Method of vehicle control, vehicle, storage medium, and program product

Abstract

The present disclosure 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: obtaining target running information of a vehicle. The target running information is fuzzed to obtain a target driving state. The target torque is determined 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. The present disclosure fuzzes the running information of the vehicle 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 style of different drivers, thereby more flexibly and reasonably controlling the strength of brake energy recovery, and improving the energy recovery efficiency.

Description

Technical Field

[0001] This disclosure relates to the field of vehicle technology, and more specifically, to a vehicle control method, a vehicle, a storage medium, and a program product. Background Technology

[0002] With the continuous development of new energy vehicles, energy recovery in these vehicles is receiving increasing attention from society. Regenerative braking, also known as brake energy recovery in new energy vehicles, refers to the process where, during braking, the electric motor is controlled to operate in a generator mode, converting the mechanical energy of the vehicle into electrical energy and storing it in energy storage systems such as batteries, thus recovering a portion of the braking energy. Simultaneously, the motor generates braking torque, which is transmitted to the wheels through the transmission system, thereby slowing the vehicle down. However, current brake energy recovery technologies use the same braking method for different drivers, failing to adapt to different driving styles and reducing the efficiency of energy recovery. Summary of the Invention

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

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

[0005] Obtain the target operating information of the vehicle;

[0006] The target's operational information is then fuzzified to obtain the target's driving state;

[0007] Determine the target torque based on the target driving state;

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

[0009] Optionally, the process of obfuscating the target running information includes:

[0010] The target operation information is fuzzified according to a preset fuzzy relationship, which includes the membership relationship between operation information and driving status.

[0011] Optionally, determining the target torque based on the target driving state includes:

[0012] The driver's target driving type is determined based on a first preset correspondence and the target driving state, wherein the first preset correspondence includes the correspondence between driving state and driving type;

[0013] The target torque is determined based on the target driving type.

[0014] Optionally, determining the target torque based on the target driving type includes:

[0015] A first candidate torque is determined based on the first current operating information and the target driving type;

[0016] Based on the second current operating information, the dynamic mass of the vehicle is determined, and the dynamic mass characterizes the actual mass of the vehicle in the current operating state;

[0017] The target torque is determined based on the first candidate torque and the dynamic mass.

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

[0019] A second candidate torque is determined based on the first current operating information and the second preset correspondence, wherein the second preset correspondence includes the correspondence between the first current operating information and the second candidate torque.

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

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

[0022] Determine the quality correction coefficient based on the dynamic quality;

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

[0024] Optionally, determining the quality correction coefficient based on the dynamic quality includes:

[0025] Obtain the curb weight of the vehicle, which represents the weight of the vehicle at the time of manufacture;

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

[0027] Optionally, determining the dynamic quality of the vehicle based on the second current operating information includes:

[0028] Based on the second current operating information, the longitudinal force relationship of the vehicle is determined, and the longitudinal force relationship includes the dynamic relationship between the vehicle's rolling resistance, wind resistance, slope resistance and acceleration resistance and the longitudinal force;

[0029] Based on the longitudinal force relationship, the dynamic mass is determined using a recursive least squares model.

[0030] Optionally, obtaining the target operating information of the vehicle includes:

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

[0032] The average value of the multiple real-time operating information is taken as the target operating information.

[0033] Optionally, the target operating 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, semi-conservative, normal, semi-aggressive, and aggressive.

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

[0035] A memory on which computer programs are stored;

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

[0037] According to a third aspect of the present disclosure, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in the first aspect of the present disclosure.

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

[0039] The above technical solution first acquires the vehicle's target operating information and then performs fuzzification processing on the target operating information to obtain the target driving state. Next, it determines the driver's target driving type based on a first preset correspondence and the target driving state, wherein the first preset correspondence includes the correspondence between driving state and driving type. Finally, it determines the target torque based on the target driving type and controls the vehicle's motor feedback torque based on the target torque to control vehicle braking. This disclosure performs fuzzification processing on the vehicle's operating information to obtain the driver's driving state, and controls the vehicle's braking feedback torque based on the driving state. This allows the feedback torque to adapt to different drivers' driving styles, thereby more flexibly and rationally controlling the intensity of regenerative braking and improving energy recovery efficiency.

[0040] Other features and advantages of this disclosure will be described in detail in the following detailed description section. Attached Figure Description

[0041] The accompanying drawings are provided to further illustrate the present disclosure and form part of the specification. They are used together with the following detailed description to explain the present disclosure, but do not constitute a limitation thereof. In the drawings:

[0042] Figure 1 This is a flowchart illustrating a vehicle control method according to an exemplary embodiment.

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

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

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

[0046] Figure 5 This is a flowchart illustrating another vehicle control method according to an exemplary embodiment.

[0047] Figure 6 This is a flowchart illustrating another vehicle control method according to an exemplary embodiment.

[0048] Figure 7 This is a flowchart illustrating another vehicle control method according to an exemplary embodiment.

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

[0050] Figure 9 This is a block diagram illustrating another vehicle control device according to an exemplary embodiment.

[0051] Figure 10 This is a block diagram illustrating a vehicle according to an exemplary embodiment. Detailed Implementation

[0052] The specific embodiments of this disclosure will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit this disclosure.

[0053] Figure 1 This is a flowchart illustrating a vehicle control method according to an exemplary embodiment, such as... Figure 1 As shown, the method may include:

[0054] Step S101: Obtain the target operating information of the vehicle.

[0055] For example, during vehicle operation, real-time vehicle operation information 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 prior to the current moment can be obtained, and the average value of the multiple real-time operation information within the preset time period can be used as the target operation information.

[0056] In other embodiments, the target operating 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 has depressed the brake pedal; when the driver has not depressed the brake pedal, the brake pedal opening is 0; when the driver has fully depressed the brake pedal to its lowest position, 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 real-time vehicle speed information, real-time deceleration information, and real-time brake pedal opening information can be collected in real time. Then, the average value of multiple real-time vehicle speed information within a preset time period is used as the target vehicle speed information, the average value of multiple real-time deceleration information within a preset time period is used as the target deceleration information, and the average value of multiple real-time brake pedal opening information within a preset time period is used as the target brake pedal opening information.

[0058] Step S102: The target operating information is fuzzed to obtain the target driving state.

[0059] For example, when the target operating information includes target vehicle speed information, target deceleration information, and target brake pedal opening information, the target driving state can include target vehicle speed state, target deceleration state, and target brake pedal opening state. Specifically, the target vehicle speed state can include any one of low speed, medium speed, and high speed; the target deceleration state can include any one of low deceleration, medium deceleration, and high deceleration; and the target brake pedal opening state can be 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 according to the first fuzzy relation corresponding to the vehicle speed information, the target deceleration state corresponding to the target deceleration state can be determined according to the second fuzzy relation corresponding to the deceleration information, and the target brake pedal opening state corresponding to the target brake pedal opening information can be determined according to the third fuzzy relation corresponding to the brake pedal opening information.

[0061] The first fuzzy relation can be a membership function between vehicle speed information and vehicle speed state; the second fuzzy relation can be a membership function between deceleration information and deceleration state; and the third fuzzy relation can be a membership function between brake pedal opening information and brake pedal opening state. By fuzzifying the target operating information through preset fuzzy relations, the vehicle's operating information can be divided into corresponding operating states, facilitating the subsequent determination of the driver's driving type.

[0062] Step S103: Determine the target torque based on the target driving state.

[0063] Step S104: Control the feedback torque of the vehicle's motor according to the target torque to control the vehicle's braking.

[0064] For example, the current vehicle speed and brake pedal opening information can be obtained, and candidate torques can be obtained based on the current vehicle speed and brake pedal opening information. Then, the candidate torques can be corrected according to the target driving state to obtain the target torque.

[0065] In some embodiments, the regenerative torque of the vehicle's motor can be controlled according to the target torque to control vehicle braking and simultaneously recover braking energy. The physical meaning of energy recovery intensity is deceleration; the greater the deceleration, the greater the energy recovery intensity. The energy recovery intensity is controlled by the motor's regenerative torque; the greater the regenerative torque, the greater the energy recovery intensity. Therefore, a target correction coefficient corresponding to the target driving state can be determined first, and then the candidate torque can be corrected using this target correction coefficient to control the energy recovery intensity. In this way, by using different correction coefficients to correct the torque according to different driving states of different drivers, the target torque can be adapted to different driving styles, making the energy recovery intensity closer to the driver's braking habits, effectively reducing the frequency of the driver's pedal input and improving energy recovery efficiency.

[0066] In one possible implementation, a third preset correspondence between driving state and correction coefficient can be pre-defined. After obtaining the target driving state, 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 the driving state and the correction coefficient can be pre-trained. After obtaining the target driving state, the target driving state can be input into the target model to obtain the target correction coefficient corresponding to the target driving state output by the target model. Then, the product of the candidate torque and the target correction coefficient is taken as the target torque.

[0068] In summary, this method first acquires the vehicle's target operating information and then performs fuzzification processing on this information to obtain the target driving state. Next, it determines the driver's target driving type based on a first preset correspondence and the target driving state, where the first preset correspondence includes the correspondence between driving states and driving types. Finally, it determines the target torque based on the target driving type and controls the vehicle's motor feedback torque to control vehicle braking. This disclosure fuzzifies the vehicle's operating information to obtain the driver's driving state, and controls the feedback torque during braking based on the driving state. This allows the feedback torque to adapt to different drivers' driving styles, thereby more flexibly and rationally controlling the intensity of regenerative braking and improving energy recovery efficiency.

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

[0070] The target operation information is fuzzified according to the preset fuzzy relationship, which includes the membership relationship between operation information and driving status.

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

[0072] The first fuzzy relation may include the first membership function corresponding to the vehicle speed information, such as... Figure 2 As shown, different vehicle speeds belong to different vehicle speed states, which can include low speed, medium speed, and high speed. Specifically, the first membership function can consist of a low-speed portion (vehicle speed less than 40 km / h), a medium-speed portion (vehicle speed greater than 30 km / h and less than 80 km / h), and a high-speed portion (vehicle speed greater than 70 km / h). For... Figure 2 For the overlapping portions of low-to-medium speed, medium speed, and high speed, the values ​​with larger membership degrees can be used as the membership degrees for the overlapping portions of the vehicle speed. The membership degrees corresponding to the intersection points of the function graphs of low-to-medium speed, medium speed, and high speed can be preset. Figure 2 Taking a vehicle speed of 37 as an example, where the intersection of the function graphs for low-speed and medium-speed vehicles corresponds to a speed of 30-40, the function graphs for low-speed and medium-speed vehicles overlap. The membership degree corresponding to the vehicle speed in the interval 30-37 can be based on the value on the function graph corresponding to low-speed, and the membership degree corresponding to the vehicle speed in the interval 37-40 can be based on the value on the function graph corresponding to medium-speed. Through the first fuzzy relation, the vehicle speed information can be fuzzified into one of three speed states: low-speed, medium-speed, or high-speed.

[0073] The second fuzzy relation can include a second membership function for deceleration information, such as... Figure 3As shown, different decelerations belong to different deceleration states, which can include low deceleration, medium deceleration, and high deceleration. Specifically, the second membership function can be defined as an average deceleration of less than 0.3 m / s². 2 The low-deceleration portion has an average deceleration greater than 0.2 m / s². 2 And less than 0.7 m / s 2 The intermediate deceleration portion and the average deceleration greater than 0.6 m / s 2 It consists of a high deceleration component. (Targeting) Figure 3 The overlapping portions of the function graphs corresponding to low-to-medium deceleration, medium deceleration, and high deceleration can be represented by values ​​with higher membership degrees, which can be used as the membership degrees of the deceleration in the overlapping portions. Through a second fuzzy relation, the deceleration information can be fuzzified into one of three deceleration states: low deceleration, medium deceleration, or high deceleration.

[0074] The third fuzzy relation can include the 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, which can include small brake pedal opening, medium brake pedal opening, and high brake pedal opening. For... Figure 4 The overlapping portion of the function graphs corresponding to small, medium, and high brake pedal openings can be used as the membership degree of the brake pedal opening in the overlapping portion, with the value having the higher membership degree. Through a third fuzzy relation, the brake pedal opening information can be fuzzified into one of three brake pedal opening states: small, medium, and high.

[0075] In this way, by fuzzifying the target operation information through preset fuzzy relationships, the vehicle's operation information can be classified into corresponding operation states, which makes it easier to determine the driver's driving type more accurately in the future.

[0076] Figure 5 This is a flowchart illustrating another vehicle control method according to an exemplary embodiment, such as... Figure 5 As shown, step S101 can be implemented in the following way:

[0077] Step S1011: Obtain multiple real-time operating information of the vehicle within a preset time period.

[0078] Step S1012: The average value of multiple real-time running information is used as the target running information.

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

[0080] Taking real-time operating information, including real-time vehicle speed, real-time deceleration, and real-time brake pedal opening, as an example, the average of multiple real-time vehicle speed information over a preset time period, i.e., the average vehicle speed, can be used as the target vehicle speed information. Similarly, the average of multiple real-time deceleration information over a preset time period, i.e., the average deceleration, can be used as the target deceleration information. Furthermore, the average of multiple real-time brake pedal opening information over a preset time period, i.e., the average brake pedal opening, can be used as the target brake pedal opening information. The average vehicle speed can be calculated using Formula 1, the average deceleration using Formula 2, and the average brake pedal opening using Formula 3.

[0081]

[0082] Among them, v ave Here, v is the average vehicle speed, and t is the real-time vehicle speed. v This refers to the moment when the vehicle speed is not zero.

[0083]

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

[0085]

[0086] Among them, s ave s represents the average brake pedal opening, and s represents 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 makes it easier to determine the target driving state and the driver's target driving type more accurately in the future.

[0088] Figure 6 This is a flowchart illustrating another vehicle control method according to an exemplary embodiment, such as... Figure 6 As shown, step S103 can be achieved through the following steps:

[0089] Step S1031: Determine the driver's target driving type based on the first preset correspondence and the target driving state, wherein the first preset correspondence may include the correspondence between driving state and driving type.

[0090] For example, multiple driving types and a first preset correspondence between driving states and driving types can be predefined. Driving types can include at least one of conservative, semi-conservative, normal, semi-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. The target driving type can be understood as the driving type of the driver determined based on the target driving state. The first preset correspondence can be the fuzzy inference rules shown in Table 1.

[0091]

[0092] Table 1

[0093] Step S1032: Determine the target torque based on the target driving type.

[0094] For example, candidate torques can first be obtained based on current vehicle speed and brake pedal opening information. Then, the candidate torques are corrected according to the target driving type to obtain the target torque. The feedback torque of the vehicle's motor is controlled based on the target torque to control vehicle braking and simultaneously recover braking energy. The physical meaning of energy recovery intensity is deceleration; the greater the deceleration, the greater the energy recovery intensity. Since energy recovery intensity is controlled by the motor's feedback torque, the greater the feedback torque, the greater the energy recovery intensity. Therefore, a torque correction coefficient corresponding to each driving type can be pre-set. This coefficient is used to correct the candidate torque to control the energy recovery intensity. For example, the torque correction coefficients for conservative, semi-conservative, normal, semi-aggressive, and aggressive driving types can decrease sequentially. Correspondingly, the energy recovery intensities for these driving types also decrease sequentially, adapting to different driver styles and making the energy recovery intensity closer to the driver's braking habits. This effectively reduces the frequency of the driver's pedal input and improves energy recovery efficiency.

[0095] Figure 7 This is a flowchart illustrating another vehicle control method according to an exemplary embodiment, such as... Figure 7 As shown, step S1032 can be achieved through the following steps:

[0096] Step S1032a: Determine the first candidate torque based on 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. A second candidate torque can be determined based on the first current operating information and a 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 be, for example, a table showing the correspondence 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 can be found in the table. Alternatively, the second preset correspondence may be a function showing the correspondence between the first current operating information and the second candidate torque. After obtaining the first current operating information, the first current operating information can be substituted into the function to obtain the second candidate torque. This disclosure does not specifically limit this approach.

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

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

[0100] Step S1032b: Determine the dynamic mass of the vehicle based on the second current operating information, wherein the dynamic mass can characterize the actual mass of the vehicle in the current operating state.

[0101] For example, the second current operating information may include vehicle speed information, acceleration information, and vehicle motor torque. In some embodiments, because the dynamic mass of the vehicle during operation differs from its factory curb weight due to passenger loads and forces acting on it, the longitudinal force relationships of the vehicle can be determined based on the second current operating information. These longitudinal force relationships may include the dynamic relationships between rolling resistance, wind resistance, gradient resistance, and acceleration resistance and the longitudinal forces. Then, based on these longitudinal force relationships, the dynamic mass is determined using a recursive least squares model.

[0102] In one possible implementation, based on the fundamental formula of automotive 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 These correspond to the vehicle's longitudinal force, rolling resistance, wind resistance, gradient resistance, and acceleration resistance, respectively. Specifically, they can be written as:

[0105]

[0106] Among them, T tq Let i be the motor torque, i0 be the reducer ratio, r be the tire rolling radius, m be the vehicle's dynamic mass, f be the rolling resistance coefficient, ρ be the air density, and C be the torque of the motor. d Where A is the air resistance coefficient, θ is the vehicle's frontal area, and θ is the road slope. This represents the actual acceleration of the vehicle. Since the acceleration signal acquired by the vehicle is collected by a longitudinal acceleration sensor, the acceleration 'a' collected by the longitudinal acceleration sensor satisfies:

[0107]

[0108] Therefore, the formula for longitudinal dynamics of a car simplifies to:

[0109]

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

[0111]

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

[0113] M(0)=ε(Formula 12)

[0114] Where K(k) and P(k) are intermediate quantities in the least squares model, a is a sufficiently large positive real number, E is the identity 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: Determine the target torque based on the first candidate torque and dynamic mass.

[0116] For example, a mass correction factor can be determined based on the vehicle's dynamic mass. In one possible implementation, the vehicle's curb weight can first be obtained, and then the ratio of dynamic mass to curb weight can be used as the mass correction factor. Here, curb weight characterizes the vehicle's mass at the time of manufacture. For example, the mass correction factor can be calculated using Formula 13.

[0117]

[0118] Where μ is the mass correction factor and m0 is the vehicle's curb weight.

[0119] In other embodiments, the first candidate torque can be corrected according to a mass correction factor to obtain the target torque. For example, the target torque can be obtained by multiplying the mass correction factor by the first candidate torque using Formula 14.

[0120] T2=μT1 (Formula 14)

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

[0122] In summary, this method first acquires the vehicle's target operating information and then performs fuzzification processing on this information to obtain the target driving state. Next, it determines the driver's target driving type based on a first preset correspondence and the target driving state, where the first preset correspondence includes the correspondence between driving states and driving types. Finally, it determines the target torque based on the target driving type and controls the vehicle's motor feedback torque to control vehicle braking. This disclosure fuzzifies the vehicle's operating information to obtain the driver's driving state, and controls the feedback torque during braking based on the driving state. This allows the feedback torque to adapt to different drivers' driving styles, thereby more flexibly and rationally controlling the intensity of regenerative braking and improving energy recovery efficiency.

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

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

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

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

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

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

[0129] The target operation information is fuzzified according to the preset fuzzy relationship, which includes the membership relationship between operation information and driving status.

[0130] Figure 9 This is a block diagram illustrating another vehicle control device according to an exemplary embodiment, such as... Figure 9 As shown, the determining module 203 includes:

[0131] The first determining submodule 2031 is configured to determine the driver's target driving type based on a first preset correspondence and the target driving state. The first preset correspondence includes the correspondence between driving state and driving type.

[0132] The second determining submodule 2032 is configured to determine the target torque based on the target driving type.

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

[0134] The first candidate torque is determined based on the first current operating information and the target driving type.

[0135] Based on the second current operating information, the dynamic mass of the vehicle is determined. The dynamic mass characterizes the actual mass of the vehicle under the current operating condition.

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

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

[0138] The second candidate torque is determined based on the first current operating information and the second preset correspondence, wherein the second preset correspondence includes the correspondence between the first current operating information and the second candidate torque.

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

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

[0141] The quality correction factor is determined based on the dynamic quality.

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

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

[0144] Obtain the vehicle's curb weight, which represents the vehicle's quality at the time of manufacture.

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

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

[0147] Based on the second current operating information, the longitudinal force relationship of the vehicle is determined. The longitudinal force relationship includes the dynamic relationship between the vehicle's rolling resistance, wind resistance, gradient resistance, and acceleration resistance and the longitudinal force.

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

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

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

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

[0152] In other embodiments, the target operating 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, semi-conservative, normal, semi-aggressive, and aggressive.

[0153] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0154] In summary, this method first acquires the vehicle's target operating information and then performs fuzzification processing on this information to obtain the target driving state. Next, it determines the driver's target driving type based on a first preset correspondence and the target driving state, where the first preset correspondence includes the correspondence between driving states and driving types. Finally, it determines the target torque based on the target driving type and controls the vehicle's motor feedback torque to control vehicle braking. This disclosure fuzzifies the vehicle's operating information to obtain the driver's driving state, and controls the feedback torque during braking based on the driving state. This allows the feedback torque to adapt to different drivers' driving styles, thereby more flexibly and rationally controlling the intensity of regenerative braking and improving energy recovery efficiency.

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

[0156] The processor 301 controls the overall operation of the vehicle 300 to complete all or part of the steps in the aforementioned vehicle control method. The memory 302 stores various types of data to support the operation of the vehicle 300. This data may include, for example, instructions for any application or method operating on the vehicle 300, and application-related data such as contact data, sent and received messages, images, audio, video, etc. The memory 302 can be implemented using any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. Multimedia component 303 may include a screen and an audio component. The screen may be, for example, a touchscreen, 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 signals may be further stored in memory 302 or transmitted via communication component 305. The audio component also includes at least one speaker for outputting audio signals. I / O interface 304 provides an interface between processor 301 and other interface modules, such as a keyboard, mouse, buttons, etc. These buttons may be virtual or physical buttons. Communication component 305 is used for wired or wireless communication between 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 technologies, or combinations thereof, is not limited here. Therefore, the corresponding communication component 305 may include: a Wi-Fi module, a Bluetooth module, an NFC module, etc.

[0157] In an exemplary embodiment, the vehicle 300 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to execute the vehicle control method described above.

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

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

[0160] The preferred embodiments of this disclosure have been described in detail above with reference to the accompanying drawings. However, this disclosure is not limited to the specific details of the above embodiments. Within the scope of the technical concept of this disclosure, various simple modifications can be made to the technical solutions of this disclosure, and these simple modifications all fall within the protection scope of this 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, this disclosure will not describe the various possible combinations separately.

[0162] Furthermore, various different embodiments of this disclosure can be combined in any way, as long as they do not violate the spirit of this disclosure, they should also be regarded as the content disclosed in this disclosure.

Claims

1. A method for vehicle control, characterized in that, The method includes: Obtain the target operating information of the vehicle; The target's operational information is then fuzzified to obtain the target's driving state; The driver's target driving type is determined based on a first preset correspondence and the target driving state, wherein the first preset correspondence includes the correspondence between driving state and driving type; A first candidate torque is determined based on the first current operating information and the target driving type; Based on the second current operating information, the dynamic mass of the vehicle is determined, and the dynamic mass characterizes the actual mass of the vehicle in the current operating state; The target torque is determined based on the first candidate torque and the dynamic mass; The vehicle's motor feedback torque is controlled according to the target torque to control the vehicle's braking.

2. The method according to claim 1, characterized in that, The process of blurring the target runtime information includes: The target operation information is fuzzified according to a preset fuzzy relationship, which includes the membership relationship between operation information and driving status.

3. The method according to claim 1, characterized in that, Determining the first candidate torque based on the first current operating information and the target driving type includes: The second candidate torque is determined based on the first current operating information and the second preset correspondence, wherein the second preset correspondence includes the correspondence between the first current operating information and the second candidate torque. The second candidate torque is modified according to the target driving type to obtain the first candidate torque.

4. The method according to claim 1, characterized in that, Determining the target torque based on the first candidate torque and the dynamic mass includes: Determine the quality correction coefficient based on the dynamic quality; The first candidate torque is corrected according to the mass correction factor to obtain the target torque.

5. The method according to claim 4, characterized in that, The step of determining the quality correction factor based on the dynamic quality includes: Obtain the curb weight of the vehicle, which represents the weight of the vehicle at the time of manufacture; The ratio of the dynamic mass to the curb weight is used as the mass correction factor.

6. The method according to claim 1, characterized in that, Determining the dynamic quality of the vehicle based on the second current operating information includes: Based on the second current operating information, the longitudinal force relationship of the vehicle is determined, and the longitudinal force relationship includes the dynamic relationship between the vehicle's rolling resistance, wind resistance, slope resistance, and acceleration resistance and the longitudinal force. Based on the longitudinal force relationship, the dynamic mass is determined using a recursive least squares model.

7. The method according to claim 1, characterized in that, The acquisition of the vehicle's target operating information includes: Acquire multiple real-time operating information of the vehicle within a preset time period; The average value of the multiple real-time operating information is taken as the target operating information.

8. The method according to any one of claims 1-7, characterized in that, The target operating 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, semi-conservative, normal, semi-aggressive, and aggressive.

9. A vehicle, characterized in that, include: A memory on which computer programs are stored; A processor for executing the computer program in the memory to implement the steps of the method according to any one of claims 1-8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the method according to any one of claims 1-8.

11. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the steps of the method according to any one of claims 1-8.

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