CN114987522B - Man-machine interaction method for automatically adjusting vehicle power based on vehicle acceleration - Google Patents
Man-machine interaction method for automatically adjusting vehicle power based on vehicle acceleration Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W50/08—Interaction between the driver and the control system
- B60W50/082—Selecting or switching between different modes of propelling
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/08—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to drivers or passengers
- B60W40/09—Driving style or behaviour
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W50/08—Interaction between the driver and the control system
- B60W50/14—Means for informing the driver, warning the driver or prompting a driver intervention
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W2050/0001—Details of the control system
- B60W2050/0002—Automatic control, details of type of controller or control system architecture
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W2050/0062—Adapting control system settings
- B60W2050/0075—Automatic parameter input, automatic initialising or calibrating means
- B60W2050/0095—Automatic control mode change
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W50/08—Interaction between the driver and the control system
- B60W50/14—Means for informing the driver, warning the driver or prompting a driver intervention
- B60W2050/146—Display means
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2520/00—Input parameters relating to overall vehicle dynamics
- B60W2520/10—Longitudinal speed
- B60W2520/105—Longitudinal acceleration
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2556/00—Input parameters relating to data
- B60W2556/10—Historical data
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/72—Electric energy management in electromobility
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/80—Technologies aiming to reduce greenhouse gasses emissions common to all road transportation technologies
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Abstract
The invention provides a man-machine interaction method for automatically adjusting vehicle power based on vehicle acceleration, which is characterized in that acceleration data of different speed zones of a user are collected, acceleration of different speeds is utilized to represent driving style of the user, the driving style of the user is identified according to comparison between the average value and standard deviation of the acceleration of the different speed zones and a reference value of a sample driver, vehicle power output is adjusted, and an adjustment result is displayed on a vehicle-mounted display screen in real time, so that the function of stepless adjusting the vehicle power is realized. According to the scheme for self-learning of the driving style, which is provided by the invention, through learning the driving behavior of the user, the correction system for obtaining the pedal MAP according to the driving habit of the user automatically adjusts the torque output, the torque response is more intelligent, and the requirements of different clients on the power response are met.
Description
Technical Field
The invention belongs to the technical field of automobile control, and particularly relates to a man-machine interaction method for automatically adjusting vehicle power based on vehicle acceleration.
Background
And (3) ECU: engine Control Unit engine electronic control unit
MAP: map, herein referred to as accelerator pedal to torque map
HMI: human MACHINE INTERFACE man-machine interaction system
The driving modes of the existing vehicles in the market at present are generally divided into a sport mode, a comfort mode, an economic mode and the like, so that different requirements of different drivers on the power of the vehicles are met. The driver manually selects the driving mode through buttons on the vehicle to realize different power outputs. For example, the driver prefers to drive violently to select a sport mode, so that the throttle response is more sensitive, and the vehicle is more power-efficient; the driver prefers fuel-saving driving to select an economic mode, the throttle response is slow, the vehicle power is weak, and the gear box is shifted earlier so that the engine is kept at a lower rotating speed.
However, the vehicle has only 3 driving modes, the power selection is less, and the driving styles of all users cannot be completely covered; and the user is required to manually set the driving mode without a function of automatically recognizing the driving style of the driver. The power output in three levels or three steps cannot fully meet the power demands of all users. How to perform stepless power adjustment to cover the driving styles of all users, and intelligently adjust the power according to the driving styles of the users, without manual selection of drivers, still lacks a reasonable and effective scheme.
Disclosure of Invention
The invention aims to solve the technical problems that: a man-machine interaction method for automatically adjusting vehicle power based on vehicle acceleration is provided, and is used for stepless adjustment of vehicle power.
The technical scheme adopted by the invention for solving the technical problems is as follows: a man-machine interaction method for automatically adjusting vehicle power based on vehicle acceleration comprises the following steps:
S1: collecting corresponding relation data of the speed and the acceleration of a sample driver control vehicle under different road conditions to form a speed zone, and calculating the average value and standard deviation of the acceleration of different speed zones as reference values for comparison of a subsequent algorithm;
S2: after a user activates intelligent power on a vehicle-mounted display screen, collecting acceleration of the user in each speed zone; classifying the acceleration of each speed zone into an acceleration array;
s3: collecting acceleration data of a current user in real time, and calculating an acceleration mean value and an acceleration variance by taking N collected acceleration data as trigger conditions in each speed zone: calculating an acceleration mean value and an acceleration standard deviation by using an acceleration array of N acceleration acquired first; meanwhile, the first i acceleration data of each speed zone are cleared, and then the data are collected again;
S4: comparing the acceleration mean value and the acceleration standard deviation obtained in the step S3 with the acceleration mean value and the acceleration standard deviation of the sample driver in the corresponding speed zone obtained in the step S1 respectively to obtain a driving style correction coefficient mu k;
S5: and multiplying the driving style correction coefficient mu k by the abscissa system of the accelerator pedal MAP, and changing the corresponding relation between the torque of the accelerator pedal MAP and the pedal opening, so as to obtain different power outputs according to the driving style of a user.
According to the above scheme, in the step S1, the speed zone comprises 0-20 km/h, 20-40 km/h, 40-60 km/h, 60-80 km/h, 80-120 km/h, 120-140 km/h, and more than 140 km/h.
According to the above scheme, in the step S1, the specific steps are as follows:
Setting n acceleration data for each speed zone; k is the sequence number of the speed zone, and the value range is 0-6; a kn is the nth acceleration data of the kth speed zone, then the average of the acceleration of the sample driver in the kth speed zone The method comprises the following steps:
The standard deviation delta k of the acceleration of the sample driver in the kth speed zone is:
Further, in the step S3, the specific steps are as follows:
S31: if N acceleration data are just acquired in a certain speed zone, calculating an acceleration mean value and an acceleration standard deviation according to the acceleration of the speed zone, and outputting the acceleration mean value and the acceleration standard deviation for subsequent operation;
s32: and meanwhile, the acceleration data of each speed zone is cleared, the counting is restarted after the first i are cleared, and if the acceleration data acquired by a certain speed zone is less than i, the acquisition is restarted after the clearing.
Further, in the step S4, the specific steps are as follows:
Respectively comparing the acceleration mean value and the acceleration standard deviation of the current user with the acceleration mean value and the acceleration standard deviation of a sample driver correspondingly; setting alpha and beta as weight coefficients for comparing the acceleration mean value and the acceleration standard deviation respectively, wherein alpha+beta=1, wherein alpha occupies a larger weight, and beta occupies a smaller weight; the acceleration correction coefficient mu k for each speed band is:
further, in the step S5, the specific steps are as follows:
s51: multiplying the torque correction coefficient mu k by the accelerator opening of a reference accelerator pedal MAP in the abscissa system to obtain a new mapping table of torque and accelerator pedal opening;
S52: after the same pedal opening is stepped on by different users under different road conditions, different power outputs obtained by driving styles corresponding to the mapping table learned in the step S51 are obtained.
According to the scheme, the method further comprises the following steps:
Displaying a result of intelligent adjustment of the vehicle power on a vehicle-mounted display screen in a progress bar mode; the progress bar defaults to an intermediate position, indicating unadjusted vehicle dynamics; the progress bar is adjusted to the left end to show that the power output of the vehicle is economical; the progress bar is adjusted to the right end to represent the vehicle power output offset motion.
Further, the method also comprises the following steps:
The real-time expression of the intelligent vehicle power regulation result is transmitted through CAN messages of the ECU and the vehicle-mounted system; the CAN message format is as follows:
Dividing a progress bar into 8 sub-gears;
if the power coefficient output by the ECU control strategy is between 0.5 and 0.75, the regulated vehicle power is the weakest, and the progress bar is at the leftmost side;
If the power coefficient output by the ECU control strategy is more than 1.8, the regulated vehicle power is the strongest, and the progress bar is at the rightmost side.
A computer storage medium having stored therein a computer program executable by a computer processor for performing a human-machine interaction method for automatically adjusting vehicle dynamics based on vehicle acceleration.
The beneficial effects of the invention are as follows:
1. according to the man-machine interaction method for automatically adjusting the vehicle power based on the vehicle acceleration, acceleration data of different speed zones of a user are collected, the acceleration of the user at different speeds is used for representing the driving style of the user, the driving style of the user is identified according to comparison between the average value and standard deviation of the acceleration of the different speed zones and the standard value of a sample driver, the vehicle power output is adjusted, and the adjusted result is displayed on a vehicle-mounted display screen in real time, so that the function of stepless adjusting the vehicle power is realized.
2. Aiming at the problems that the selectivity of the driving mode of the vehicle is low, the vehicle cannot adapt to the requirements of each user level on the power response of the vehicle, and the vehicle can only manually select the driving mode, the invention innovatively provides a driving style self-learning scheme, and by learning the driving behaviors of the user, the torque output is automatically regulated by a correction system for obtaining the pedal MAP according to the driving habits of the user, the torque response is more intelligent, and the requirements of different clients on the power response are met.
Drawings
FIG. 1 is a flow chart of an embodiment of the present invention.
FIG. 2 is a diagram of an intelligent power adjustment real-time display of an embodiment of the present invention.
FIG. 3 is a raw accelerator pedal MAP diagram of an embodiment of the invention.
Fig. 4 is a corrected accelerator pedal MAP according to the embodiment of the invention.
Detailed Description
The invention will be described in further detail with reference to the drawings and the detailed description.
Referring to fig. 1, the ECU control method for intelligently adjusting vehicle power according to an embodiment of the present invention includes the steps of:
S1: acquiring corresponding relation data of vehicle speeds and accelerations of a plurality of sample drivers under different road conditions in advance, and obtaining acceleration mean values and acceleration standard deviations of different speed zones (7 speed zones are divided into 0-20 km/h, 20 km/h-40 km/h, 40 km/h-60 km/h, 60 km/h-80 km/h, 80 km/h-120 km/h, 120 km/h-140 km/h and more than 140 km/h) as reference values for comparison of subsequent algorithms;
s2: after the intelligent power is activated on the vehicle-mounted display screen by the user, the acceleration speed of the user under each vehicle speed zone is collected, 7 speed zone areas exist, and the acceleration data of each speed zone area are classified into one group;
S3: according to the acceleration data acquired in the step S2, acquiring an acceleration average value and an acceleration standard deviation by using an acceleration array of a speed zone where N acceleration data are acquired firstly, and simultaneously, restarting acquisition after the acceleration data of 7 speed zones are cleared out;
s4: comparing the user acceleration mean value and the standard deviation obtained in the step S3 with the acceleration mean value and the standard version of a sample driver corresponding to the speed zone region respectively to obtain a driving style correction coefficient mu k;
S5: multiplying the driving style correction coefficient obtained in the step S4 by the abscissa system of the accelerator pedal MAP to change the corresponding relation between the torque of the accelerator pedal MAP and the pedal opening, thereby obtaining different power outputs according to the driving style of a user;
in the step S1, the average value of the acceleration of each speed zone of the sample driver is calculated by using the formula Solving acceleration standard deviation utilization formula of each speed zone of sample driverWherein the value range of K is 0-6, which represents the average value and standard deviation of 7 speed zones.
In the step S2, the collected acceleration data are grouped in different speed bands, and 7 speed bands correspond to 7 groups of acceleration data;
in the step S3, acceleration data of the current user are collected in real time, each speed zone takes collected N acceleration data as a trigger condition to calculate average acceleration values and variances, and for 7 speed zones, if N acceleration data are just collected in a certain speed zone, average acceleration data and standard deviation of the speed zone are calculated according to the acceleration data of the speed zone, and the speed zone is output to perform subsequent operation. And meanwhile, the acceleration data of 7 speed bands are cleared, the counting is restarted after the first i speed bands are cleared, and if the acceleration data acquired by a certain speed band is less than i speed bands, the acquisition is started after the clearing. For example, if N acceleration data (a 1, a2, a3 … ai … an) are collected for a speed zone of (20 km/h,40 km/h), the mean and standard deviation are calculated therefrom, while the first i acceleration data are cleared, i.e. re-collected from (a i+1,ai+2,...an.), as are other speed zones; if the data collected in the interval of the speed zone (40 km/h,60 km/h) is only i-2, namely (a 1,a2,...ai-2), the collection is restarted after the acceleration data of the speed zone is cleared 0, the thinking of the algorithm is that the user firstly reaches N numbers, the calculation is carried out by using the acceleration data of the user, and the collection is restarted after the first i acceleration data of each speed zone are cleared. The time interval for acquisition is every 3s (or M seconds, M being nominally modifiable).
In step S3, the formula is also usedAndObtaining the average value of the acceleration of the current userAnd standard deviation lambda k;
In the step S4, a formula is used Mean value of acceleration for current userAnd the standard deviation lambda k is compared with the average acceleration value and the standard deviation of the sample driver, wherein alpha and beta respectively represent weight coefficients for comparing the average acceleration value and the standard deviation of the acceleration, alpha+beta=1, wherein alpha occupies a larger weight (the available value is 0.8), and beta occupies a smaller weight (the available value is 0.2), and the acceleration correction coefficient mu k of each speed zone is obtained after comparison.
In the step S5, the obtained torque correction coefficient μ k is multiplied by the abscissa accelerator opening of the reference accelerator pedal MAP to obtain a new torque and accelerator pedal opening mapping table, so that different users and different road conditions can obtain different power outputs based on the driving style learned before after stepping on the same pedal opening. Fig. 4 below.
According to the result of the intelligent adjustment of the vehicle power, the result is displayed on a vehicle-mounted display screen, such as a progress bar below the display screen of fig. 2, the progress bar defaults to be at a middle position, the vehicle power is not adjusted at the moment, such as the automatic adjustment of the progress bar to the left end, the vehicle power output is economical at the moment, and otherwise, the vehicle power output is biased.
The real-time expression of the intelligent adjustment of the vehicle power is transmitted through CAN messages of the ECU and the vehicle-mounted system, for example, the defined CAN message format is as follows:
and respectively 8 sub-gears of the progress bar, wherein if the power coefficient output by the ECU control strategy is between 0.5 and 0.75, the adjusted vehicle power is the weakest, and the progress bar is leftmost. If the power coefficient is above 1.8, the regulated vehicle power is strongest, and the progress bar is at the rightmost side.
The original accelerator pedal MAP is shown in fig. 3; the corrected accelerator pedal MAP is shown in fig. 4.
The key points of the algorithm in the invention are as follows:
1) The driving style of the user is constantly learned because the acceleration acquisition is never stopped in real time after the function is activated. The acceleration of the user is divided into different speed acquisition intervals, and the acceleration mean value and the standard deviation are obtained by taking the acceleration data acquisition of which speed interval reaches a set value as a trigger condition. The user can reach the collection number in the sampling period after long running time in the speed interval, and the calculation is reasonable according to the acceleration data of the speed interval, so that the latest driving style of the user can be represented.
2) The acceleration correction coefficient provides that the average acceleration value and the standard deviation can reflect the driving style of the user at the same time and have different weights. The average acceleration value represents the acceleration of a certain speed zone of a user, the weight is larger, the standard deviation reflects the consistency or the discreteness of the acceleration of the user stepping on the speed zone, and the weight is smaller.
3) And the man-machine interaction system displays the intelligently adjusted vehicle power in real time at the vehicle-machine end through the defined CAN message, and presents visual power adjustment for a user.
The above embodiments are merely for illustrating the design concept and features of the present invention, and are intended to enable those skilled in the art to understand the content of the present invention and implement the same, the scope of the present invention is not limited to the above embodiments. Therefore, all equivalent changes or modifications according to the principles and design ideas of the present invention are within the scope of the present invention.
Claims (9)
1. A man-machine interaction method for automatically adjusting vehicle power based on vehicle acceleration is characterized in that: the method comprises the following steps:
S1: collecting corresponding relation data of the speed and the acceleration of a sample driver control vehicle under different road conditions to form a speed zone, and calculating the average value and standard deviation of the acceleration of different speed zones as reference values for comparison of a subsequent algorithm;
S2: after a user activates intelligent power on a vehicle-mounted display screen, collecting acceleration of the user in each speed zone; classifying the acceleration of each speed zone into an acceleration array;
s3: collecting acceleration data of a current user in real time, and calculating an acceleration mean value and an acceleration variance by taking N collected acceleration data as trigger conditions in each speed zone: calculating an acceleration mean value and an acceleration standard deviation by using an acceleration array of N acceleration acquired first; meanwhile, the first i acceleration data of each speed zone are cleared, and then the data are collected again;
S4: comparing the acceleration mean value and the acceleration standard deviation obtained in the step S3 with the acceleration mean value and the acceleration standard deviation of the sample driver in the corresponding speed zone obtained in the step S1 respectively to obtain a driving style correction coefficient mu k;
S5: and multiplying the driving style correction coefficient mu k by the abscissa system of the accelerator pedal MAP, and changing the corresponding relation between the torque of the accelerator pedal MAP and the pedal opening, so as to obtain different power outputs according to the driving style of a user.
2. The human-computer interaction method for automatically adjusting vehicle dynamics based on vehicle acceleration according to claim 1, wherein: in the step S1, the speed zone comprises 0-20 km/h, 20-40 km/h, 40-60 km/h, 60-80 km/h, 80-120 km/h, 120-140 km/h and more than 140 km/h.
3. The human-computer interaction method for automatically adjusting vehicle dynamics based on vehicle acceleration according to claim 1, wherein: in the step S1, the specific steps are as follows:
Setting n acceleration data for each speed zone; k is the sequence number of the speed zone, and the value range is 0-6; a kn is the nth acceleration data of the kth speed zone, then the average of the acceleration of the sample driver in the kth speed zone The method comprises the following steps:
The standard deviation delta k of the acceleration of the sample driver in the kth speed zone is:
4. a human-machine interaction method for automatically adjusting vehicle dynamics based on vehicle acceleration according to claim 3, wherein: in the step S3, the specific steps are as follows:
S31: if N acceleration data are just acquired in a certain speed zone, calculating an acceleration mean value and an acceleration standard deviation according to the acceleration of the speed zone, and outputting the acceleration mean value and the acceleration standard deviation for subsequent operation;
s32: and meanwhile, the acceleration data of each speed zone is cleared, the counting is restarted after the first i are cleared, and if the acceleration data acquired by a certain speed zone is less than i, the acquisition is restarted after the clearing.
5. The human-computer interaction method for automatically adjusting vehicle dynamics based on vehicle acceleration according to claim 4, wherein: in the step S4, the specific steps are as follows:
average acceleration of current user And the acceleration standard deviation lambda k of the current user are respectively compared with the acceleration mean value and the acceleration standard deviation of the sample driver correspondingly; setting alpha and beta as weight coefficients for comparing the acceleration mean value and the acceleration standard deviation respectively, wherein alpha+beta=1, wherein alpha occupies a larger weight, and beta occupies a smaller weight; the acceleration correction coefficient mu k for each speed band is:
6. the human-computer interaction method for automatically adjusting vehicle dynamics based on vehicle acceleration according to claim 5, wherein: in the step S5, the specific steps are as follows:
s51: multiplying the torque correction coefficient mu k by the accelerator opening of a reference accelerator pedal MAP in the abscissa system to obtain a new mapping table of torque and accelerator pedal opening;
S52: after the same pedal opening is stepped on by different users under different road conditions, different power outputs obtained by driving styles corresponding to the mapping table learned in the step S51 are obtained.
7. The human-computer interaction method for automatically adjusting vehicle dynamics based on vehicle acceleration according to claim 1, wherein: the method also comprises the following steps:
Displaying a result of intelligent adjustment of the vehicle power on a vehicle-mounted display screen in a progress bar mode; the progress bar defaults to an intermediate position, indicating unadjusted vehicle dynamics; the progress bar is adjusted to the left end to show that the power output of the vehicle is economical; the progress bar is adjusted to the right end to represent the vehicle power output offset motion.
8. The human-computer interaction method for automatically adjusting vehicle dynamics based on vehicle acceleration according to claim 7, wherein: the method also comprises the following steps:
The real-time expression of the intelligent vehicle power regulation result is transmitted through CAN messages of the ECU and the vehicle-mounted system; the CAN message format is as follows:
Dividing a progress bar into 8 sub-gears;
if the power coefficient output by the ECU control strategy is between 0.5 and 0.75, the regulated vehicle power is the weakest, and the progress bar is at the leftmost side;
If the power coefficient output by the ECU control strategy is more than 1.8, the regulated vehicle power is the strongest, and the progress bar is at the rightmost side.
9. A computer storage medium, characterized by: a computer program executable by a computer processor is stored therein, the computer program executing a man-machine interaction method for automatically adjusting vehicle dynamics based on vehicle acceleration according to any one of claims 1 to 8.
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| CN116653993B (en) * | 2023-03-08 | 2024-04-05 | 广州汽车集团股份有限公司 | Throttle sensitivity control model training method, sensitivity control method and device |
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