Vehicle control method, vehicle, storage medium and program product

Abstract

The invention relates to a vehicle control method, a vehicle, a storage medium and a program product, and relates to the technical field of vehicles, and the method comprises the following steps: obtaining target operation information of the vehicle; and performing fuzzy processing on the target operation information to obtain a target driving state. And determining a target torque according to the target driving state. And feedback torque of a motor of the vehicle is controlled according to the target torque so as to control vehicle braking. The running information of the vehicle is fuzzified to obtain the driving state when the driver drives the vehicle, and the feedback torque during vehicle braking is controlled according to the driving state, so that the feedback torque can adapt to driving styles of different drivers, the strength of braking energy recovery is more flexibly and reasonably controlled, and the braking energy recovery efficiency is improved. And the energy recovery efficiency is improved.

Description

Technical Field

[0001] The present disclosure relates to the field of vehicle technology, and in particular, to a vehicle control method, a vehicle, a storage medium, and a program product. Background Art

[0002] With the continuous development of new energy vehicles, energy recovery in new energy vehicles has also been increasingly valued by society. Braking energy recovery in new energy vehicles is also called regenerative braking, which means that during the braking process of the vehicle, the motor is controlled to work in a power generation state, the mechanical energy of the vehicle is converted into electrical energy, and stored in energy storage systems such as batteries, to achieve a portion of the braking energy recovery; while the motor generates electricity, it will generate braking torque, which is transmitted to the wheels through the transmission system, thereby slowing down the vehicle. However, the current braking energy recovery technology uses the same braking method for different drivers, which cannot adapt to the driving styles of different drivers, reducing the efficiency of energy recovery. Summary of the invention

[0003] The purpose of the present disclosure is to provide a vehicle control method, a vehicle, a storage medium and a program product for improving the efficiency of energy recovery.

[0004] According to a first aspect of an embodiment of the present disclosure, a vehicle control method is provided, the method comprising:

[0005] Obtain target operation information of the vehicle;

[0006] Performing fuzzy processing on the target operation information to obtain a target driving state;

[0007] determining a target torque according to the target driving state;

[0008] The feedback torque of the motor of the vehicle is controlled according to the target torque to control the braking of the vehicle.

[0009] Optionally, the fuzzy processing of the target operation information includes:

[0010] The target operation information is fuzzified according to a preset fuzzy relationship, wherein the preset fuzzy relationship includes a subordinate relationship between the operation information and the driving state.

[0011] Optionally, determining the target torque according to the target driving state includes:

[0012] determining a target driving type of the driver according to a first preset corresponding relationship and the target driving state, wherein the first preset corresponding relationship includes a corresponding relationship between the driving state and the driving type;

[0013] The target torque is determined according to the target driving type.

[0014] Optionally, determining the target torque according to the target driving type includes:

[0015] determining a first candidate torque according to first current operating information and the target driving type;

[0016] Determining the dynamic mass of the vehicle according to the second current operation information, wherein the dynamic mass represents the actual mass of the vehicle in the current operation state;

[0017] The target torque is determined according to the first candidate torque and the dynamic mass.

[0018] Optionally, determining the first candidate torque according to the first current operating information and the target driving type includes:

[0019] determining a second candidate torque according to the first current operating information and a second preset corresponding relationship, wherein the second preset corresponding relationship includes a corresponding relationship between the first current operating information and the second candidate torque;

[0020] The second candidate torque is corrected according to the target driving type to obtain the first candidate torque.

[0021] Optionally, determining the target torque according to the first candidate torque and the dynamic mass includes:

[0022] determining a mass correction factor based on the dynamic mass;

[0023] The first candidate torque is corrected according to the mass correction coefficient to obtain the target torque.

[0024] Optionally, determining a mass correction coefficient according to the dynamic mass includes:

[0025] Obtaining the curb weight of the vehicle, where the curb weight represents the weight of the vehicle when it leaves the factory;

[0026] The ratio of the dynamic mass to the curb mass is used as the mass correction coefficient.

[0027] Optionally, determining the dynamic mass of the vehicle according to the second current operation information includes:

[0028] determining a longitudinal force relationship of the vehicle according to the second current operation information, wherein the longitudinal force relationship includes a dynamic relationship between the longitudinal force and the rolling resistance, wind resistance, ramp resistance and acceleration resistance of the vehicle;

[0029] The dynamic mass is determined based on the longitudinal force relationship through a recursive least squares model.

[0030] Optionally, the acquiring target operation information of the vehicle includes:

[0031] Acquire multiple real-time operation information of the vehicle within a preset time period;

[0032] An average value of the plurality of real-time operation information is used as the target operation information.

[0033] Optionally, the target operation information includes at least one of vehicle speed information, deceleration information and brake pedal opening information, and the driving type includes at least one of conservative, conservative, normal, aggressive and aggressive.

[0034] According to a second aspect of an embodiment of the present disclosure, a vehicle is provided, comprising:

[0035] a memory having a computer program stored thereon;

[0036] A processor is used to execute the computer program in the memory to implement the steps of the method described in the first aspect of the embodiment of the present disclosure.

[0037] According to a third aspect of an embodiment of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method described in the first aspect of the embodiment of the present disclosure are implemented.

[0038] According to a fourth aspect of an embodiment of the present disclosure, a computer program product is provided, including a computer program, which, when executed by a processor, implements the steps of the method described in the first aspect of the embodiment of the present disclosure.

[0039] Through the above technical solution, the method first obtains the target operation information of the vehicle, and performs fuzzy processing on the target operation information to obtain the target driving state. Then, the target driving type of the driver is determined according to the first preset correspondence and the target driving state, wherein the first preset correspondence includes the correspondence between the driving state and the driving type. Finally, the target torque is determined according to the target driving type, and the feedback torque of the vehicle's motor is controlled according to the target torque to control the vehicle braking. The present disclosure performs fuzzy processing on the vehicle's operation information to obtain the driving state of the driver when driving the vehicle, and controls the feedback torque when the vehicle brakes according to the driving state, so that the feedback torque can adapt to the driving styles of different drivers, thereby more flexibly and reasonably controlling the intensity of braking energy recovery and improving the energy recovery efficiency.

[0040] Other features and advantages of the present disclosure will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The accompanying drawings are used to provide a further understanding of the present disclosure and constitute a part of the specification. Together with the following specific embodiments, they are used to explain the present disclosure but do not constitute a limitation of the present disclosure. In the accompanying drawings:

[0042] Figure 1 The present invention is a flowchart of a vehicle control method according to an exemplary embodiment.

[0043] Figure 2 is a schematic diagram of a first membership function according to an exemplary embodiment.

[0044] Figure 3 is a schematic diagram of a second membership function according to an exemplary embodiment.

[0045] Figure 4 is a schematic diagram of a third membership function according to an exemplary embodiment.

[0046] Figure 5 The present invention is a flowchart of another method for controlling a vehicle according to an exemplary embodiment.

[0047] Figure 6 The present invention is a flowchart of another method for controlling a vehicle according to an exemplary embodiment.

[0048] Figure 7 The present invention is a flowchart of another method for controlling a vehicle according to an exemplary embodiment.

[0049] Figure 8 is a block diagram of a vehicle control device according to an exemplary embodiment.

[0050] Fig. 9 is a block diagram of another vehicle control device according to an exemplary embodiment.

[0051] Fig.10 is a block diagram of a vehicle according to an exemplary embodiment. DETAILED DESCRIPTION

[0052] The specific implementation of the present disclosure is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described herein is only used to illustrate and explain the present disclosure, and is not used to limit the present disclosure.

[0053] Figure 1 is a flow chart of a vehicle control method according to an exemplary embodiment. Figure 1 As shown, the method may include:

[0054] Step S101, obtaining target operation information of the vehicle.

[0055] For example, during the operation of the vehicle, the real-time operation information of the vehicle can be collected in real time, and the target operation information of the vehicle can be determined based on multiple real-time operation information. In some embodiments, multiple real-time operation information within a preset time period before the current moment can be obtained, and the average value of the multiple real-time operation information within the preset time period is used as the target operation information.

[0056] In other embodiments, the target operation information may include at least one of target vehicle speed information, target deceleration information, and target brake pedal opening information. The brake pedal opening indicates the degree to which the driver presses the brake pedal. When the brake pedal is not pressed by the driver, the brake pedal opening is 0, and when the brake pedal is fully pressed to the lowest end by the driver, the brake pedal opening is 1.

[0057] When the target operating information includes target vehicle speed information, target deceleration information and target brake pedal opening information, the vehicle's real-time vehicle speed information, real-time deceleration information and real-time brake pedal opening information can be collected in real time, and then the average value of multiple real-time vehicle speed information within a preset time length is used as the target vehicle speed information, and the average value of multiple real-time deceleration information within a preset time length is used as the target deceleration information, and the average value of multiple real-time brake pedal opening information within the preset time length is used as the target brake pedal opening information.

[0058] Step S102, fuzzy processing is performed on the target operation information to obtain the target driving state.

[0059] For example, when the target operation information includes target vehicle speed information, target deceleration information, and target brake pedal opening information, the target driving state may include target vehicle speed state, target deceleration state, and target brake pedal opening state. The target vehicle speed state may include any one of low speed, medium speed, and high speed, the target deceleration state may include any one of low deceleration, medium deceleration, and high deceleration, and the target brake pedal opening state may include any one of small brake pedal opening, medium brake pedal opening, and high brake pedal opening.

[0060] In some embodiments, the target vehicle speed state corresponding to the target vehicle speed information can be determined based on the first fuzzy relationship corresponding to the vehicle speed information, the target deceleration state corresponding to the target deceleration state can be determined based on the second fuzzy relationship corresponding to the deceleration information, and the target brake pedal opening state corresponding to the target brake pedal opening information can be determined based on the third fuzzy relationship corresponding to the brake pedal opening information.

[0061] The first fuzzy relationship may be a membership function between vehicle speed information and vehicle speed state, the second fuzzy relationship may be a membership function between deceleration information and deceleration state, and the third fuzzy relationship may be a membership function between brake pedal opening information and brake pedal opening state. By fuzzifying the target operation information through the preset fuzzy relationship, the vehicle's operation information can be divided into corresponding operation states, which is convenient for the subsequent determination of the driver's driving type.

[0062] Step S103, determining the target torque according to the target driving state.

[0063] Step S104 , controlling the feedback torque of the motor of the vehicle according to the target torque to control the braking of the vehicle.

[0064] For example, the current vehicle speed information and brake pedal opening information of the vehicle may be obtained, and a candidate torque may be obtained based on the current vehicle speed information and brake pedal opening information, and then the candidate torque may be corrected based on the target driving state to obtain the target torque.

[0065] In some embodiments, the feedback torque of the vehicle's motor can be controlled according to the target torque to control vehicle braking and realize the recovery of braking energy. Among them, the physical meaning of energy recovery intensity is deceleration. The greater the deceleration, the greater the energy recovery intensity, and the energy recovery intensity is controlled by the feedback torque of the motor. The greater the feedback torque, the greater the energy intensity. Therefore, the target correction coefficient corresponding to the target driving state can be determined first, and then the candidate torque can be corrected by the target correction coefficient to control the energy recovery intensity. In this way, according to different driving states corresponding to different drivers, the target torque obtained by correcting the torque with different correction coefficients can be adapted to the driving styles of different drivers, so that the energy recovery intensity is closer to the driver's braking habits, effectively reducing the frequency of the driver's pedaling and improving the energy recovery efficiency.

[0066] In one possible implementation, a third preset correspondence between the driving state and the correction coefficient can be set in advance. After the target driving state is obtained, the target correction coefficient corresponding to the target driving state can be determined through the third preset correspondence, and then the product of the candidate torque and the target correction coefficient can be used as the target torque.

[0067] In another possible implementation, a target model between driving states and correction coefficients may be pre-trained. After the target driving state is obtained, the target driving state may be input into the target model to obtain a target correction coefficient corresponding to the target driving state output by the target model. The product of the candidate torque and the target correction coefficient may then be used as the target torque.

[0068] In summary, the method first obtains the target operation information of the vehicle, and performs fuzzy processing on the target operation information to obtain the target driving state. Then, the target driving type of the driver is determined according to the first preset correspondence and the target driving state, wherein the first preset correspondence includes the correspondence between the driving state and the driving type. Finally, the target torque is determined according to the target driving type, and the feedback torque of the vehicle's motor is controlled according to the target torque to control the vehicle braking. The present disclosure performs fuzzy processing on the vehicle's operation information to obtain the driving state of the driver when driving the vehicle, and controls the feedback torque when the vehicle brakes according to the driving state, so that the feedback torque can adapt to the driving styles of different drivers, thereby more flexibly and reasonably controlling the intensity of braking energy recovery and improving the energy recovery efficiency.

[0069] In some embodiments, an implementation of step S102 may be:

[0070] The target operation information is fuzzified according to a preset fuzzy relationship, where the preset fuzzy relationship includes a subordinate relationship between the operation information and the driving state.

[0071] For example, when the target operating information includes vehicle speed information, deceleration information, and brake pedal opening information, the preset fuzzy relationship may include a first fuzzy relationship, a second fuzzy relationship, and a third fuzzy relationship.

[0072] The first fuzzy relationship may include a first membership function corresponding to the vehicle speed information, such as Figure 2 As shown, different vehicle speeds belong to different vehicle speed states, where the vehicle speed state may include low speed, medium speed and high speed. Specifically, the first membership function may be composed of a low speed portion with a vehicle speed less than 40 km / h, a medium speed portion with a vehicle speed greater than 30 km / h and less than 80 km / h, and a high speed portion with a vehicle speed greater than 70 km / h. Figure 2 For the overlapping parts of low-medium speed, medium speed and high speed, the value with a larger membership degree can be used as the membership degree of the vehicle speed of the overlapping part, and the membership degree corresponding to the intersection of the function graphs of low speed, medium speed and high speed can be preset. Figure 2 For example, if the intersection of the function graphs of medium-low speed and medium speed corresponds to a vehicle speed of 37, in the speed interval of 30-40, the function graphs corresponding to low speed and medium speed overlap, and the membership corresponding to the speed in the interval of 30-37 can be based on the value on the function graph corresponding to low speed, and the membership corresponding to the speed in the interval of 37-40 can be based on the value on the function graph corresponding to medium speed. The vehicle speed information can be fuzzified into one of the three vehicle speed states of low speed, medium speed or high speed through the first fuzzy relationship.

[0073] The second fuzzy relationship may include a second membership function of the deceleration information, such as Figure 3As shown, different decelerations belong to different deceleration states, wherein the deceleration state may include low deceleration, medium deceleration and high deceleration. Specifically, the second membership function may be composed of an average deceleration of less than 0.3 m / s 2 The average deceleration of the low deceleration part is greater than 0.2m / s 2 And less than 0.7m / s 2 The middle deceleration part and the average deceleration are greater than 0.6m / s 2 The high deceleration part. Figure 3 The overlapping part of the function graph corresponding to medium-low deceleration, medium deceleration and high deceleration can use the value with a larger membership as the membership of the deceleration of the overlapping part. The deceleration information can be fuzzyized into one of the three deceleration states of low deceleration, medium deceleration or high deceleration through the second fuzzy relationship.

[0074] The third fuzzy relationship may include a third membership function of the brake pedal opening information, such as Figure 4 As shown, different brake pedal openings belong to different brake pedal opening states, wherein the brake pedal opening state may include a small brake pedal opening, a medium brake pedal opening, and a high brake pedal opening. Figure 4 The overlapping part of the function graph corresponding to the small and medium brake pedal opening, the medium brake pedal opening and the high brake pedal opening can use the value with the larger membership as the membership of the brake pedal opening of the overlapping part. The brake pedal opening information can be fuzzyized into one of the three brake pedal opening states of small brake pedal opening, medium brake pedal opening and high brake pedal opening through the third fuzzy relationship.

[0075] In this way, by fuzzifying the target operating information through a preset fuzzy relationship, the vehicle's operating information can be divided into corresponding operating states, which is convenient for more accurate determination of the driver's driving type later.

[0076] Figure 5 is a flow chart of another vehicle control method according to an exemplary embodiment. Figure 5 As shown, step S101 can be implemented in the following manner:

[0077] Step S1011, obtaining multiple real-time operation information of the vehicle within a preset time period.

[0078] Step S1012: taking an average value of multiple real-time operation information as target operation information.

[0079] For example, real-time operation information may include real-time vehicle speed information, real-time deceleration information, and real-time brake pedal opening information. During the operation of the vehicle, the real-time operation information of the vehicle can be collected and stored in real time, and the target operation information can be calculated in real time based on the real-time operation information of the vehicle within a preset time period. Specifically, the average value of the real-time operation information within the preset time period can be used as the target operation information, wherein the preset time period can be understood as the time period before the current moment, and the preset time period can be, for example, the time period of 1 hour before the current moment. Accordingly, the target operation information can be the average value of all real-time operation information within the time period of 1 hour before the current moment.

[0080] Taking the real-time operation information including real-time vehicle speed information, real-time deceleration information and real-time brake pedal opening information as an example, the average value of multiple real-time vehicle speed information within a preset time period, that is, the average vehicle speed, can be used as the target vehicle speed information, and the average value of multiple real-time deceleration information within a preset time period, that is, the average deceleration, can be used as the target deceleration information, and the average value of multiple real-time brake pedal opening information within a preset time period, that is, the average brake pedal opening, can be used as the target brake pedal opening information. Among them, the average vehicle speed can be calculated by formula 1, the average deceleration can be calculated by formula 2, and the average brake pedal opening can be obtained by formula 3.

[0081]

[0082] Among them, v ave is the average speed, v is the real-time speed, t v The moment when the vehicle speed is not 0.

[0083]

[0084] Among them, a ave is the average deceleration, t de The moment when the vehicle slows down.

[0085]

[0086] Among them, s ave is the average brake pedal opening, and s is the real-time brake pedal opening.

[0087] In this way, the target operating information is obtained by calculating the average value of a large amount of operating information generated by the vehicle within a preset time period, which facilitates more accurate determination of the target driving state and the driver's target driving type in the subsequent period.

[0088] Figure 6 is a flow chart of another vehicle control method according to an exemplary embodiment. Figure 6 As shown, step S103 can be implemented by the following steps:

[0089] Step S1031, determining the target driving type of the driver according to a first preset corresponding relationship and the target driving state, wherein the first preset corresponding relationship may include a corresponding relationship between the driving state and the driving type.

[0090] For example, multiple driving types and a first preset correspondence relationship between driving states and driving types can be preset. The driving type can include at least one of conservative, conservative, normal, aggressive, and aggressive. After obtaining the target driving state, the target driving type corresponding to the target driving state can be obtained through the first preset correspondence relationship. The target driving type can be understood as the driving type of the driver determined according to the target driving state. The first preset correspondence relationship can be the fuzzy inference rules shown in Table 1.

[0091]

[0092] Table 1

[0093] Step S1032, determining the target torque according to the target driving type.

[0094] For example, first, the candidate torque can be obtained according to the current vehicle speed information and the brake pedal opening information, and then the candidate torque is corrected according to the target driving type to obtain the target torque, and the feedback torque of the vehicle's motor is controlled according to the target torque to control the vehicle braking and realize the recovery of braking energy at the same time. Among them, the physical meaning of energy recovery intensity is deceleration. The greater the deceleration, the greater the energy recovery intensity, and the energy recovery intensity is controlled by the feedback torque of the motor. The greater the feedback torque, the greater the energy intensity. Therefore, the torque correction coefficient corresponding to each driving type can be pre-set, and the candidate torque can be corrected by the torque correction coefficient to control the energy recovery intensity. For example, the torque correction coefficients corresponding to the conservative, conservative, ordinary, aggressive, and aggressive driving types can be reduced in sequence, and accordingly, the energy recovery intensity corresponding to the conservative, conservative, ordinary, aggressive, and aggressive driving types can also be reduced in sequence, which can adapt to the driving styles of different drivers, so that the energy recovery intensity is closer to the driver's braking habits, effectively reducing the driver's pedaling frequency and improving the energy recovery efficiency.

[0095] Figure 7 is a flow chart of another vehicle control method according to an exemplary embodiment. Figure 7 As shown, step S1032 can be implemented by the following steps:

[0096] Step S1032a: determining a first candidate torque according to the first current operating information and the target driving type.

[0097] For example, the first current operating information may include vehicle speed information and brake pedal opening information. The second candidate torque may be determined based on the first current operating information and the second preset correspondence, wherein the second preset correspondence may include the correspondence between the first current operating information and the second candidate torque. The second preset correspondence may, for example, be a correspondence table between the first current operating information and the second candidate torque. After obtaining the first current operating information, the second candidate torque corresponding to the first current operating information may be searched in the correspondence table. The second preset correspondence may also be a correspondence function between the first current operating information and the second candidate torque. After obtaining the first current operating information, the first current operating information may be substituted into the correspondence function to obtain the second candidate torque. The present disclosure does not specifically limit this.

[0098] In some embodiments, the second candidate torque may be corrected according to the target driving type to obtain the first candidate torque. In a possible implementation, different torque correction coefficients may be set for different driving types, and after obtaining the target driving type, the product of the torque correction coefficient corresponding to the target driving type and the second candidate torque may be used as the first candidate torque.

[0099] For example, the driving type may include conservative, conservative, normal, aggressive, and aggressive, and the corresponding torque correction coefficients are 1.2, 1.1, 1, 0.9, and 0.8, respectively. Taking the second candidate torque as 100 as an example, when the target driving type is conservative, the first candidate torque may be 1.2×100=120; when the target driving type is conservative, the first candidate torque may be 1.1×100=110; when the target driving type is normal, the first candidate torque may be 1×100=100; when the target driving type is aggressive, the first candidate torque may be 0.9×100=90; when the target driving type is aggressive, the first candidate torque may be 0.8×100=80.

[0100] Step S1032b: determining the dynamic mass of the vehicle according to the second current operating information, wherein the dynamic mass can represent the actual mass of the vehicle in the current operating state.

[0101] For example, the second current operation information may include vehicle speed information, acceleration information, and motor torque of the vehicle. In some embodiments, due to the force applied by passengers and during operation, the dynamic mass of the vehicle during operation may be different from the curb mass of the vehicle when it leaves the factory. Therefore, the longitudinal force relationship of the vehicle may be determined based on the second current operation information, wherein the longitudinal force relationship may include the dynamic relationship between the rolling resistance, wind resistance, ramp resistance, and acceleration resistance of the vehicle and the longitudinal force. Then, based on the longitudinal force relationship, the dynamic mass is determined by a recursive least squares model.

[0102] In a possible implementation, according to the basic formula of vehicle longitudinal dynamics:

[0103] F x =F f +F w +F i +F j (Formula 4)

[0104] Among them, F x , F f , F w , F i , F j They correspond to the longitudinal force, rolling resistance, wind resistance, ramp resistance and acceleration resistance of the vehicle respectively. Specifically, they can be written as:

[0105]

[0106] Among them, T tq is the motor torque, i0 is the gear ratio of the reducer, r is the tire rolling radius, m is the dynamic mass of the vehicle, f is the rolling resistance coefficient, ρ is the air density, C d is the air resistance coefficient, A is the frontal area of ​​the vehicle, θ is the road slope, is the actual acceleration of the vehicle. Since the acceleration signal obtained by the vehicle is collected by the longitudinal acceleration sensor, the acceleration a collected by the longitudinal acceleration sensor satisfies:

[0107]

[0108] So the longitudinal dynamics formula of the vehicle is simplified to:

[0109]

[0110] set up a k =a+gf, and M(k) and M(k-1) correspond to the estimated mass of the vehicle at the kth moment and the k-1th moment respectively. The recursive least squares model is introduced for mass estimation:

[0111]

[0112] P(0) = aE (Formula 11)

[0113] M(0) = ε (Formula 12)

[0114] Among them, K(k) and P(k) are the intermediate quantities of the least squares model, a is a sufficiently large positive real number, E is the unit matrix, and ε is the zero vector. Therefore, the dynamic mass m of the vehicle can be calculated based on the real-time collected vehicle speed, acceleration, and motor torque.

[0115] Step S1032c: determining the target torque according to the first candidate torque and the dynamic mass.

[0116] For example, the mass correction factor can be determined according to the dynamic mass of the vehicle. In one possible implementation, the curb mass of the vehicle can be first obtained, and then the ratio of the dynamic mass to the curb mass is used as the mass correction factor. The curb mass can represent the mass of the vehicle when it leaves the factory. For example, the mass correction factor can be calculated by formula 13.

[0117]

[0118] Among them, μ is the mass correction coefficient and m0 is the curb mass of the vehicle.

[0119] In other embodiments, the first candidate torque may be corrected according to the mass correction coefficient to obtain the target torque. For example, the product of the mass correction coefficient and the first candidate torque may be used as the target torque through Formula 14.

[0120] T2=μT1 (Formula 14)

[0121] Wherein, T1 is the first candidate torque, and T2 is the target torque.

[0122] In summary, the method first obtains the target operation information of the vehicle, and performs fuzzy processing on the target operation information to obtain the target driving state. Then, the target driving type of the driver is determined according to the first preset correspondence and the target driving state, wherein the first preset correspondence includes the correspondence between the driving state and the driving type. Finally, the target torque is determined according to the target driving type, and the feedback torque of the vehicle's motor is controlled according to the target torque to control the vehicle braking. The present disclosure performs fuzzy processing on the vehicle's operation information to obtain the driving state of the driver when driving the vehicle, and controls the feedback torque when the vehicle brakes according to the driving state, so that the feedback torque can adapt to the driving styles of different drivers, thereby more flexibly and reasonably controlling the intensity of braking energy recovery and improving the energy recovery efficiency.

[0123] Figure 8 is a block diagram of a vehicle control device according to an exemplary embodiment. Figure 8 As shown, the device 200 includes:

[0124] The acquisition module 201 is configured to acquire target operation information of the vehicle.

[0125] The processing module 202 is configured to perform fuzzy processing on the target operation information to obtain the target driving state.

[0126] The determination module 203 is configured to determine the target torque according to the target driving state.

[0127] The control module 204 is configured to control the feedback torque of the motor of the vehicle according to the target torque to control the braking of the vehicle.

[0128] In some embodiments, the processing module 202 is configured to:

[0129] The target operation information is fuzzified according to a preset fuzzy relationship, where the preset fuzzy relationship includes a subordinate relationship between the operation information and the driving state.

[0130] Fig. 9 is a block diagram of another vehicle control device according to an exemplary embodiment. Fig. 9 As shown, the determination module 203 includes:

[0131] The first determination submodule 2031 is configured to determine the target driving type of the driver according to a first preset corresponding relationship and the target driving state, wherein the first preset corresponding relationship includes a corresponding relationship between the driving state and the driving type.

[0132] The second determination submodule 2032 is configured to determine the target torque according to the target driving type.

[0133] In some other embodiments, the second determining submodule 2032 is configured to:

[0134] A first candidate torque is determined according to first current operating information and a target driving type.

[0135] The dynamic mass of the vehicle is determined according to the second current operating information, where the dynamic mass represents the actual mass of the vehicle in the current operating state.

[0136] A target torque is determined based on the first candidate torque and the dynamic mass.

[0137] In some other embodiments, the second determining submodule 2032 is configured to:

[0138] The second candidate torque is determined according to the first current operating information and the second preset corresponding relationship, where the second preset corresponding relationship includes a corresponding relationship between the first current operating information and the second candidate torque.

[0139] The second candidate torque is corrected according to the target driving type to obtain the first candidate torque.

[0140] In some other embodiments, the second determining submodule 2032 is configured to:

[0141] Determine the mass correction factor based on the dynamic mass.

[0142] The first candidate torque is corrected according to the mass correction coefficient to obtain the target torque.

[0143] In some other embodiments, the second determining submodule 2032 is configured to:

[0144] Get the vehicle's curb weight, which represents the quality of the vehicle when it leaves the factory.

[0145] The ratio of dynamic mass to curb mass is taken as the mass correction factor.

[0146] In some other embodiments, the second determining submodule 2032 is configured to:

[0147] The longitudinal force relationship of the vehicle is determined according to the second current operation information, where the longitudinal force relationship includes the dynamic relationship between the rolling resistance, wind resistance, ramp resistance and acceleration resistance of the vehicle and the longitudinal force.

[0148] Based on the longitudinal force relationship, the dynamic mass is determined through a recursive least squares model.

[0149] In some other embodiments, the acquisition module 201 is configured to:

[0150] Get multiple real-time operation information of the vehicle within a preset time period.

[0151] The average value of multiple real-time operation information is used as the target operation information.

[0152] In other embodiments, the target operation information includes at least one of vehicle speed information, deceleration information and brake pedal opening information, and the driving type includes at least one of conservative, conservative, normal, aggressive and aggressive.

[0153] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0154] In summary, the method first obtains the target operation information of the vehicle, and performs fuzzy processing on the target operation information to obtain the target driving state. Then, the target driving type of the driver is determined according to the first preset correspondence and the target driving state, wherein the first preset correspondence includes the correspondence between the driving state and the driving type. Finally, the target torque is determined according to the target driving type, and the feedback torque of the vehicle's motor is controlled according to the target torque to control the vehicle braking. The present disclosure performs fuzzy processing on the vehicle's operation information to obtain the driving state of the driver when driving the vehicle, and controls the feedback torque when the vehicle brakes according to the driving state, so that the feedback torque can adapt to the driving styles of different drivers, thereby more flexibly and reasonably controlling the intensity of braking energy recovery and improving the energy recovery efficiency.

[0155] Fig.10FIG. 1 is a block diagram of a vehicle according to an exemplary embodiment. Fig.10 As shown, the vehicle 300 may include: a processor 301 , a memory 302 . The vehicle 300 may also include one or more of a multimedia component 303 , an input / output (I / O) interface 304 , and a communication component 305 .

[0156] The processor 301 is used to control the overall operation of the vehicle 300 to complete all or part of the steps in the above-mentioned vehicle control method. The memory 302 is used to store various types of data to support the operation of the vehicle 300. For example, these data may include instructions for any application or method used to operate on the vehicle 300, and application-related data, such as contact data, sent and received messages, pictures, audio, video, etc. The memory 302 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read-Only Memory, referred to as EPROM), programmable read-only memory (Programmable Read-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic memory, flash memory, magnetic disk or optical disk. The multimedia component 303 may include a screen and an audio component. The screen may be, for example, a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may be further stored in the memory 302 or sent through the communication component 305. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 304 provides an interface between the processor 301 and other interface modules, and the other interface modules may be keyboards, mice, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 305 is used for wired or wireless communication between the vehicle 300 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, 4G, NB-IOT, eMTC, or other 5G, etc., or a combination of one or more of them, is not limited here. Therefore, the corresponding communication component 305 may include: Wi-Fi module, Bluetooth module, NFC module, etc.

[0157] In an exemplary embodiment, the vehicle 300 can be implemented by one or more application specific integrated circuits (ASIC), digital signal processors (DSP), digital signal processing devices (DSPD), programmable logic devices (PLD), field programmable gate arrays (FPGA), controllers, microcontrollers, microprocessors or other electronic components to execute the above-mentioned vehicle control method.

[0158] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, and when the program instructions are executed by a processor, the steps of the above-mentioned vehicle control method are implemented. For example, the computer-readable storage medium can be the above-mentioned memory 302 including program instructions, and the above-mentioned program instructions can be executed by the processor 301 of the vehicle 300 to complete the above-mentioned vehicle control method.

[0159] In another exemplary embodiment, a computer program product is also provided, including a computer program, which implements the steps of the above-mentioned vehicle control method when executed by a processor.

[0160] The preferred embodiments of the present disclosure are described in detail above in conjunction with the accompanying drawings; however, the present disclosure is not limited to the specific details in the above embodiments. Within the technical concept of the present disclosure, a variety of simple modifications can be made to the technical solution of the present disclosure, and these simple modifications all fall within the protection scope of the present disclosure.

[0161] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the present disclosure will not further describe various possible combinations.

[0162] In addition, various embodiments of the present disclosure may be arbitrarily combined, and as long as they do not violate the concept of the present disclosure, they should also be regarded as the contents disclosed by the present disclosure.

Claims

1. A vehicle control method, characterized in that: The method comprises: Obtain target operation information of the vehicle; Performing fuzzy processing on the target operation information to obtain a target driving state; determining a target torque according to the target driving state; The feedback torque of the motor of the vehicle is controlled according to the target torque to control the braking of the vehicle.

2. The method according to claim 1, characterized in that The fuzzy processing of the target operation information includes: The target operation information is fuzzified according to a preset fuzzy relationship, wherein the preset fuzzy relationship includes a subordinate relationship between the operation information and the driving state.

3. The method according to claim 1, characterized in that Determining the target torque according to the target driving state includes: determining a target driving type of the driver according to a first preset corresponding relationship and the target driving state, wherein the first preset corresponding relationship includes a corresponding relationship between the driving state and the driving type; The target torque is determined according to the target driving type.

4. The method according to claim 3, characterized in that Determining the target torque according to the target driving type includes: determining a first candidate torque according to first current operating information and the target driving type; Determining the dynamic mass of the vehicle according to the second current operation information, wherein the dynamic mass represents the actual mass of the vehicle in the current operation state; The target torque is determined according to the first candidate torque and the dynamic mass.

5. The method according to claim 4, characterized in that The determining the first candidate torque according to the first current operating information and the target driving type comprises: determining a second candidate torque according to the first current operating information and a second preset corresponding relationship, wherein the second preset corresponding relationship includes a corresponding relationship between the first current operating information and the second candidate torque; The second candidate torque is corrected according to the target driving type to obtain the first candidate torque.

6. The method according to claim 4, characterized in that The determining the target torque according to the first candidate torque and the dynamic mass comprises: determining a mass correction factor based on the dynamic mass; The first candidate torque is corrected according to the mass correction coefficient to obtain the target torque.

7. The method according to claim 6, characterized in that Determining the mass correction coefficient according to the dynamic mass includes: Obtaining the curb weight of the vehicle, where the curb weight represents the weight of the vehicle when it leaves the factory; The ratio of the dynamic mass to the curb mass is used as the mass correction coefficient.

8. The method according to claim 4, characterized in that Determining the dynamic mass of the vehicle according to the second current operation information includes: determining a longitudinal force relationship of the vehicle according to the second current operation information, wherein the longitudinal force relationship includes a dynamic relationship between the longitudinal force and the rolling resistance, wind resistance, ramp resistance and acceleration resistance of the vehicle; The dynamic mass is determined based on the longitudinal force relationship through a recursive least squares model.

9. The method according to claim 1, characterized in that: The obtaining of the target operation information of the vehicle comprises: Acquire multiple real-time operation information of the vehicle within a preset time period; An average value of the plurality of real-time operation information is used as the target operation information.

10. The method according to any one of claims 1 to 9, characterized in that The target operation information includes at least one of vehicle speed information, deceleration information and brake pedal opening information, and the driving type includes at least one of conservative, conservative, normal, aggressive and aggressive.

11. A vehicle, characterized in that: include: a memory having a computer program stored thereon; A processor, configured to execute the computer program in the memory to implement the steps of the method according to any one of claims 1 to 10.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 10 are implemented.

13. A computer program product, characterized in that The invention comprises a computer program, which, when executed by a processor, implements the steps of the method according to any one of claims 1 to 10.

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