1. A vehicle drive torque monitoring system, comprising: the system comprises a vehicle end and a cloud server, wherein the vehicle end comprises a sampling module, a torque calculation module and a drive control module;

the sampling module is used for acquiring target state data of a target vehicle and transmitting the target state data to the cloud server and the torque calculation module, wherein the target state data comprises a target vehicle model, target vehicle driving demand data and target vehicle driving state data;

the torque calculation module is used for determining the actual driving torque of the target vehicle according to the target vehicle driving demand data and the target vehicle driving state data;

the cloud server is used for acquiring historical state data of at least one group of vehicles with the same type as the target vehicle, and determining the driving torque range of the target vehicle according to the target state data and the historical state data, wherein the historical state data comprises historical driving demand data and historical driving state data;

and the drive control module is used for determining whether torque output is abnormal or not according to the actual drive torque and the drive torque range, and adjusting the output torque of the whole vehicle when the torque output is abnormal.

2. The vehicle drive torque monitoring system of claim 1, wherein the historical drive demand data includes historical accelerator pedal opening parameters and historical brake pressure parameters, and the historical drive status data includes one or more combinations of historical vehicle speed parameters, historical vehicle acceleration parameters, or historical grade parameters.

3. The vehicle drive torque monitoring system of claim 1, wherein the target vehicle driving demand data includes a target vehicle accelerator pedal opening parameter and a target vehicle brake pressure parameter, and the target vehicle driving status data includes one or more combinations of a target vehicle speed parameter, a target vehicle overall vehicle acceleration parameter, or a target vehicle grade parameter.

4. The vehicle driving torque monitoring system according to claim 3, wherein the torque calculation module is configured to determine a whole vehicle braking torque according to the target vehicle braking pressure parameter, determine a whole vehicle running road resistance according to the target vehicle speed parameter, determine a whole vehicle acceleration resistance according to the target vehicle whole vehicle acceleration parameter, and determine an actual driving torque of the target vehicle according to the whole vehicle braking torque, the whole vehicle running road resistance, and the whole vehicle acceleration resistance.

5. The vehicle driving torque monitoring system according to claim 1, wherein the cloud server is configured to determine real-time operating conditions of the vehicle of the same model as the target vehicle according to the historical driving demand data, and the real-time operating conditions include driving operating conditions and braking operating conditions.

6. The vehicle driving torque monitoring system according to claim 5, wherein the cloud server is configured to obtain at least one entire vehicle acceleration of a vehicle of the same type as the target vehicle within a preset vehicle speed range and a preset accelerator pedal opening range under a driving condition, determine a maximum value of the at least one entire vehicle acceleration as historical driving state data under the driving condition, obtain at least one entire vehicle acceleration of the vehicle of the same type as the target vehicle within the preset vehicle speed range and the preset accelerator pedal opening range under a braking condition, and determine a minimum value of the at least one entire vehicle acceleration as historical driving state data under the braking condition.

7. The vehicle driving torque monitoring system according to claim 1, wherein the cloud server stores a preset accelerator pedal opening upper threshold value and a preset maximum acceleration value, and is configured to determine the driving torque range of the target vehicle according to the preset maximum acceleration value when the target vehicle driving demand data exceeds the preset accelerator pedal opening upper threshold value.

8. The vehicle driving torque monitoring system according to claim 1, wherein the cloud server stores a preset upper brake pressure threshold and a preset minimum acceleration value, and is configured to determine the driving torque range of the target vehicle according to the preset minimum acceleration value when the target vehicle driving demand data exceeds the preset upper brake pressure threshold.

9. A vehicle drive torque monitoring method, comprising the steps of:

acquiring target state data of a target vehicle, and transmitting the target state data to a cloud server, wherein the target state data comprises a target vehicle model, target vehicle driving demand data and target vehicle driving state data;

determining the actual driving torque of the target vehicle according to the target vehicle driving demand data and the target vehicle driving state data;

acquiring historical state data of at least one group of vehicles with the same type as the target vehicle by using a cloud server, and determining a driving torque range of the target vehicle according to the target state data and the historical state data by using the cloud server, wherein the historical state data comprises historical driving demand data and historical driving state data;

and determining whether torque output is abnormal or not according to the actual driving torque and the driving torque range, and adjusting the output torque of the whole vehicle when the torque output is abnormal.

10. A vehicle characterized by comprising the vehicle drive torque monitoring system of any one of claims 1-8.

Background

With the development of new energy technology, pure electric vehicles and hybrid electric vehicles are widely popularized and used, and in the driving process, the pure electric vehicles adopt a power motor to provide driving torque and braking energy recovery torque; the hybrid electric vehicle adopts an engine and a power motor to provide driving torque, and simultaneously, the power motor provides braking energy recovery torque.

In the driving process, if a sensor, a controller, an actuator or a communication wire harness in a finished automobile driving control system fails, the finished automobile driving torque is abnormal, and according to the finished automobile safety development standard, a finished automobile driving torque monitoring system is designed according to a finished automobile power system framework, so that the finished automobile is prevented from being accelerated or decelerated accidentally.

The existing whole vehicle driving torque monitoring system usually estimates the actual driving torque of the whole vehicle according to a whole vehicle control signal or an acceleration signal, sets a whole vehicle driving torque allowable range according to the distance between the whole vehicle and the vehicle before and after test calibration, and limits torque output or cuts off power output when the actual driving torque of the whole vehicle exceeds the whole vehicle driving torque allowable range.

Disclosure of Invention

The invention provides a vehicle driving torque monitoring system, which solves the problem of inaccurate failure prediction of vehicle driving torque monitoring, improves the torque abnormity monitoring accuracy rate, and reduces accident risks.

In a first aspect, an embodiment of the present invention provides a vehicle driving torque monitoring system, including: the system comprises a vehicle end and a cloud server, wherein the vehicle end comprises a sampling module, a torque calculation module and a drive control module; the sampling module is used for acquiring target state data of a target vehicle and transmitting the target state data to the cloud server and the torque calculation module, wherein the target state data comprises a target vehicle model, target vehicle driving demand data and target vehicle driving state data; the torque calculation module is used for determining the actual driving torque of the target vehicle according to the target vehicle driving demand data and the target vehicle driving state data; the cloud server is used for acquiring historical state data of at least one group of vehicles with the same type as the target vehicle, and determining the driving torque range of the target vehicle according to the target state data and the historical state data, wherein the historical state data comprises historical driving demand data and historical driving state data; and the drive control module is used for determining whether torque output is abnormal or not according to the actual drive torque and the drive torque range, and adjusting the output torque of the whole vehicle when the torque output is abnormal.

Optionally, the historical driving demand data comprises historical accelerator pedal opening parameters and historical brake pressure parameters, and the historical driving state data comprises one or more of historical vehicle speed parameters, historical integral vehicle acceleration parameters or historical gradient parameters.

Optionally, the target vehicle driving demand data includes a target vehicle accelerator pedal opening degree parameter and a target vehicle brake pressure parameter, and the target vehicle driving state data includes one or more combinations of a target vehicle speed parameter, a target vehicle overall vehicle acceleration parameter or a target vehicle gradient parameter.

Optionally, the torque calculation module is configured to determine a whole vehicle braking torque according to the target vehicle braking pressure parameter, determine a whole vehicle running road resistance according to the target vehicle speed parameter, determine a whole vehicle acceleration resistance according to the target vehicle whole vehicle acceleration parameter, and determine an actual driving torque of the target vehicle according to the whole vehicle braking torque, the whole vehicle running road resistance, and the whole vehicle acceleration resistance.

Optionally, the cloud server is configured to determine a real-time working condition of the vehicle of the same model as the target vehicle according to the historical driving demand data, where the real-time working condition includes a driving working condition and a braking working condition.

Optionally, the cloud server is configured to obtain, under a driving condition, at least one entire vehicle acceleration of a vehicle with the same target vehicle model within a preset vehicle speed range and a preset accelerator pedal opening range, determine a maximum value of the at least one entire vehicle acceleration as historical driving state data under the driving condition, and obtain, under a braking condition, at least one entire vehicle acceleration of a vehicle with the same target vehicle model within a preset vehicle speed range and a preset accelerator pedal opening range, and determine a minimum value of the at least one entire vehicle acceleration as historical driving state data under the braking condition.

Optionally, the cloud server stores a preset accelerator pedal opening upper limit threshold and a preset maximum acceleration value, and is configured to determine a driving torque range of the target vehicle according to the preset maximum acceleration value when the target vehicle driving demand data exceeds the preset accelerator pedal opening upper limit threshold.

Optionally, the cloud server stores a preset brake pressure upper limit threshold and a preset minimum acceleration value, and is configured to determine a driving torque range of the target vehicle according to the preset minimum acceleration value when the target vehicle driving demand data exceeds the preset brake pressure upper limit threshold.

In a second aspect, an embodiment of the present invention further provides a vehicle driving torque monitoring method, including the following steps: acquiring target state data of a target vehicle, and transmitting the target state data to a cloud server, wherein the target state data comprises a target vehicle model, target vehicle driving demand data and target vehicle driving state data; determining the actual driving torque of the target vehicle according to the target vehicle driving demand data and the target vehicle driving state data; acquiring historical state data of at least one group of vehicles with the same type as the target vehicle by using a cloud server, and determining a driving torque range of the target vehicle according to the target state data and the historical state data by using the cloud server, wherein the historical state data comprises historical driving demand data and historical driving state data; and determining whether torque output is abnormal or not according to the actual driving torque and the driving torque range, and adjusting the output torque of the whole vehicle when the torque output is abnormal.

In a third aspect, the embodiment of the invention further provides a vehicle, which includes the vehicle driving torque monitoring system.

The vehicle provided by the embodiment of the invention is provided with a vehicle driving torque monitoring system, the system is provided with a vehicle end and a cloud server, target state data are obtained through a sampling module of the vehicle end, the target state data comprise a target vehicle model of the target vehicle, target vehicle driving demand data and target vehicle driving state data, the target vehicle model, the target vehicle driving demand data and the target vehicle driving state data are transmitted to the cloud server and a torque calculation module of the vehicle end, the actual driving torque of the target vehicle is calculated by using the torque calculation module according to the target vehicle driving demand data and the target vehicle driving state data, at least one group of historical state data of the vehicle with the same model as the target vehicle is obtained by using the cloud server, the historical state data comprise historical driving demand data and historical driving state data, the cloud server determines the driving torque range of the target vehicle according to the target state data and the historical state data, whether torque output is abnormal is determined by a drive control module at the vehicle end according to actual drive torque and a drive torque range, and output torque of the whole vehicle is adjusted when torque output is abnormal, so that the problem of inaccurate monitoring fault prediction of the drive torque of the whole vehicle is solved, whether accidental acceleration or accidental deceleration faults of the whole vehicle occur is judged by a big data statistical rule, the torque abnormality monitoring accuracy rate is improved, the accident risk is reduced, the driving safety performance is improved, and a data basis is provided for auxiliary driving.

Drawings

FIG. 1 is a schematic structural diagram of a vehicle driving torque monitoring system according to an embodiment of the present invention;

FIG. 2 is a schematic structural diagram of another vehicle drive torque monitoring system provided in accordance with an embodiment of the present invention;

FIG. 3 is a flow chart of a method for monitoring a driving torque of a vehicle according to a second embodiment of the present invention;

fig. 4 is a schematic structural diagram of a vehicle according to a third embodiment of the present invention.

Detailed Description

The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.

Example one

Fig. 1 is a schematic structural diagram of a vehicle driving torque monitoring system according to an embodiment of the present invention, which is applicable to a driving torque monitoring scenario of a pure electric vehicle or a hybrid electric vehicle.

As shown in fig. 1, the vehicle driving torque monitoring system 00 includes: the system comprises a vehicle end 1 and a cloud server 2, wherein the vehicle end 1 comprises a sampling module 10, a torque calculation module 20 and a drive control module 30, the sampling module 10 is used for acquiring target state data of a target vehicle and transmitting the target state data to the cloud server 2 and the torque calculation module 20, and the target state data comprises a target vehicle model, target vehicle driving demand data and target vehicle driving state data; the torque calculation module 20 is used for determining the actual driving torque of the target vehicle according to the target vehicle driving demand data and the target vehicle driving state data; the cloud server 2 is used for acquiring historical state data of at least one group of vehicles with the same type as the target vehicle, and determining the driving torque range of the target vehicle according to the target state data and the historical state data, wherein the historical state data comprises historical driving demand data and historical driving state data; and the driving control module 30 is used for determining whether torque output is abnormal according to the actual driving torque and the driving torque range, and adjusting the output torque of the whole vehicle when the torque output is abnormal.

In this embodiment, the target vehicle is a vehicle to be monitored, the model of the target vehicle may be a powertrain model of the vehicle to be monitored, and the vehicle of the same model as the target vehicle may be a vehicle of the same powertrain model as the vehicle to be monitored.

In this embodiment, the cloud server 2 is configured with a big data computing platform, and the big data computing platform prestores a data group list of vehicles corresponding to part of powertrain models, wherein the data group list comprises a group of historical driving demand data and a group of historical driving state data, and the group of historical driving demand data and the group of historical driving state data correspond to each other one by one; or in the driving torque monitoring process, the cloud server 2 can also acquire real vehicle driving demand data uploaded by vehicles with the same models as the target vehicles and real vehicle driving state data corresponding to the real vehicle driving demand data in real time through a wireless communication technology, add the real vehicle driving demand data and the real vehicle driving state data corresponding to the real vehicle driving demand data to historical driving demand data and historical driving state data, and the cloud server 2 performs statistical analysis on the historical driving demand data and the historical driving state data by using a big data computing platform to acquire the driving state and the driving torque range of the whole vehicle under different driving demands.

Specifically, after the target vehicle is started, the torque calculation module 20 calculates the actual driving torque of the target vehicle according to the target vehicle driving demand data and the target vehicle driving state data collected by the sampling module 10, meanwhile, a communication component of the vehicle end 1 is in communication connection with the cloud server 2, the model number of the target vehicle, the driving demand data of the target vehicle and the driving state data of the target vehicle, which are acquired by the sampling module 10, are transmitted to the cloud server 2 through a wireless communication technology, the cloud server 2 compares the driving demand data of the target vehicle and the driving state data of the target vehicle with a prestored data group list, determines a data group matched with the driving demand data of the target vehicle, determines a driving torque range corresponding to the data group as a driving torque range of the target vehicle, and transmits the driving torque range of the target vehicle to the driving control module 30 of the vehicle end of the target vehicle through a wireless communication technology.

After the driving control module 30 receives the actual driving torque sent by the torque calculation module 20 and the driving torque range sent by the cloud server 2, the driving control module 30 determines whether the actual driving torque at the current sampling time exceeds the driving torque range, and if the actual driving torque is greater than the upper limit threshold of the driving torque range, the driving control module 30 determines that the driving torque of the target vehicle at the current sampling time is too large, and controls the engine or the power motor to reduce the output torque; if the actual driving torque is lower than the lower limit threshold of the driving torque range, the driving control module 30 determines that the driving torque of the target vehicle is too small at the current sampling moment, and controls the engine or the power motor to increase the output torque; if the actual driving torque is within the driving torque range, the driving control module 30 judges that the driving torque of the target vehicle at the current sampling moment is normal, intervention on the driving torque of the whole vehicle is not needed, the problem of inaccurate fault prediction caused by the fact that the driving monitoring parameters of the whole vehicle cannot be updated in real time is solved, whether the accidental acceleration or accidental deceleration fault of the whole vehicle occurs is judged by utilizing a big data statistical rule, the torque abnormity monitoring accuracy rate is favorably improved, the accident risk is reduced, the driving safety performance is improved, and a data basis is provided for auxiliary driving.

Optionally, the target vehicle driving demand data includes a target vehicle accelerator pedal opening degree parameter and a target vehicle brake pressure parameter, and the target vehicle driving state data includes one or more combinations of a target vehicle speed parameter, a target vehicle overall vehicle acceleration parameter or a target vehicle gradient parameter.

Optionally, the target vehicle driving demand data and the target vehicle driving state data may be transmitted to the torque calculation module 20 and the driving control module 30 by using a data transmission cable, and the target vehicle driving demand data and the target vehicle driving state data are transmitted to the cloud server 2 by using a wireless communication technology, and of course, a person skilled in the art may also use other data transmission strategies to implement data interaction, which is not limited thereto.

Fig. 2 is a schematic structural diagram of another vehicle driving torque monitoring system according to an embodiment of the present invention.

Optionally, as shown in fig. 2, the sampling module 10 includes a driving demand data sampling unit 101 and a driving state data sampling unit 102, where the driving demand data sampling unit 101 includes but is not limited to: one or more combinations of an accelerator pedal opening sensor, a brake pedal force or brake master cylinder pressure sensor and a gear position sensor, and the driving state data sampling unit 102 includes, but is not limited to: one or more combinations of a vehicle speed or wheel speed sensor, an acceleration sensor, and a grade sensor.

The system comprises an accelerator pedal opening sensor, a brake pedal force sensor or a brake master cylinder pressure sensor, a vehicle speed or wheel speed sensor, an acceleration sensor and a gradient sensor, wherein the accelerator pedal opening sensor is used for collecting accelerator pedal opening parameters of the whole vehicle, the brake pedal force sensor or the brake master cylinder pressure sensor is used for obtaining brake pressure parameters of the whole vehicle, the vehicle speed or wheel speed sensor is used for obtaining driving vehicle speed parameters of the whole vehicle, the acceleration sensor is used for obtaining acceleration parameters of the whole vehicle, and the gradient sensor is used for obtaining gradient parameters of the whole vehicle.

Optionally, the torque calculation module 20 is configured to determine a whole vehicle braking torque according to the target vehicle braking pressure parameter, determine a whole vehicle running road resistance according to the target vehicle speed parameter, determine a whole vehicle acceleration resistance according to the target vehicle whole vehicle acceleration parameter, and determine an actual driving torque of the target vehicle according to the whole vehicle braking torque, the whole vehicle running road resistance, and the whole vehicle acceleration resistance.

In this embodiment, the braking torque of the whole vehicle is defined as F_BreakThe resistance of the whole running road is F_rThe acceleration resistance of the whole vehicle is F_rThe actual driving torque of the target vehicle is F_DriveBraking torque F of the entire vehicle_BreakAnd the resistance F of the whole vehicle running road_rAnd the acceleration resistance F of the whole vehicle_aAnd actual driving torque F of the target vehicle_DriveSatisfies the formula one as shown below:

F_Drive＝F_Break+F_r+ Fa (formula one)

Combining the formula I, the actual driving torque F of the target vehicle_DriveEqual to the braking torque F of the whole vehicle_BreakAnd the resistance F of the whole vehicle running road_rAnd the acceleration resistance F of the whole vehicle_aAnd (4) summing.

Optionally, defining the target vehicle brake pressure parameter as P_BreakThe target vehicle brake pressure parameter P can be determined_BreakSubstituting the formula II shown below to calculate the braking torque F of the whole vehicle_Break：

F_Break＝a′+b′*P_Break(formula two)

Wherein a 'and b' are brake torque calibration coefficients, and specific values of a 'and b' can be obtained by calibration, which is not limited.

Optionally, the vehicle speed parameter of the target vehicle is defined as v, the vehicle speed parameter v of the target vehicle can be substituted into a formula three shown below, and the resistance F of the whole vehicle running road is calculated_r：

F_r＝a+b*v+c*v²(formula three)

The a, b and c are calibration coefficients of the resistance of the whole vehicle running road, and the specific numerical values of the a, b and c can be obtained through calibration, which is not limited.

Optionally, the overall vehicle acceleration parameter of the target vehicle is defined as A_XThe whole vehicle acceleration parameter A of the target vehicle can be obtained_XSubstituting the formula IV shown below to calculate the acceleration resistance F of the whole vehicle_a：

F_a＝M*A_x(formula four)

Wherein, M is the whole vehicle preparation mass, and the specific numerical value of M can be obtained by calibration, which is not limited.

In conjunction with equations one through four, the torque calculation module 20 may receive the target vehicle brake pressure parameter P_BreakTarget vehicle speed parameter v and target vehicle whole vehicle acceleration parameter A_XSubstituting into corresponding formula, and regulating braking torque F_BreakAnd the resistance F of the whole vehicle running road_rAnd the acceleration resistance F of the whole vehicle_aSumming up and calculating actual driving torque F of the target vehicle at the current sampling time_DriveThe method is simple, the real-time performance of data is high, and the accuracy of the actual driving torque is improved.

Optionally, the historical driving demand data comprises historical accelerator pedal opening parameters and historical brake pressure parameters, and the historical driving state data comprises one or more combinations of historical vehicle speed parameters, historical integral vehicle acceleration parameters or historical gradient parameters.

In this embodiment, each historical state data set of the historical driving demand data includes a group of historical driving demand data and a group of historical driving state data, historical vehicle acceleration parameters corresponding to different historical driving demand data and different historical vehicle speed parameters under different working conditions are respectively counted, and a data set list under driving working conditions and braking working conditions is established according to the counted data and is used for subsequent table lookup comparison.

Optionally, the cloud server 2 is configured to determine a real-time working condition of the vehicle of the same model as the target vehicle according to the historical driving demand data, where the real-time working condition includes a driving working condition and a braking working condition.

Specifically, if the cloud server 2 determines that the received historical accelerator pedal opening parameter is greater than zero, the cloud server 2 determines that the current vehicle is in a driving condition; if the cloud server 2 judges that the received historical brake pressure parameter is larger than zero, the cloud server 2 determines that the current vehicle is in a brake working condition; if the cloud server 2 judges that the received historical brake pressure parameter and the received historical accelerator pedal opening parameter are both greater than zero, the cloud server 2 determines that the current data are wrong, and ignores the current data.

Optionally, the cloud server 2 is configured to obtain at least one entire vehicle acceleration of a vehicle of the same type as the target vehicle in a preset vehicle speed range and a preset accelerator pedal opening range under the driving condition, determine a maximum value of the at least one entire vehicle acceleration as historical driving state data under the driving condition, obtain at least one entire vehicle acceleration of the vehicle of the same type as the target vehicle in the preset vehicle speed range and the preset accelerator pedal opening range under the braking condition, and determine a minimum value of the at least one entire vehicle acceleration as historical driving state data under the braking condition.

Specifically, under the driving condition, historical driving demand data can be set as historical accelerator pedal opening parameters, different historical vehicle speed parameters and maximum acceleration values within different historical accelerator pedal opening parameter ranges are determined as historical whole vehicle acceleration parameters corresponding to the group of data, and a pre-stored data group list under the driving condition is established and stored in the cloud server 2; under the braking condition, historical driving demand data can be set as historical braking pressure parameters, the maximum acceleration values within different historical vehicle speed parameters and different historical braking pressure parameter ranges are determined as historical whole vehicle acceleration parameters corresponding to the group of data, a prestored data group list under the braking condition is established, and the prestored data group list is stored in the cloud server 2.

Optionally, the cloud server 2 stores a preset accelerator pedal opening upper limit threshold and a preset maximum acceleration value, and the cloud server 2 is configured to determine a driving torque range of the target vehicle according to the preset maximum acceleration value when the driving demand data of the target vehicle exceeds the preset accelerator pedal opening upper limit threshold.

Optionally, the cloud server 2 may substitute the driving demand data of the target vehicle and the preset maximum acceleration value into the first to fourth formulas to calculate the driving torque range of the target vehicle, or the cloud server 2 may establish a corresponding relationship list between the vehicle acceleration and the driving torque range of the entire vehicle through a calibration test, and determine the driving torque range of the target vehicle through a table lookup method, which is not limited to this.

Specifically, if the accelerator pedal opening parameter of the target vehicle exceeds the preset accelerator pedal opening upper limit threshold, the cloud server 2 determines that the driver of the target vehicle has an obvious acceleration requirement at the current sampling moment, determines the preset maximum acceleration value in the prestored data group list as the currently required acceleration of the target vehicle, and determines the driving torque range of the target vehicle according to the preset maximum acceleration value and the current vehicle speed of the target vehicle.

For example, the preset accelerator pedal opening upper limit threshold may be set to be equal to 50%, and the preset maximum acceleration value may be a historical entire vehicle acceleration parameter with a maximum value within the current vehicle speed range of the target vehicle, that is, if the accelerator pedal opening parameter of the target vehicle exceeds the preset accelerator pedal opening upper limit threshold by 50%, the cloud server 2 determines that the driver of the target vehicle has an obvious acceleration demand at the current sampling time, and determines the historical entire vehicle acceleration parameter with the maximum value at the current vehicle speed of the target vehicle as the current required acceleration of the target vehicle.

Optionally, the cloud server 2 stores a preset brake pressure upper limit threshold and a preset minimum acceleration value, and the cloud server 2 is configured to determine a driving torque range of the target vehicle according to the preset minimum acceleration value when the driving demand data of the target vehicle exceeds the preset brake pressure upper limit threshold.

Specifically, if the brake pressure parameter of the target vehicle exceeds the preset brake pressure upper limit threshold, the cloud server 2 determines that the driver of the target vehicle has an obvious brake demand at the current sampling moment, determines the preset minimum acceleration value in the prestored data group list as the currently required acceleration of the target vehicle, and determines the driving torque range of the target vehicle according to the preset minimum acceleration value and the current vehicle speed of the target vehicle.

For example, the preset upper brake pressure threshold may be set to be equal to 50%, and the preset minimum acceleration value may be a historical entire vehicle acceleration parameter with a minimum value within the current vehicle speed range of the target vehicle, that is, if the target vehicle brake pressure parameter exceeds the preset upper brake pressure threshold by 50%, the cloud server 2 determines that the driver of the target vehicle has an obvious deceleration requirement at the current sampling time, and determines the historical entire vehicle acceleration parameter with the minimum value within the current vehicle speed range of the target vehicle as the current required acceleration of the target vehicle.

Illustratively, the historical vehicle speed parameter, the historical accelerator pedal opening parameter and the historical brake pressure parameter are segmented by combining with a preset accelerator pedal opening upper limit threshold and a preset brake pressure upper limit threshold, and a prestored data group list as shown in table 1 and table 2 is established, wherein table 1 is a prestored data group list stored by the cloud server under a driving condition, and table 2 is a prestored data group list stored by the cloud server under a brake condition.

In combination with table 1, under the driving condition, the target vehicle brake pressure parameter is equal to 0, the cloud server 2 may compare the received target vehicle accelerator pedal opening parameter and target vehicle speed parameter at the current sampling time with the pre-stored data group list in table 1, determine the acceleration value required by the target vehicle at the current sampling time according to the comparison result, and determine the driving torque range required by the current target vehicle according to the acceleration value. For example, if the speed parameter of the target vehicle is 90km/h and the accelerator opening parameter of the target vehicle is 8% of the maximum accelerator opening, the acceleration value required by the target vehicle at the previous sampling time can be determined to be A through table look-up comparison₉₂(ii) a If the speed parameter of the target vehicle is 102km/h and the opening parameter of the accelerator pedal of the target vehicle is 20% of the maximum opening of the accelerator pedal, the position of the target vehicle at the previous sampling moment can be determined through table look-up comparisonThe required acceleration value is A_B3(ii) a If the speed parameter of the target vehicle is 100km/h and the opening parameter of the accelerator pedal of the target vehicle is more than 50%, the acceleration value required by the target vehicle at the previous sampling moment can be determined to be A through table look-up comparison_A7。

In combination with table 2, under the braking condition, the opening parameter of the accelerator pedal of the target vehicle is equal to 0, the cloud server 2 can compare the received brake pressure parameter and the vehicle speed parameter of the target vehicle at the current sampling time with the pre-stored data group list in table 1, determine the acceleration value required by the target vehicle at the current sampling time according to the comparison result, and determine the driving torque range required by the current target vehicle according to the acceleration value. For example, if the speed parameter of the target vehicle is 20km/h and the brake pressure parameter of the target vehicle is 10bar, the acceleration value required by the target vehicle at the previous sampling moment can be determined to be A through table look-up comparison₂₂' of a compound of formula I; if the speed parameter of the target vehicle is 20km/h and the brake pressure parameter of the target vehicle is more than 50bar, the acceleration value required by the target vehicle at the previous sampling moment can be determined to be A through table look-up comparison₂₇＇。

Therefore, the driving demand data of the target vehicle and the driving state data of the target vehicle are uploaded to the cloud server, the cloud server carries out statistical analysis, a driving torque range of the whole vehicle consistent with the driving capacity of a power system of the target vehicle is calculated and transmitted to the vehicle end, the vehicle end controller is used for judging whether the target vehicle has unexpected acceleration and unexpected deceleration faults or not, and carrying out torque intervention on the target vehicle, torque limitation or power output cut-off is carried out, real-time fault judgment is carried out on the driving monitoring parameters of the whole vehicle in the driving process of the vehicle through big data statistics, the torque monitoring faults caused by random faults or system faults of an electric control system of the whole vehicle are avoided, the abnormal torque monitoring accuracy is favorably improved, the accident risk is reduced, the driving safety performance is improved, and data basis is provided for auxiliary driving.

Example two

The second embodiment of the invention provides a vehicle driving torque monitoring method, which can be executed by the vehicle driving torque monitoring system and has the same beneficial effects as the vehicle driving torque monitoring system.

Fig. 3 is a flowchart of a vehicle driving torque monitoring method according to a second embodiment of the present invention.

As shown in fig. 3, the vehicle driving torque monitoring method includes the steps of:

step S1: the method comprises the steps of obtaining target state data of a target vehicle, and transmitting the target state data to a cloud server, wherein the target state data comprise the model of the target vehicle, the driving demand data of the target vehicle and the driving state data of the target vehicle.

Step S2: and determining the actual driving torque of the target vehicle according to the target vehicle driving demand data and the target vehicle driving state data.

Step S3: the method comprises the steps of acquiring historical state data of at least one group of vehicles with the same type as a target vehicle by using a cloud server, and determining a driving torque range of the target vehicle according to the target state data and the historical state data by using the cloud server, wherein the historical state data comprise historical driving demand data and historical driving state data.

Step S4: and determining whether torque output is abnormal or not according to the actual driving torque and the driving torque range, and adjusting the output torque of the whole vehicle when the torque output is abnormal.

Optionally, the historical driving demand data comprises historical accelerator pedal opening parameters and historical brake pressure parameters, and the historical driving state data comprises one or more combinations of historical vehicle speed parameters, historical integral vehicle acceleration parameters or historical gradient parameters.

Optionally, the target vehicle driving demand data includes a target vehicle accelerator pedal opening degree parameter and a target vehicle brake pressure parameter, and the target vehicle driving state data includes one or more combinations of a target vehicle speed parameter, a target vehicle overall vehicle acceleration parameter or a target vehicle gradient parameter.

Optionally, the vehicle driving torque monitoring method further comprises the steps of: determining the braking torque of the whole vehicle according to the target vehicle braking pressure parameter; determining the resistance of the whole vehicle running road according to the speed parameter of the target vehicle; determining the whole vehicle acceleration resistance according to the whole vehicle acceleration parameter of the target vehicle; and determining the actual driving torque of the target vehicle according to the braking torque of the whole vehicle, the resistance of the running road of the whole vehicle and the acceleration resistance of the whole vehicle.

Optionally, the vehicle driving torque monitoring method further comprises the steps of: and determining the real-time working condition of the vehicle with the same model as the target vehicle according to the historical driving demand data, wherein the real-time working condition comprises a driving working condition and a braking working condition.

Optionally, the vehicle driving torque monitoring method further comprises the steps of: the method comprises the steps of obtaining at least one whole vehicle acceleration of a vehicle with the same model as a target vehicle within a preset vehicle speed range and a preset accelerator pedal opening range under a driving condition, determining the maximum value of the at least one whole vehicle acceleration as historical driving state data under the driving condition, obtaining at least one whole vehicle acceleration of the vehicle with the same model as the target vehicle within the preset vehicle speed range and the preset accelerator pedal opening range under a braking condition, and determining the minimum value of the at least one whole vehicle acceleration as historical driving state data under the braking condition.

Optionally, the vehicle driving torque monitoring method further comprises the steps of: and acquiring a preset accelerator pedal opening upper limit threshold value and a preset maximum acceleration value, and determining the driving torque range of the target vehicle according to the preset maximum acceleration value when the driving demand data of the target vehicle exceeds the preset accelerator pedal opening upper limit threshold value.

Optionally, the vehicle driving torque monitoring method further comprises the steps of: and acquiring a preset brake pressure upper limit threshold value and a preset minimum acceleration value, and determining the driving torque range of the target vehicle according to the preset minimum acceleration value when the driving demand data of the target vehicle exceeds the preset accelerator pedal opening upper limit threshold value.

The vehicle driving torque monitoring method provided by the embodiment of the invention comprises the steps of acquiring target state data through a sampling module of a vehicle end, wherein the target state data comprises a target vehicle model of a target vehicle, target vehicle driving demand data and target vehicle driving state data, transmitting the target vehicle model, the target vehicle driving demand data and the target vehicle driving state data to a cloud server and a torque calculation module of the vehicle end, calculating actual driving torque of the target vehicle according to the target vehicle driving demand data and the target vehicle driving state data by using the torque calculation module, acquiring historical state data of at least one group of vehicles with the same model as the target vehicle by using the cloud server, wherein the historical state data comprises historical driving demand data and historical driving state data, and determining the driving torque range of the target vehicle by using the cloud server according to the target state data and the historical state data, whether torque output is abnormal is determined by a drive control module at the vehicle end according to actual drive torque and a drive torque range, and output torque of the whole vehicle is adjusted when torque output is abnormal, so that the problem of inaccurate monitoring fault prediction of the drive torque of the whole vehicle is solved, whether accidental acceleration or accidental deceleration faults of the whole vehicle occur is judged by a big data statistical rule, the torque abnormality monitoring accuracy rate is improved, the accident risk is reduced, the driving safety performance is improved, and a data basis is provided for auxiliary driving.

EXAMPLE III

Fig. 4 is a schematic structural diagram of a vehicle according to a third embodiment of the present invention.

As shown in fig. 4, the vehicle 100 includes the vehicle driving torque monitoring system 00 described above.

In this embodiment, the vehicle 100 may be a pure electric vehicle or a hybrid electric vehicle.

The vehicle provided by the embodiment of the invention is provided with a vehicle driving torque monitoring system, the system is provided with a vehicle end and a cloud server, target state data are obtained through a sampling module of the vehicle end, the target state data comprise a target vehicle model of the target vehicle, target vehicle driving demand data and target vehicle driving state data, the target vehicle model, the target vehicle driving demand data and the target vehicle driving state data are transmitted to the cloud server and a torque calculation module of the vehicle end, the actual driving torque of the target vehicle is calculated by using the torque calculation module according to the target vehicle driving demand data and the target vehicle driving state data, at least one group of historical state data of the vehicle with the same model as the target vehicle is obtained by using the cloud server, the historical state data comprise historical driving demand data and historical driving state data, the cloud server determines the driving torque range of the target vehicle according to the target state data and the historical state data, whether torque output is abnormal is determined by a drive control module at the vehicle end according to actual drive torque and a drive torque range, and output torque of the whole vehicle is adjusted when torque output is abnormal, so that the problem of inaccurate monitoring fault prediction of the drive torque of the whole vehicle is solved, whether accidental acceleration or accidental deceleration faults of the whole vehicle occur is judged by a big data statistical rule, the torque abnormality monitoring accuracy rate is improved, the accident risk is reduced, the driving safety performance is improved, and a data basis is provided for auxiliary driving.

It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.