and the distance between the vehicle and the preceding vehicle is greater than a second preset distance, and the speed difference between the vehicle and the preceding vehicle is greater than the second preset speed difference, determining the target driving style coefficient to be a fourth coefficient; wherein the fourth coefficient is greater than 1;

[0183] Sub-step S3232, until the distance between the vehicle and the vehicle ahead is less than or equal to the second preset distance, and the speed difference between the vehicle and the vehicle ahead is less than or equal to the second preset speed difference, determine the target driving style coefficient as the fifth coefficient; wherein the fifth coefficient is less than 1.

[0184] The conservative target driving style coefficient K includes two coefficients, namely a fourth coefficient and a fifth coefficient. The fourth coefficient can be set to be greater than 1, and the fifth coefficient can be set to be less than 1.

[0185] In an embodiment of the present invention, a target driving style coefficient K for the conservative style can be obtained by looking up a table based on the distance between the vehicle and the vehicle ahead and the speed difference between the vehicle and the vehicle ahead: when the distance between the vehicle and the vehicle ahead is greater than a second preset distance ΔS2, and the speed difference between the vehicle and the vehicle ahead is greater than the second preset speed difference ΔV2, the target driving style coefficient can be determined to be the fourth coefficient (K>1); until the distance between the vehicle and the vehicle ahead is less than or equal to the second preset distance ΔS2, and the speed difference between the vehicle and the vehicle ahead is less than or equal to the second preset speed difference ΔV2, the target driving style coefficient can be determined to be the fifth coefficient (K<1).

[0186] The driving style coefficient K may correspond to the slope of the preset deceleration curve. Figure 4 As shown, the slope of the conservative deceleration curve decreases from high to low, and accordingly, the value of the conservative target driving style coefficient K also decreases. Therefore, when the target driving style is conservative, the vehicle first decelerates faster, then slower, as the distance to the vehicle ahead decreases, until it reaches the same speed as the vehicle ahead.

[0187] Step 104 : Using the target reverse torque to control the energy recovery intensity of the vehicle.

[0188] After obtaining the target reverse torque, the vehicle's motor controller can control the motor to output the target reverse torque, and then the vehicle decelerates based on the target reverse torque, recovering energy during the deceleration process, thereby controlling the vehicle's energy recovery intensity.

[0189] In an embodiment of the present invention, when a vehicle is in an automatic regenerative braking intensity control mode and the accelerator pedal is released, a first reverse torque of the vehicle is determined; driving style information specific to the driver is obtained; the driving style information is obtained through deep learning based on the driver's driving habit data; the first reverse torque is modified using the driving style information to obtain a target reverse torque; and the target reverse torque is used to control the regenerative braking intensity of the vehicle. This embodiment of the present invention allows the driver to simply release the accelerator pedal, and the vehicle automatically modifies the reverse torque based on the driver's driving style, thereby controlling the regenerative braking intensity of the vehicle, thereby meeting the driver's deceleration requirements and reducing the frequency of driver intervention in the vehicle's deceleration process due to driving style inconsistencies with the preset regenerative braking intensity.

[0190] Reference Figure 5 , shows a structural block diagram of an energy recovery intensity control device provided in an embodiment of the present invention, which may specifically include the following modules:

[0191] A first reverse torque determination module 501 is configured to determine a first reverse torque of the vehicle when it is detected that the accelerator pedal is released while the vehicle is in an automatic energy recovery intensity control mode;

[0192] A driving style information acquisition module 502 is configured to acquire driving style information specific to a driver; the driving style information is acquired through deep learning based on the driver's driving habit data;

[0193] a first reverse torque correction module 503 for correcting the first reverse torque using the driving style information to obtain a target reverse torque;

[0194] The energy recovery intensity control module 504 is configured to control the energy recovery intensity of the vehicle using the target reverse torque.

[0195] In an optional embodiment of the present invention, the first reverse torque determination module 501 may include:

[0196] an initial energy recovery intensity calculation submodule, configured to calculate the vehicle's initial energy recovery intensity based on the distance between the vehicle and a preceding vehicle, the vehicle's speed, and the preceding vehicle's speed when the accelerator pedal is detected to be released while the vehicle is in an automatic energy recovery intensity control mode;

[0197] A vehicle parameter acquisition submodule, configured to acquire vehicle parameters of the vehicle;

[0198] The first reverse torque determination submodule is configured to determine a first reverse torque of the vehicle according to the initial energy recovery intensity and the vehicle parameters.

[0199] In an optional embodiment of the present invention, the first reverse torque correction module 503 may include:

[0200] a target driving style determination submodule, configured to determine a target driving style of the driver based on the driving style information;

[0201] a target driving style coefficient determination submodule, configured to determine a target driving style coefficient according to the target driving style;

[0202] The target reverse torque determination submodule is configured to calculate a product value of the first reverse torque and the target driving style coefficient, and determine the product value as the target reverse torque.

[0203] In an optional embodiment of the present invention, the target driving style includes a general style; and the target driving style coefficient determination submodule may include:

[0204] The first target driving style coefficient determining unit is configured to determine the target driving style coefficient as a first coefficient when the target driving style is a general style; wherein the first coefficient is 1.

[0205] In an optional embodiment of the present invention, the target driving style includes an aggressive style; and the target driving style coefficient determination submodule may include:

[0206] a second target driving style coefficient determining unit, configured to determine, when the target driving style is an aggressive style and the distance between the vehicle and the preceding vehicle is greater than a first preset distance and the speed difference between the vehicle and the preceding vehicle is greater than the first preset speed difference, the target driving style coefficient to be a second coefficient; wherein the second coefficient is less than 1;

[0207] and a third target driving style coefficient determining unit, configured to determine the target driving style coefficient to be a third coefficient until the distance between the vehicle and the preceding vehicle is less than or equal to a first preset distance and the speed difference between the vehicle and the preceding vehicle is less than or equal to the first preset speed difference; wherein the third coefficient is greater than 1.

[0208] In an optional embodiment of the present invention, the target driving style includes a conservative style; and the target driving style coefficient determination submodule may include:

[0209] a fourth target driving style coefficient determining unit, configured to, when the target driving style is a conservative style, determine the target driving style coefficient to be a fourth coefficient when the distance between the vehicle and the preceding vehicle is greater than a second preset distance and the speed difference between the vehicle and the preceding vehicle is greater than the second preset speed difference; wherein the fourth coefficient is greater than 1;

[0210] and a fifth target driving style coefficient determining unit, configured to determine the target driving style coefficient as a fifth coefficient until the distance between the vehicle and the preceding vehicle is less than or equal to a second preset distance and the speed difference between the vehicle and the preceding vehicle is less than or equal to the second preset speed difference; wherein the fifth coefficient is less than 1.

[0211] In an optional embodiment of the present invention, the driving style information acquisition module 502 may include:

[0212] A driving habit data acquisition submodule, configured to acquire the driver's driving habit data;

[0213] an actual deceleration curve generating submodule, configured to generate an actual deceleration curve for the driver based on the driving habit data;

[0214] A fitting submodule, configured to fit the actual deceleration curve with a preset deceleration curve;

[0215] The driving style information generating submodule is configured to generate driving style information for the driver according to the fitting result.

[0216] In an optional embodiment of the present invention, the driving habit data acquisition submodule may include:

[0217] An accelerator pedal opening acquisition unit, used to acquire the accelerator pedal opening when the vehicle is in a manual control energy recovery intensity mode;

[0218] a second reverse torque determining unit, configured to determine a second reverse torque of the vehicle according to the accelerator pedal opening;

[0219] an energy recovery intensity control unit, configured to control the energy recovery intensity of the vehicle using the second reverse torque;

[0220] A driving habit data collection unit is used to collect the driver's driving habit data during the deceleration process based on the second reverse torque; the driving habit data includes the distance between the vehicle and the vehicle in front, the speed of the vehicle, and the speed of the vehicle in front.

[0221] In an optional embodiment of the present invention, the actual deceleration curve generation submodule may include:

[0222] a speed difference determining unit, configured to determine a speed difference between the vehicle and the preceding vehicle based on the speed of the vehicle and the speed of the preceding vehicle;

[0223] a relationship curve establishing unit, configured to establish a relationship curve with the distance between the vehicle and the preceding vehicle as the abscissa and the speed difference between the vehicle and the preceding vehicle as the ordinate;

[0224] The actual deceleration curve determining unit is configured to determine the relationship curve as an actual deceleration curve for the driver.

[0225] In an optional embodiment of the present invention, the actual deceleration curve includes a plurality of curves, the preset deceleration curve includes a plurality of curves, and each preset deceleration curve corresponds to a preset driving style; the fitting submodule may include:

[0226] A fitting unit, used for fitting each actual deceleration curve with each preset deceleration curve;

[0227] The driving style information generation submodule may include:

[0228] a target preset deceleration curve determining unit, configured to determine, from a plurality of preset deceleration curves, a target preset deceleration curve whose degree of fit with the actual deceleration curves satisfies a preset condition;

[0229] The driving style information generating unit is configured to generate driving style information for the driver by using a preset driving style corresponding to the target preset deceleration curve.

[0230] In an embodiment of the present invention, when a vehicle is in an automatic regenerative braking intensity control mode and the accelerator pedal is released, a first reverse torque of the vehicle is determined; driving style information specific to the driver is obtained; the driving style information is obtained through deep learning based on the driver's driving habit data; the first reverse torque is modified using the driving style information to obtain a target reverse torque; and the target reverse torque is used to control the regenerative braking intensity of the vehicle. This embodiment of the present invention allows the driver to simply release the accelerator pedal, and the vehicle automatically modifies the reverse torque based on the driver's driving style, thereby controlling the regenerative braking intensity of the vehicle, thereby meeting the driver's deceleration requirements and reducing the frequency of driver intervention in the vehicle's deceleration process due to driving style inconsistencies with the preset regenerative braking intensity.

[0231] As for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0232] An embodiment of the present invention also provides a vehicle comprising a memory and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by one or more processors, and the one or more programs include a control method for executing the energy recovery intensity as described in any one of the embodiments of the present invention.

[0233] An embodiment of the present invention further provides a readable storage medium. When instructions in the storage medium are executed by a processor of an electronic device, the electronic device can execute the energy recovery intensity control method as described in any one of the embodiments of the present invention.

[0234] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0235] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, apparatus, or computer program products. Thus, embodiments of the present invention may take the form of a fully hardware embodiment, a fully software embodiment, or an embodiment combining software and hardware. Furthermore, embodiments of the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0236] The embodiments of the present invention are described with reference to the flowcharts and / or block diagrams of the methods, terminal devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing terminal device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate instructions for implementing the process in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0237] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing terminal device to operate in a specific manner, so that the instructions stored in the computer readable memory produce a manufactured product including an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0238] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device so that a series of operating steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable terminal device to implement the process. Figure 1 a process or multiple processes and / or boxes Figure 1A step that specifies a function in one or more boxes.

[0239] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they become aware of the basic creative concepts. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention.

[0240] Finally, it should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or terminal device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or terminal device that includes the element.

[0241] The above is a detailed introduction to the energy recovery intensity control method, device, vehicle and readable storage medium provided by the present invention. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for those skilled in the art, according to the ideas of the present invention, there will be changes in the specific implementation methods and application scopes. In summary, the contents of this specification should not be understood as limiting the present invention.

Claims

1. A method for controlling energy recovery intensity, characterized in that: The method comprises: When the vehicle is in an automatic energy recovery intensity control mode, if it is detected that the accelerator pedal is released, determining a first reverse torque of the vehicle; Acquiring driving style information for a driver; the driving style information is obtained through deep learning based on the driver's driving habit data, and the driving style information is used to determine a target driving style for the driver, where different target driving styles correspond to different target driving style coefficients; Using the driving style information, correcting the first reverse torque to obtain a target reverse torque; controlling the energy recovery intensity of the vehicle by using the target reverse torque; The obtaining of the driving style information of the driver includes: Acquiring driving habit data of the driver; generating an actual deceleration curve for the driver based on the driving habit data; fitting the actual deceleration curve to a preset deceleration curve, wherein the preset deceleration curve corresponds to a preset driving style, and the slope of the preset deceleration curve corresponds to a driving style coefficient; Driving style information for the driver is generated according to the fitting result.

2. The method according to claim 1, characterized in that The method of determining a first reverse torque of the vehicle when the vehicle is in an automatic energy recovery intensity control mode and detecting that the accelerator pedal is released comprises: When the vehicle is in the automatic control energy recovery intensity mode, if it is detected that the accelerator pedal is released, the initial energy recovery intensity of the vehicle is calculated based on the distance between the vehicle and the vehicle ahead, the speed of the vehicle, and the speed of the vehicle ahead; collecting vehicle parameters of the vehicle; A first reverse torque of the vehicle is determined according to the initial energy recovery intensity and the vehicle parameters.

3. The method according to claim 1, characterized in that The modifying the first reverse torque by using the driving style information to obtain a target reverse torque includes: determining a target driving style of the driver according to the driving style information; determining a target driving style coefficient according to the target driving style; A product value of the first reverse torque and the target driving style coefficient is calculated, and the product value is determined as a target reverse torque.

4. The method according to claim 3, wherein the target driving style comprises a general driving style; and determining a target driving style coefficient according to the target driving style comprises: When the target driving style is a general style, the target driving style coefficient is determined to be a first coefficient; wherein the first coefficient is 1.

5. The method according to claim 3, wherein the target driving style comprises an aggressive style; and determining a target driving style coefficient according to the target driving style comprises: When the target driving style is an aggressive style, and the distance between the vehicle and the preceding vehicle is greater than a first preset distance, and the speed difference between the vehicle and the preceding vehicle is greater than the first preset speed difference, determining the target driving style coefficient to be a second coefficient; wherein the second coefficient is less than 1; Until the distance between the vehicle and the vehicle ahead is less than or equal to a first preset distance and the speed difference between the vehicle and the vehicle ahead is less than or equal to the first preset speed difference, the target driving style coefficient is determined to be a third coefficient; wherein the third coefficient is greater than 1.

6. The method according to claim 3, wherein the target driving style comprises a conservative style; and determining a target driving style coefficient according to the target driving style comprises: When the target driving style is a conservative style, and the distance between the vehicle and the preceding vehicle is greater than a second preset distance, and the speed difference between the vehicle and the preceding vehicle is greater than the second preset speed difference, determining the target driving style coefficient to be a fourth coefficient; wherein the fourth coefficient is greater than 1; Until the distance between the vehicle and the vehicle ahead is less than or equal to a second preset distance and the speed difference between the vehicle and the vehicle ahead is less than or equal to a second preset speed difference, the target driving style coefficient is determined to be a fifth coefficient; wherein the fifth coefficient is less than 1.

7. The method according to claim 1, characterized in that The obtaining of the driver's driving habit data includes: When the vehicle is in manual control energy recovery intensity mode, collect the accelerator pedal opening; determining a second reverse torque of the vehicle according to the accelerator pedal opening; controlling the energy recovery intensity of the vehicle by using the second reverse torque; During the deceleration process based on the second reverse torque, the driver's driving habit data is collected; the driving habit data includes the distance between the vehicle and a preceding vehicle, the speed of the vehicle, and the speed of the preceding vehicle.

8. The method according to claim 7, characterized in that Generating an actual deceleration curve for the driver based on the driving habit data includes: determining a speed difference between the vehicle and the preceding vehicle based on the speed of the vehicle and the speed of the preceding vehicle; Establishing a relationship curve with the distance between the vehicle and the preceding vehicle as the abscissa and the speed difference between the vehicle and the preceding vehicle as the ordinate; The relationship curve is determined as an actual deceleration curve for the driver.

9. The method according to claim 1, characterized in that The actual deceleration curves include a plurality of deceleration curves, the preset deceleration curves include a plurality of deceleration curves, and each preset deceleration curve corresponds to a preset driving style; The fitting of the actual deceleration curve with a preset deceleration curve includes: Fitting each actual deceleration curve with each preset deceleration curve; Generating driving style information for the driver according to the fitting result includes: Determining, from a plurality of preset deceleration curves, a target preset deceleration curve whose degree of fit with the actual deceleration curves satisfies a preset condition; The preset driving style corresponding to the target preset deceleration curve is used to generate driving style information for the driver.

10. A control device for energy recovery intensity, characterized in that: The device comprises: a first reverse torque determination module, configured to determine a first reverse torque of the vehicle if it is detected that the accelerator pedal is released when the vehicle is in an automatic energy recovery intensity control mode; a driving style information acquisition module, configured to acquire driving style information specific to a driver; the driving style information is obtained through deep learning based on the driver's driving habit data, and the driving style information is used to determine the driver's target driving style, with different target driving styles corresponding to different target driving style coefficients; a first reverse torque correction module, configured to correct the first reverse torque using the driving style information to obtain a target reverse torque; an energy recovery intensity control module, configured to control the energy recovery intensity of the vehicle using the target reverse torque; The driving style information acquisition module includes: A driving habit data acquisition submodule, configured to acquire the driver's driving habit data; an actual deceleration curve generating submodule, configured to generate an actual deceleration curve for the driver based on the driving habit data; a fitting submodule, configured to fit the actual deceleration curve to a preset deceleration curve, wherein the preset deceleration curve corresponds to a preset driving style, and the slope of the preset deceleration curve corresponds to a driving style coefficient; The driving style information generating submodule is configured to generate driving style information for the driver according to the fitting result.

11. A vehicle, characterized in that: It includes a memory and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by one or more processors, and the one or more programs include a method for executing the energy recovery intensity control method as described in any one of method claims 1-9.

12. A readable storage medium, characterized in that: When the instructions in the storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the energy recovery intensity control method as described in any one of method claims 1-9.

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