Vehicle adaptive control method and device, vehicle and computer program product

Abstract

The invention is suitable for the technical field of automobiles, and provides a vehicle adaptive control method and device, a vehicle and a computer program product. The vehicle self-adaptive control method comprises the steps that a vehicle load is obtained, and real-time mass distribution of a vehicle is calculated according to the vehicle load and preset vehicle geometric structure parameters; acquiring a longitudinal slope angle of the vehicle, performing dynamic gravity center estimation based on the real-time mass distribution, and outputting a gravity center position in real time; according to the longitudinal slope angle, the gravity center position and a preset correlation model, a driving force and braking force distribution strategy is corrected; and controlling a driving actuator and a braking actuator of the vehicle according to the corrected driving force and braking force distribution strategy. According to the embodiment of the invention, the accuracy of driving or braking control is improved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of automobiles, and particularly relates to a vehicle adaptive control method and device, a vehicle, and a computer program product. BACKGROUND

[0002] In the intelligent control process of a vehicle, the vehicle can replace mechanical linkage with electrical signals, comprehensively consider the intention of a driver and automatic driving decisions, and realize throttle and brake functions through driving and braking actuators. Related technologies usually control the response of driving and braking of a vehicle based on a calibration curve. However, the calibration curve is mostly related to the static state of the vehicle, and in actual application, the driving or braking may not be accurate. SUMMARY

[0003] Embodiments of the present application provide a vehicle adaptive control method and device, a vehicle, and a computer program product, which can improve the accuracy of driving or braking control.

[0004] The first aspect of embodiments of the present application provides a vehicle adaptive control method, including: obtaining a vehicle load, and calculating a real-time mass distribution of the vehicle according to the vehicle load and a preset vehicle geometric structure parameter; obtaining a longitudinal slope angle of the vehicle, and performing dynamic gravity center estimation based on the real-time mass distribution to output a gravity center position in real time; correcting a driving force and braking force distribution strategy according to the longitudinal slope angle, the gravity center position, and a preset correlation model; and controlling driving actuators and braking actuators of the vehicle according to the corrected driving force and braking force distribution strategy.

[0005] In some embodiments of the first aspect, the vehicle adaptive control method further includes: collecting driving accelerations of the vehicle under a fixed driving force at a plurality of different gravity center positions, fitting a first functional relationship between the gravity center position and the driving acceleration based on a linear regression algorithm; collecting braking decelerations of the vehicle under a fixed braking force at a plurality of different gravity center positions, fitting a second functional relationship between the gravity center position and the braking deceleration based on a linear regression algorithm; and determining the first functional relationship and the second functional relationship as the preset correlation model.

[0006] In some embodiments of the first aspect, the correcting of the driving force and braking force distribution strategy according to the longitudinal slope angle, the gravity center position, and the preset correlation model includes: determining a slope interval according to the longitudinal slope angle, and determining corresponding driving compensation coefficients and braking compensation coefficients; calculating a basic driving demand according to the current gravity center position based on the first functional relationship, and multiplying the basic driving demand by the driving compensation coefficients to obtain a corrected driving force; and calculating a basic braking demand according to the current gravity center position based on the second functional relationship, and multiplying the basic braking demand by the braking compensation coefficients to obtain a corrected braking force.

[0007] In some embodiments of the first aspect, the vehicle adaptive control method further comprises: when going uphill, the driving compensation coefficient corresponding to the slope interval is greater than 1, and the braking compensation coefficient corresponding to the slope interval is less than 1; when going downhill, the driving compensation coefficient corresponding to the slope interval is less than 1, and the braking compensation coefficient corresponding to the slope interval is greater than 1.

[0008] In some embodiments of the first aspect, the vehicle load includes a water tank and a garbage tank fixedly installed; the vehicle load is acquired, and real-time mass distribution of the vehicle is calculated according to the vehicle load and preset vehicle geometric structure parameters, comprising: liquid level information of the water tank is acquired through a water level sensor; load information of the garbage tank is acquired through a weight sensor; and the real-time mass distribution of the vehicle is calculated according to the liquid level information of the water tank, the load information of the garbage tank and the vehicle geometric structure parameters.

[0009] In some embodiments of the first aspect, the longitudinal slope angle of the vehicle is acquired, and dynamic center of gravity estimation is performed based on the real-time mass distribution to output the center of gravity position in real time, comprising: the longitudinal slope angle of the vehicle is acquired, and the dynamic center of gravity estimation is performed through hybrid extended Kalman filtering or extended Kalman filtering algorithm according to the real-time mass distribution, and a state equation contains a model based on vehicle chassis configuration coefficients, mass center wheelbase and suspension compression compensation amount.

[0010] In some embodiments of the first aspect, the vehicle adaptive control method further comprises: monitoring actual starting response speed and braking distance of the vehicle according to an odometer and a speed sensor; comparing the monitored results with expected targets, and updating parameters of the preset correlation model and / or driving compensation coefficients and braking compensation coefficients corresponding to the slope intervals in real time according to the comparison results.

[0011] The second aspect of the embodiments of the present application provides a control device of a vehicle, comprising: a mass distribution acquisition unit configured to acquire a vehicle load, and calculate real-time mass distribution of the vehicle according to the vehicle load and preset vehicle geometric structure parameters; a center of gravity acquisition unit configured to acquire a longitudinal slope angle of the vehicle, and perform dynamic center of gravity estimation based on the real-time mass distribution to output a center of gravity position in real time; a power distribution unit configured to correct driving force and braking force distribution strategies according to the longitudinal slope angle, the center of gravity position and a preset correlation model; and a control unit configured to control driving actuators and braking actuators of the vehicle according to the corrected driving force and braking force distribution strategies.

[0012] The third aspect of the embodiments of the present application provides a vehicle, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the vehicle adaptive control method when executing the computer program.

[0013] The fourth aspect of the embodiments of the present application provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the steps of the vehicle adaptive control method.

[0014] The fifth aspect of the embodiments of the present application provides a computer program product, which makes the vehicle adaptive control method be executed when the computer program is run.

[0015] In the embodiments of the present application, by acquiring the vehicle load, calculating the real-time mass distribution of the vehicle according to the vehicle load and the preset vehicle geometric structure parameters, acquiring the longitudinal slope angle of the vehicle, and performing dynamic gravity center estimation based on the real-time mass distribution, the real-time gravity center position is output, the driving force and braking force distribution strategy is corrected according to the longitudinal slope angle, the gravity center position and the preset correlation model, and the driving actuator and the braking actuator of the vehicle are controlled according to the corrected driving force and braking force distribution strategy, which can avoid the phenomenon of excessive control or insufficient control due to the change of the gravity center position of the vehicle with the mass distribution, and improve the accuracy of driving or braking control. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or prior art description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0017] Figure 1 is an implementation flow diagram of a vehicle adaptive control method provided by the embodiments of the present application;

[0018] Figure 2 is a structural schematic diagram of a control device of a vehicle provided by the embodiments of the present application;

[0019] Figure 3 is a structural schematic diagram of a vehicle provided by the embodiments of the present application. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solutions and advantages of the present application more clear, the present application will be further described in detail below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application, and are not used to limit the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0021] The application provides a vehicle adaptive control method, which can avoid excessive braking / driving or insufficient braking / driving due to changes in the center of gravity of the vehicle with the mass distribution, and improves the accuracy of driving or braking control.

[0022] In order to illustrate the technical solutions of the application, specific examples are used in the following description.

[0023] Figure 1 An implementation flowchart of a vehicle adaptive control method provided by an embodiment of the application is shown, and the method can be applied to a vehicle.

[0024] Specifically, the vehicle adaptive control method can include the following steps S101 to S104.

[0025] Step S101, acquiring the vehicle load, and calculating the real-time mass distribution of the vehicle according to the vehicle load and the preset vehicle geometric structure parameters.

[0026] The vehicle load refers to the weight carried by the vehicle, which can be determined by a weight sensor or a related weight estimation algorithm. The vehicle geometric structure parameters represent the structure of the vehicle, including but not limited to the external dimensions (length, width, height) of the vehicle, wheelbase, track, suspension parameters, and chassis configuration coefficients.

[0027] The real-time mass distribution of the vehicle represents the distribution of the overall mass of the vehicle. It can be understood that the vehicle load and the vehicle geometric structure parameters reflect the mass changes at each position of the vehicle, thereby being used to calculate the real-time mass distribution of the vehicle.

[0028] Step S102, acquiring the longitudinal slope angle of the vehicle, and performing dynamic center of gravity estimation based on the real-time mass distribution to output the center of gravity position in real time.

[0029] The longitudinal slope angle is the slope angle of the vehicle along the driving direction, which can be determined by an inertial measurement unit (IMU).

[0030] The dynamic center of gravity estimation based on the real-time mass distribution can predict the changes in the center of gravity with the mass distribution, and then be used to output the center of gravity position in real time.

[0031] Step S103, correcting the driving force and braking force distribution strategy according to the longitudinal slope angle, the center of gravity position, and a preset correlation model.

[0032] The preset correlation model is used to map the longitudinal slope angle and the gravity center position to a working parameter required by the driving force and the braking force, for example, the working parameter can be acceleration, deceleration, etc. In the embodiments of the present application, according to the longitudinal slope angle, the gravity center position and the preset correlation model, the working parameter matched with the current longitudinal slope angle and the gravity center position can be mapped to correct the driving force and braking force distribution strategy.

[0033] In step S104, the driving and braking actuators of the vehicle are controlled according to the corrected driving and braking force distribution strategy.

[0034] In the embodiments of the present application, the corrected driving and braking force distribution strategy can be sent to the driving and braking actuators of the vehicle to control the driving and braking actuators of the vehicle to start, accelerate or brake, so that the starting, accelerating or braking operation can be adapted to the current longitudinal slope angle and the gravity center position.

[0035] In the embodiments of the present application, by acquiring the vehicle load, calculating the real-time mass distribution of the vehicle according to the vehicle load and the preset vehicle geometric structure parameters, acquiring the longitudinal slope angle of the vehicle, and dynamically estimating the gravity center based on the real-time mass distribution, the gravity center position is output in real time, the driving force and braking force distribution strategy is corrected according to the longitudinal slope angle, the gravity center position and the preset correlation model, and the driving and braking actuators of the vehicle are controlled according to the corrected driving and braking force distribution strategy, which can avoid the phenomenon of excessive control or insufficient control due to the change of the gravity center position of the vehicle with the mass distribution, and improve the accuracy of driving or braking control.

[0036] In some embodiments of the present application, the vehicle load includes a fixedly installed water tank and a garbage tank. The above-mentioned acquiring the vehicle load and calculating the real-time mass distribution of the vehicle according to the vehicle load and the preset vehicle geometric structure parameters include: acquiring liquid level information of the water tank through a water level sensor; acquiring load information of the garbage tank through a weight sensor; and calculating the real-time mass distribution of the vehicle according to the liquid level information of the water tank, the load information of the garbage tank and the vehicle geometric structure parameters.

[0037] Specifically, the water tank is provided with a liquid level sensor for collecting liquid level information of the water tank, and the liquid level information can reflect the volume of the liquid in the water tank, and then the load information of the water tank can be determined. The garbage tank is provided with a weight sensor for collecting load information of the garbage tank, and the load information can represent the weight of the garbage in the garbage tank. The vehicle geometric structure parameters can reflect the weight of the vehicle body.

[0038] According to the liquid level information of the water tank, the load information of the garbage tank and the vehicle geometric structure parameters, the total mass of the vehicle can be calculated to obtain the real-time mass distribution of the vehicle.

[0039] Specifically, the total weight of the vehicle can be represented as total weight M total = M body + M water +

[0040] M rubbish . Wherein, M body is the body weight, M water is the load information of the water tank, M rubbish is the load information of the garbage tank.

[0041] In some embodiments of the present application, the longitudinal slope angle of the vehicle is obtained, and the dynamic center of gravity is estimated based on the real-time mass distribution, and the real-time output of the center of gravity position can include: obtaining the longitudinal slope angle of the vehicle, and according to the real-time mass distribution, the dynamic center of gravity is estimated by hybrid extended Kalman filtering or extended Kalman filtering algorithm.

[0042] Wherein, the state equation contains a model based on vehicle chassis configuration coefficient, mass center wheelbase and suspension compression compensation amount. Wherein, the mass center wheelbase is the wheelbase of the mass center of the water tank and the garbage tank and the center of the front axle / rear axle of the vehicle.

[0043] Specifically, the center of gravity position can include longitudinal position X cg and height H cg .

[0044] The longitudinal position X cg can be represented as:

[0045]

[0046] Wherein, K1 and K2 are the chassis configuration coefficients of the vehicle. K1 represents the rate of change of the longitudinal position of the center of gravity when the mass of the water tank and the garbage tank changes, which is related to the difference between the longitudinal distance of the mass center of the water tank and the mass center of the vehicle and the difference between the longitudinal distance of the mass center of the garbage tank and the mass center of the vehicle. K2 represents the rate of change of the longitudinal position of the center of gravity when the slope changes, which is related to the height of the mass center of the vehicle. L water and L rubbish are the mass center wheelbases respectively.

[0047] The height H cg can be represented as:

[0048]

[0049] Wherein, h0 is the initial center of gravity height when the vehicle is empty, Δh is the suspension compression compensation amount, and k is the height coefficient.

[0050] In order to facilitate the correction of the driving force and braking force distribution strategy, the preset correlation model can be calibrated in advance.

[0051] In some embodiments of the present application, the vehicle adaptive control method further comprises: collecting driving acceleration of the vehicle under a fixed driving force at a plurality of different center of gravity positions, fitting a first functional relationship between the center of gravity position and the driving acceleration based on a linear regression algorithm; and collecting braking deceleration of the vehicle under a fixed braking force at a plurality of different center of gravity positions, fitting a second functional relationship between the center of gravity position and the braking deceleration based on a linear regression algorithm. The first functional relationship and the second functional relationship are determined as a preset correlation model.

[0052] Specifically, the center of gravity position of the vehicle can be measured under different loading conditions to obtain the longitudinal position X cg On flat ground, straight-line acceleration and braking tests are performed at a fixed driving force and a fixed braking force, and sample data of driving acceleration a and braking deceleration d are recorded.

[0053] Sample data is as follows:

[0054]

[0055] At this time, using a linear regression method, a first functional relationship between the center of gravity position and the driving acceleration and a second functional relationship between the center of gravity position and the braking deceleration can be fitted. Specifically, a corresponding relationship between the longitudinal position X cg and the driving acceleration a and the braking deceleration d can be established to obtain the first functional relationship and the second functional relationship. The first functional relationship and the second functional relationship can be expressed in the form of a unary function. For example, the first functional relationship a(X cg )=k a ·X cg +b a , and the second functional relationship d(X cg )=k d ·X cg +b d .

[0056] Wherein, k a and k d are negative values, so that when the center of gravity moves backward, the front axle load decreases, and both the driving force and the braking force decrease.

[0057] Considering that the slope will introduce an additional gravity component g·sin(θ) to affect the longitudinal acceleration and deceleration capability, it is necessary to correct for different longitudinal slope angles.

[0058] In some embodiments of the present application, the correction of the driving force and braking force distribution strategy according to the longitudinal slope angle, the center of gravity position and the preset correlation model can include: determining the slope interval according to the longitudinal slope angle, and determining the corresponding driving compensation coefficient and braking compensation coefficient; calculating the basic driving demand according to the current center of gravity position based on the first function relationship, and multiplying the basic driving demand by the driving compensation coefficient to obtain the corrected driving force; calculating the basic braking demand according to the current center of gravity position based on the second function relationship, and multiplying the basic braking demand by the braking compensation coefficient to obtain the corrected braking force.

[0059] Specifically, in the case of different slopes and fixed weight, straight line acceleration and braking tests can be carried out with fixed driving force and fixed braking force, and sample data of driving acceleration a and braking deceleration d are recorded to determine the compensation coefficient required by the response information corresponding to the weight.

[0060] The compensation coefficient of the slope is as follows:

[0061] Slope interval Drive compensation factor Brake compensation factor -5° to -3° (downhill) 0.85 1.10 -3° to -1° (slight downhill) 0.95 1.05 -1° to 1° (flat) 1.00 1.00 1° to 3° (slight uphill) 1.05 0.95 3° to 5° (uphill) 1.15 0.90

[0062] At this time, the slope interval can be determined according to the longitudinal slope angle, and the corresponding driving compensation coefficient and braking compensation coefficient can be determined. At this time, the basic driving demand can be calculated according to the current center of gravity position based on the first function relationship, and the basic driving demand is multiplied by the driving compensation coefficient to obtain the corrected driving force. And based on the second function relationship, the basic braking demand is calculated according to the current center of gravity position, and the basic braking demand is multiplied by the braking compensation coefficient to obtain the corrected braking force. The basic driving demand and the basic braking demand here can be used to determine the driving acceleration and the driving deceleration.

[0063] For example, the center of gravity position X cg = 0.95m, according to the first mapping relationship, the basic driving demand is 40% of the maximum driving force, and the basic braking demand is 30% of the maximum braking force. The current slope is 4, which is located in the slope interval of 3°~5 of the above table, and the driving compensation coefficient is 1.15 and the braking compensation coefficient is 0.90 according to the table. Then the corrected driving force is 40% of the maximum driving force x 1.15. The corrected braking force is 30% of the maximum braking force x 0.9. Thus, the double-dimensional adaptive calibration based on the center of gravity position and the slope can be realized, and the reliability of the driving or braking response can be improved.

[0064] In particular, in the embodiments of the present application, when going uphill, the driving compensation coefficient corresponding to the slope interval is greater than 1, and the braking compensation coefficient corresponding to the slope interval is less than 1. When going downhill, the driving compensation coefficient corresponding to the slope interval is less than 1, and the braking compensation coefficient corresponding to the slope interval is greater than 1. In this way, when starting uphill, stronger driving force can be provided to avoid insufficient front wheel grip, slow start and slipping; when braking downhill, stronger braking force can be provided to avoid insufficient braking force and increased braking distance.

[0065] In some embodiments of the present application, the vehicle adaptive control method can further include: monitoring the actual start-up response speed and braking distance of the vehicle according to the odometer and the speed sensor; comparing the monitored results with the expected target, and updating the parameters of the preset correlation model and / or the driving compensation coefficient and the braking compensation coefficient corresponding to the slope interval in real time according to the comparison results.

[0066] Specifically, the IMU can obtain the speed and displacement of the vehicle by acceleration integration, and then fuse the IMU and wheel speed data by using a Kalman filter or a complementary filter to obtain the real-time speed of the vehicle. When it is detected that the brake switch is pressed, it is determined that braking starts, and the actual start-up response speed v_start and the starting position s_start at this time are recorded. When the real-time speed v_est falls below a speed threshold value (such as <0.1 km / h), it is determined that braking ends, and the ending position s_end is recorded. At this time, the braking distance s_actual=s_end-s_start can be calculated. The driving force and braking force distribution strategy obtained according to the foregoing method can be used as the expected target. The expected target can include a braking distance expectation s_desired and a start-up response speed expectation v_desired, and then the comparison results error_s=s_actual-s_desired and / or error_v=v_actual-v_desired are obtained.

[0067] error_s>0, indicating that the actual braking distance is longer than the expected one, which means that the braking force is insufficient, and the braking pressure needs to be increased by updating the parameters of the preset correlation model and / or the driving compensation coefficient and the braking compensation coefficient corresponding to the slope interval. error_s<0, indicating that the actual braking distance is shorter than the expected one, which means that the braking force is too strong, and the braking pressure needs to be reduced by updating the parameters of the preset correlation model and / or the driving compensation coefficient and the braking compensation coefficient corresponding to the slope interval. The driving acceleration process is the same, and will not be described herein.

[0068] Therefore, the preset correlation model and the compensation coefficient can be iteratively updated according to the actual response of the vehicle, so that the driving or braking response is more accurate, and the to-point accuracy and speed stability can be maintained at the end of a slope or a turning position, which is beneficial to the accurate execution of the automatic cleaning path when the vehicle performs the cleaning task.

[0069] It should be noted that, for the foregoing method embodiments, in order to simply describe, they are all expressed as a series of action combinations, but those skilled in the art should know that the present application is not limited by the action sequence described, because according to the present application, certain steps can be performed in other sequences.

[0070] As Figure 2 Fig. 2 shows a structural schematic diagram of a vehicle control device 200 provided by an embodiment of the present application, which is arranged on a vehicle.

[0071] Specifically, the vehicle control device 200 can include:

[0072] a mass distribution acquisition unit 201 configured to acquire a vehicle load, and calculate a real-time mass distribution of the vehicle according to the vehicle load and a preset vehicle geometric structure parameter;

[0073] a gravity center acquisition unit 202 configured to acquire a longitudinal slope angle of the vehicle, and perform dynamic gravity center estimation based on the real-time mass distribution to output a gravity center position in real time;

[0074] a power distribution unit 203 configured to correct a driving force and braking force distribution strategy according to the longitudinal slope angle, the gravity center position and a preset correlation model;

[0075] a control unit 204 configured to control driving actuators and braking actuators of the vehicle according to the corrected driving force and braking force distribution strategy.

[0076] In some embodiments of the present application, the vehicle control device 200 further includes a calibration unit, which is specifically configured to: collect driving accelerations of the vehicle under a fixed driving force at a plurality of different gravity center positions, and fit a first functional relationship between the gravity center position and the driving acceleration based on a linear regression algorithm; collect braking decelerations of the vehicle under a fixed braking force at a plurality of different gravity center positions, and fit a second functional relationship between the gravity center position and the braking deceleration based on a linear regression algorithm; and determine the first functional relationship and the second functional relationship as the preset correlation model.

[0077] In some embodiments of the present application, the power distribution unit 203 can be specifically configured to: determine the slope interval according to the longitudinal slope angle, and determine the corresponding driving compensation coefficient and braking compensation coefficient; calculate the basic driving demand according to the current center of gravity position based on the first function relationship, and multiply the basic driving demand by the driving compensation coefficient to obtain the corrected driving force; calculate the basic braking demand according to the current center of gravity position based on the second function relationship, and multiply the basic braking demand by the braking compensation coefficient to obtain the corrected braking force.

[0078] In some embodiments of the present application, when climbing uphill, the driving compensation coefficient corresponding to the slope interval is greater than 1, and the braking compensation coefficient corresponding to the slope interval is less than 1; when descending, the driving compensation coefficient corresponding to the slope interval is less than 1, and the braking compensation coefficient corresponding to the slope interval is greater than 1.

[0079] In some embodiments of the present application, the vehicle load includes a fixedly installed water tank and a garbage tank; the mass distribution acquisition unit 201 can be configured to: acquire the liquid level information of the water tank through a water level sensor; acquire the load information of the garbage tank through a weight sensor; and calculate the real-time mass distribution of the vehicle according to the liquid level information of the water tank, the load information of the garbage tank, and the vehicle geometric structure parameters.

[0080] In some embodiments of the present application, the center of gravity acquisition unit 202 can be configured to: acquire the longitudinal slope angle of the vehicle, and estimate the dynamic center of gravity according to the real-time mass distribution by using a hybrid extended Kalman filter or an extended Kalman filter algorithm, wherein the state equation includes a model based on vehicle chassis configuration coefficients, mass center wheelbase, and suspension compression compensation amount.

[0081] In some embodiments of the present application, the control device 200 of the vehicle further includes an updating unit, which is specifically configured to: monitor the actual starting response speed and braking distance of the vehicle according to the odometer and the speed sensor; compare the monitored results with the expected target, and update the parameters of the preset correlation model and / or the driving compensation coefficient and the braking compensation coefficient corresponding to the slope interval in real time according to the comparison results.

[0082] It should be noted that, for the convenience and brevity of description, the specific working process of the control device 200 of the vehicle described above can be referred to Figure 1 The corresponding process of the method, which will not be repeated here.

[0083] As Figure 3As shown in FIG. 1, a schematic diagram of a vehicle is provided. Specifically, the vehicle 3 can include a processor 30, a memory 31, and a computer program 32 stored in the memory 31 and executable on the processor 30, for example, a control program of the vehicle. The processor 30 implements the steps in each of the vehicle adaptive control method embodiments described above when executing the computer program 32, for example Figure 1 As shown in steps S101-S104. Alternatively, the processor 30 implements the functions of each module / unit in each of the device embodiments described above when executing the computer program 32, for example Figure 2 As shown in the functions of the mass distribution acquisition unit 204, the center of gravity acquisition unit 202, the power distribution unit 203, and the control unit 204.

[0084] The computer program can be divided into one or more modules / units, which are stored in the memory 31 and executed by the processor 30 to complete the present application. The one or more modules / units can be a series of computer program instruction segments that can complete a specific function, which are used to describe the execution process of the computer program in the vehicle.

[0085] For example, the computer program can be divided into a mass distribution acquisition unit, a center of gravity acquisition unit, a power distribution unit, and a control unit. The specific functions of each unit are as follows: the mass distribution acquisition unit is configured to acquire the vehicle load and calculate the real-time mass distribution of the vehicle according to the vehicle load and preset vehicle geometric structure parameters; the center of gravity acquisition unit is configured to acquire the longitudinal slope angle of the vehicle and perform dynamic center of gravity estimation based on the real-time mass distribution to output the center of gravity position in real time; the power distribution unit is configured to correct the driving force and braking force distribution strategy according to the longitudinal slope angle, the center of gravity position, and a preset correlation model; and the control unit is configured to control the driving actuator and the braking actuator of the vehicle according to the corrected driving force and braking force distribution strategy.

[0086] The vehicle can include, but is not limited to, the processor 30, the memory 31. Those skilled in the art can understand that, Figure 3 The vehicle is only an example and does not constitute a limitation on the vehicle, which can include more or fewer components than shown, or combine certain components, or different components, for example, the vehicle can also include an input / output device, a network access device, a bus, etc.

[0087] The processor 30 can be a central processing unit (CPU), and can also be other general-purpose processors, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a programmable logic device (PLD), a discrete gate or transistor logic, a discrete hardware component, or the like. The general-purpose processor can be a microprocessor, or the like.

[0088] The memory 31 can be an internal storage unit of the vehicle, such as a hard disk or a memory of the vehicle. The memory 31 can also be an external storage device of the vehicle, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, or the like. Further, the memory 31 can include both the internal storage unit and the external storage device of the vehicle. The memory 31 is used to store the computer program and other programs and data required by the vehicle. The memory 31 can also be used to temporarily store data that has been output or is to be output.

[0089] It should be noted that, for the convenience and brevity of description, the structure of the vehicle can also refer to the specific description of the structure in the method embodiments, which will not be repeated here.

[0090] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above functional units and modules is exemplified, and in actual application, the above functions can be completed by different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit. In addition, the specific names of each functional unit and module are only for easy distinction, and do not limit the protection scope of the present application. The specific working process of the units and modules in the system can refer to the corresponding process in the foregoing method embodiments, which will not be repeated here.

[0091] In the above embodiments, the description of each embodiment has its own emphasis, and the parts not described or recorded in detail in a certain embodiment can be referred to the related description of other embodiments.

[0092] Those skilled in the art can understand that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. A person skilled in the art can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0093] In the embodiments provided in the present application, it should be understood that the disclosed devices / vehicles and methods can be implemented in other ways. For example, the device / vehicle embodiments described above are merely illustrative. For example, the division of the modules or units is only a logical function division, and there can be another division manner in actual implementation. For example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interface, device or unit, and can be electrical, mechanical or other forms.

[0094] The units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, they can be located in one place, or can be distributed on a plurality of network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiments.

[0095] In addition, each functional unit in each embodiment of the present application can be integrated into a processing unit, or each unit can exist physically independently, or two or more units can be integrated into one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0096] The integrated module / unit, if implemented in the form of a software functional unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, all or part of the processes in the above-mentioned embodiment methods can also be completed by a computer program instructing related hardware, and the computer program can be stored in a computer readable storage medium. The computer program can implement the steps of each method embodiment when executed by a processor. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or some intermediate forms. The computer readable medium can include any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the contents included in the computer readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction, for example, in some jurisdictions, according to legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals.

[0097] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A vehicle adaptive control method, characterized in that, include: The vehicle load is acquired, and the real-time mass distribution of the vehicle is calculated based on the vehicle load and preset vehicle geometric parameters. The longitudinal slope angle of the vehicle is obtained, and dynamic center of gravity estimation is performed based on the real-time mass distribution, and the center of gravity position is output in real time. Based on the longitudinal slope angle, the center of gravity position, and the preset correlation model, the driving force and braking force distribution strategy is corrected; The vehicle's drive actuators and brake actuators are controlled according to the modified drive and brake force distribution strategy.

2. The vehicle adaptive control method according to claim 1, characterized in that, Also includes: At multiple different center of gravity positions, the driving acceleration of the vehicle under a fixed driving force is collected. Based on the linear regression algorithm, the first functional relationship between the center of gravity position and the driving acceleration is fitted. At multiple different center of gravity positions, the braking deceleration of the vehicle under a fixed braking force was collected. Based on the linear regression algorithm, a second functional relationship between the center of gravity position and the braking deceleration was fitted. The first functional relationship and the second functional relationship are determined as the preset association model.

3. The vehicle adaptive control method according to claim 2, characterized in that, Based on the longitudinal slope angle, the center of gravity position, and the preset correlation model, the driving force and braking force distribution strategy is corrected, including: The slope range is determined based on the longitudinal slope angle, and the corresponding driving compensation coefficient and braking compensation coefficient are determined accordingly. Based on the first functional relationship, the basic driving demand is calculated according to the current center of gravity position, and the basic driving demand is multiplied by the driving compensation coefficient to obtain the corrected driving force; Based on the second functional relationship, the basic braking demand is calculated according to the current center of gravity position, and the basic braking demand is multiplied by the braking compensation coefficient to obtain the corrected braking force.

4. The vehicle adaptive control method according to claim 3, characterized in that, Also includes: When going uphill, the driving compensation coefficient corresponding to the slope range is greater than 1, and the braking compensation coefficient corresponding to the slope range is less than 1. When going downhill, the driving compensation coefficient corresponding to the slope range is less than 1, and the braking compensation coefficient corresponding to the slope range is greater than 1.

5. The vehicle adaptive control method according to any one of claims 1-4, characterized in that, The vehicle load includes a fixedly installed water tank and a garbage bin; Acquire the vehicle load and, based on the vehicle load and preset vehicle geometry parameters, calculate the real-time mass distribution of the vehicle, including: The water level information of the water tank is obtained through a water level sensor; The load information of the trash can is obtained through a weight sensor; The real-time mass distribution of the vehicle is calculated based on the water tank level information, the garbage bin load information, and the vehicle's geometric parameters.

6. The vehicle adaptive control method according to any one of claims 1-4, characterized in that, Obtain the vehicle's longitudinal slope angle and perform dynamic center of gravity estimation based on the real-time mass distribution, outputting the center of gravity position in real time, including: The longitudinal slope angle of the vehicle is obtained, and the dynamic center of gravity is estimated by means of a hybrid extended Kalman filter or an extended Kalman filter algorithm based on the real-time mass distribution. The state equation includes a model based on the vehicle chassis configuration coefficient, center of gravity wheelbase and suspension compression compensation.

7. The vehicle adaptive control method according to any one of claims 1-4, characterized in that, Also includes: The vehicle's actual start-up response speed and braking distance are monitored using odometer and speed sensor data. The monitored results are compared with the expected target, and the parameters of the preset correlation model and / or the driving compensation coefficient and braking compensation coefficient corresponding to the slope range are updated in real time based on the comparison results.

8. A vehicle adaptive control device based on dynamic center of gravity, characterized in that, include: The mass distribution acquisition unit is used to acquire the vehicle load and calculate the real-time mass distribution of the vehicle based on the vehicle load and preset vehicle geometric parameters. The center of gravity acquisition unit is used to acquire the longitudinal slope angle of the vehicle, and perform dynamic center of gravity estimation based on the real-time mass distribution, and output the center of gravity position in real time. The power distribution unit is used to modify the driving force and braking force distribution strategy based on the longitudinal slope angle, the center of gravity position and the preset correlation model; A control unit for controlling the vehicle's drive actuators and brake actuators according to the modified drive and brake force distribution strategy.

9. A vehicle, characterized in that, The system includes a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor, when executing the computer program, implements the steps of the vehicle adaptive control method as described in any one of claims 1 to 7.

10. A computer program product, characterized in that, When the computer program is run, the vehicle adaptive control method as described in any one of claims 1 to 7 is executed.

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