CN117584996A - New energy automobile control method - Google Patents
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- B—PERFORMING OPERATIONS; TRANSPORTING
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- 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
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- 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
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Abstract
The invention discloses a new energy automobile control method, which relates to the technical field of automobile control and comprises the following steps: s101, acquiring information of a vehicle controller during operation, wherein the information comprises prediction model information and system data processing information; s102, establishing a data analysis model with the prediction model information and the system data processing information, generating an abnormal evaluation index, and establishing an analysis set with the generated plurality of abnormal evaluation indexes. According to the invention, through monitoring the running state of the vehicle controller, when the situation that the vehicle controller possibly has an abnormal problem occurs, the vehicle driver is prompted to know the situation in time, the situation that the controller cannot correctly identify and respond to dangers is effectively avoided, meanwhile, the situation that the controller cannot correctly control the speed, steering or braking force of the vehicle, so that the vehicle is out of control, deviates from a preset track or does not control movement is effectively avoided, and the accident risk is reduced.
Description
Technical Field
The invention relates to the technical field of automobile control, in particular to a new energy automobile control method.
Background
The controller in the new energy automobile control system is crucial, and the controller has the main functions of coordinating and managing various functions of the electric automobile, and ensuring safe, efficient and reliable operation of the automobile. The controller adjusts the energy flow between the battery and the motor in real time by monitoring parameters such as the battery state, the motor rotating speed, the vehicle speed and the like so as to optimize the energy utilization rate. In addition, the controller is responsible for managing the braking system, suspension system, and driving force distribution to provide optimal ride performance and vehicle stability. Through accurate algorithm and sensor integration, the controller makes the new energy automobile can adapt to different driving conditions in a flexible way, ensures the high-efficient work of system simultaneously, provides more intelligent, environmental protection's driving experience for the user. Therefore, the controller plays a key role in the new energy automobile, and the vehicle is prompted to achieve the best performance in terms of energy management and power control.
A method for model-based predictive control (Model Predictive Control, MPC) of a vehicle controller, intended to achieve accurate control of the vehicle, MPC being an optimal control method, by building a mathematical model to describe the dynamic behavior of the vehicle and using the model for prediction and optimization to determine an optimal control strategy. The MPC method based on the non-offset model particularly emphasizes the accuracy in modeling the behavior of the vehicle to minimize the effect of modeling errors on the control performance.
In a vehicle control system based on a model without offset, it is first necessary to build a vehicle dynamics model describing the equations of motion and constraints of the vehicle. This may be a multivariate dynamics model that takes into account factors such as the mass, inertia, tire friction, etc. of the vehicle and may include control inputs such as steering, acceleration, and braking of the vehicle, which in turn is used by the MPC algorithm to predict vehicle behavior over a period of time in the future. Based on the current vehicle state and environmental information, the MPC optimization problem solves an optimization program to find the optimal control strategy. The optimization objective may be to minimize errors in the driving process of the vehicle, minimize energy consumption, maximize comfort, etc., depending on the application scenario and requirements, once the optimization problem is solved, the MPC algorithm may provide an optimal control sequence, including steering angle, acceleration, braking force, etc., as the control command at the current moment. The vehicle controller adjusts the state of the vehicle based on these commands to cause the vehicle to travel along the desired trajectory.
The prior art has the following defects: however, when the operation state of the vehicle controller predicted based on the non-offset model is abnormal, the prior art cannot sense the situation in time, and the abnormal controller may not correctly recognize and respond to dangerous situations such as avoidance of an obstacle, prevention of collision, or maintenance of stability of the vehicle, which may cause traffic accidents, casualties, or loss of property, and secondly, the abnormal controller may not correctly control the speed, steering, or braking force of the vehicle, which may cause the vehicle to lose control, deviate from a predetermined trajectory, or occur uncontrolled movement, increasing accident risk.
The above information disclosed in the background section is only for enhancement of understanding of the background of the disclosure and therefore it may include information that does not form the prior art that is already known to a person of ordinary skill in the art.
Disclosure of Invention
The invention aims to provide a new energy automobile control method, which is characterized in that through monitoring the running state of a vehicle controller, the possible abnormal problem of the vehicle controller is perceived in time, when the possible abnormal problem of the vehicle controller occurs, a vehicle driver is prompted to know the situation in time, the situation that the controller cannot correctly recognize and respond to dangers is effectively avoided, meanwhile, the situation that the controller cannot correctly control the speed, steering or braking force of the vehicle, and the situation that the vehicle loses control, deviates from a preset track or does uncontrolled movement is effectively avoided, and the accident risk is reduced, so that the problems in the background technology are solved.
In order to achieve the above object, the present invention provides the following technical solutions: a new energy automobile control method comprises the following steps:
s101, acquiring information of a vehicle controller during operation, wherein the information comprises prediction model information and system data processing information;
s102, establishing a data analysis model with the prediction model information and the system data processing information, generating an abnormal evaluation index, and establishing an analysis set with the generated plurality of abnormal evaluation indexes;
s103, analyzing a plurality of abnormal evaluation indexes in the analysis set, and solving an abnormal evaluation index average value and an abnormal evaluation index standard deviation of the plurality of abnormal evaluation indexes in the analysis set;
s104, comparing the abnormal evaluation index average value and the abnormal evaluation index standard deviation with an abnormal evaluation index reference threshold value and a standard deviation reference threshold value respectively to generate a risk level signal, and sending out a corresponding early warning prompt or not sending out an early warning prompt to the risk level signal.
Preferably, the predictive model information includes a speed-related coefficient stability index, and the speed-related coefficient stability index is calibrated to SPC after acquisition x The system data processing information comprises response time length abnormality indexes, and after acquisition, the response time length abnormality indexes are calibrated as XYY x 。
Preferably, the logic for obtaining the velocity correlation coefficient stability index is as follows:
s1, obtaining the predicted vehicle speed and the actual vehicle speed of a vehicle controller at different moments in T time, and calibrating the predicted vehicle speed and the actual vehicle speed as VX respectively i And VY i I represents the numbers of the predicted vehicle speed and the actual vehicle speed of the vehicle controller at different moments in time T, i=1, 2, 3, 4, … …, n being a positive integer;
s2, calculating a speed correlation coefficient of the vehicle controller, wherein the calculated expression is as follows:
in (1) the->Predicted vehicle speed VX for different times of time T for vehicle controller i The obtained calculation formula is as follows: />For the actual vehicle speed VY of the vehicle controller at different moments in time T i Average value of (2), calculation of the acquisitionThe formula is: />ρ represents a speed-related coefficient of the vehicle controller;
s3, calibrating speed correlation coefficients of the vehicle controller at different moments in the T time as rho y Y represents the number of the speed correlation coefficient of the vehicle controller at different moments in time T, y=1, 2, 3, 4, … …, N and N are positive integers, the standard deviation of the speed correlation coefficient of the vehicle controller is calculated, and the standard deviation is calibrated as E, and then the calculation formula of the standard deviation E is as follows:
wherein (1)>For the average value of the velocity correlation coefficients of the vehicle controller at different moments in the T time, the acquired calculation formula is as follows: />
S4, acquiring a speed correlation coefficient stability index through a standard deviation E of a vehicle controller in a time T, wherein the acquired calculation formula is as follows: SPC (SPC) x ＝E*e E+1 。
Preferably, the logic for obtaining the response time length abnormality index is as follows:
s1, setting a response time reference value for the response time of the vehicle controller, when the response time is greater than or equal to the response time reference value, indicating that the response time of the vehicle controller is longer, and when the response time is less than the response time reference value, indicating that the response time of the vehicle controller is shorter;
s2, acquiring all response time lengths of the vehicle controller in the T time, and calibrating the response time lengths as XY (X, Y) h H represents the number of the response time of the vehicle controller in the time T, h=1, 2, 3, 4, … …, H being a positive integer;
s3, calibrating the response time length larger than the response time length reference value as XY k And will be greater than the response duration XY of the response duration reference value k Establishing a data set, wherein k represents the number of the response time length larger than the response time length reference value, and k=1, 2, 3, 4, … … and M, wherein M is a positive integer;
s4, calculating a response time abnormal index according to the response time and the response time reference value in the data set, wherein the calculation formula is as follows:
in the formula, XY Ginseng radix Representing a response time reference.
Preferably, a velocity-dependent coefficient stability index SPC is obtained x And response time length abnormality index XYY x Then, a data analysis model is established to generate an abnormality evaluation index PG x The formula according to is:
wherein f1 and f2 are respectively the velocity-related coefficient stability index SPC x And response time length abnormality index XYY x And f1 and f2 are both greater than 0.
Preferably, the abnormality evaluation index PG generated during the running of the controller in the T time is obtained x Then, several abnormality evaluation indexes PG are used x Establishing an analysis set, and calibrating the analysis set as R, wherein R= { PG x }＝{PG 1 、PG 2 、PG 3 、…、PG m X represents the number of abnormality assessment indices within the analysis set, x=1, 2, 3, 4, … …, mM is a positive integer.
Preferably, a plurality of abnormality evaluation indexes PG in the analysis set are obtained x And calibrating the mean value and standard deviation of the abnormality evaluation index as PG Average of And PG Standard deviation of The calculation formula of the mean value of the abnormality evaluation index is:
wherein, the calculation formula of the mean value of the abnormality evaluation index is as follows: />
Preferably, the abnormality evaluation index reference threshold and the standard deviation reference threshold are compared with a preset abnormality evaluation index reference threshold and a preset standard deviation reference threshold respectively, and the risk level signal is generated as follows:
if the average value of the abnormal evaluation indexes is larger than or equal to the reference threshold value of the abnormal evaluation indexes, or if the average value of the abnormal evaluation indexes is smaller than the reference threshold value of the abnormal evaluation indexes and the standard deviation of the abnormal evaluation indexes is larger than or equal to the reference threshold value of the standard deviation, a high risk level signal is generated, and a high risk early warning prompt is sent to the high risk signal;
if the average value of the abnormal evaluation indexes is smaller than the reference threshold value of the abnormal evaluation indexes and the standard deviation of the abnormal evaluation indexes is smaller than the reference threshold value of the standard deviation, a low risk grade signal is generated, and a low risk early warning prompt is not sent to the low risk signal.
In the technical scheme, the invention has the technical effects and advantages that:
according to the invention, through monitoring the running state of the vehicle controller, the possible abnormal problem of the vehicle controller is perceived in time, when the possible abnormal problem of the vehicle controller occurs, the situation is prompted to be known by a vehicle driver in time, the situation that the controller cannot correctly recognize and respond to dangers is effectively avoided, meanwhile, the situation that the controller cannot correctly control the speed, steering or braking force of the vehicle, and the vehicle is out of control, deviates from a preset track or does uncontrolled movement is effectively avoided, and the accident risk is reduced;
according to the invention, by comprehensively analyzing a plurality of groups of evaluation data generated when the vehicle controller operates, rather than only analyzing single evaluation data, the emergency situation caused by single analysis can be effectively prevented, the accuracy of the operation state evaluation of the vehicle controller is further improved, the vehicle controller is ensured to control the vehicle efficiently and accurately, and the risk of accidents is further reduced.
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For a clearer description of embodiments of the present application or of the solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments described in the present invention, and that other drawings may be obtained according to these drawings for a person skilled in the art.
Fig. 1 is a flow chart of a method for controlling a new energy automobile.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art.
The invention provides a new energy automobile control method as shown in fig. 1, which comprises the following steps:
s101, acquiring information of a vehicle controller during operation, wherein the information comprises prediction model information and system data processing information;
the prediction model information comprises a speed correlation coefficient stability index, and after acquisition, the speed correlation coefficient stability index is calibrated to SPC x ；
When the stability of the velocity correlation coefficient of the vehicle controller based on the non-offset model prediction is poor, the following serious accident risks may be caused:
inaccurate speed control: poor stability of the speed-related coefficient may cause a large deviation of the model in predicting the speed of the vehicle, and if the controller cannot accurately predict and control the speed of the vehicle, the vehicle may accelerate too fast or too slow, thereby affecting the control of the driver on the vehicle and increasing the risk of collision or out of control;
unstable braking operation: failure of the controller to accurately predict speed changes may result in instability of braking operation, if the controller fails to accurately predict changes in vehicle speed and adjusts braking effort and time accordingly, it may result in excessive or insufficient braking, causing dangerous situations of vehicle runaway or braking failure;
distance calculation error: poor stability of the speed-related coefficient may cause an increase in error in distance calculation, and if the controller cannot accurately predict the speed and position of the vehicle, the safety distance between the vehicle and the obstacle ahead may be erroneously estimated, which may cause the vehicle to collide with the vehicle ahead or the obstacle ahead, increasing the risk of accident;
improper turn control: poor stability of the speed correlation coefficient may cause inaccuracy of turning control, and if the controller cannot accurately predict the relationship between the speed and the steering angle of the vehicle, the controller may cause the vehicle to perform steering operation too early or too late during turning, so that the vehicle is out of control or has dangerous situations such as sideslip;
therefore, the speed correlation coefficient information of the vehicle controller during operation is obtained, and the situation that the speed correlation coefficient of the vehicle controller is poor in stability can be perceived in time;
the logic for obtaining the velocity correlation coefficient stability index is as follows:
s1, obtaining the predicted vehicle speed and the actual vehicle speed of a vehicle controller at different moments in T time, and calibrating the predicted vehicle speed and the actual vehicle speed as VX respectively i And VY i Table iNumbers showing the predicted vehicle speed and the actual vehicle speed of the vehicle controller at different times within the T time, i=1, 2, 3, 4, … …, n being a positive integer;
it should be noted that, the prediction model generally accepts a series of input features, and outputs a corresponding vehicle speed prediction result, where the input features may include vehicle sensor data (such as vehicle speed, acceleration, steering angle), environmental information (such as road condition, traffic flow), and other relevant information, and by inputting the input features into the non-offset prediction model, the non-offset prediction model predicts the speed of the vehicle according to the patterns and rules learned during training;
the actual vehicle speed can be obtained through sensors or measuring equipment installed on the vehicle, such as a vehicle speed sensor, a vehicle-mounted GPS (global positioning system), an Inertial Measurement Unit (IMU) and the like, wherein the sensors can measure the speed of the vehicle in real time and provide the speed for a vehicle control system to use, and the actual vehicle speed is a real-time measured value obtained directly from the sensors and reflects the actual speed of the vehicle at a specific moment;
s2, calculating a speed correlation coefficient of the vehicle controller, wherein the calculated expression is as follows:
in (1) the->Predicted vehicle speed VX for different times of time T for vehicle controller i The obtained calculation formula is as follows: />For the actual vehicle speed VY of the vehicle controller at different moments in time T i The obtained calculation formula is as follows: />ρ represents a speed-related coefficient of the vehicle controller;
s3, calibrating speed correlation coefficients of the vehicle controller at different moments in the T time as rho y Y represents the number of the speed correlation coefficient of the vehicle controller at different moments in time T, y=1, 2, 3, 4, … …, N and N are positive integers, the standard deviation of the speed correlation coefficient of the vehicle controller is calculated, and the standard deviation is calibrated as E, and then the calculation formula of the standard deviation E is as follows:
wherein (1)>For the average value of the velocity correlation coefficients of the vehicle controller at different moments in the T time, the acquired calculation formula is as follows: />
From the standard deviation E of the vehicle controller in the T time, the larger the expression value of the standard deviation E of the speed correlation coefficient of the vehicle controller at different moments in the T time is, the higher the expression value of the speed correlation coefficient rho is y The larger the fluctuation of the speed correlation coefficient at different times in the T time, the smaller the expression value of the standard deviation E of the speed correlation coefficient at different times in the T time, indicating the speed correlation coefficient ρ y The smaller the fluctuations of (2);
s4, acquiring a speed correlation coefficient stability index through a standard deviation E of a vehicle controller in a time T, wherein the acquired calculation formula is as follows: SPC (SPC) x ＝E*e E+1 ；
The calculation formula obtained by the speed correlation coefficient stability index shows that the larger the expression value of the speed correlation coefficient stability index of the vehicle controller in the time T is, the worse the running state of the vehicle controller is, the larger the risk of abnormal control of the vehicle is, otherwise, the better the running state of the vehicle controller is, and the smaller the risk of abnormal control of the vehicle is;
the system data processing information comprises response time length abnormality indexes, and after acquisition, the response time length abnormality indexes are calibrated as XYY x ；
When the response time of the vehicle controller predicted based on the non-offset model is long, the following serious accident risks may be caused:
delayed braking operation: the longer response time of the controller may cause delay of braking operation, if the vehicle controller cannot timely respond and adjust braking force, the braking reaction time may be prolonged, thus increasing the parking distance, and failing to timely avoid collision or reduce impact force;
excessive acceleration or deceleration: the long response time can cause the vehicle controller to be unable to adjust acceleration or deceleration operation in time, if the vehicle needs to accelerate or decelerate rapidly to cope with emergency, the long response time of the controller can cause the power output to be unable to be adjusted in time, so that the vehicle can not operate according to expectations, and the risk of accidents is increased;
unstable turning control: the long response time can cause the vehicle controller to not respond and adjust steering operation in time when turning, which can cause excessive or insufficient steering of the vehicle, cause unstable turning and even out of control, and increase the risk of dangerous situations such as sideslip or turning over;
the risk of collision increases: longer response times may result in the vehicle controller failing to timely identify and respond to obstacles or other vehicles in front, which may increase the risk of collisions, particularly in high speed travel or complex traffic environments;
vehicle control failure: the overlong response time can cause the vehicle controller to not effectively control the vehicle at the key moment, and if the controller cannot respond in time and take necessary control measures, the vehicle control can be invalid, so that serious accidents are caused;
therefore, the response time length of the vehicle controller during operation is obtained, and the abnormal condition of the response time length of the vehicle controller can be timely perceived;
the logic for obtaining the response time length abnormality index is as follows:
s1, setting a response time reference value for the response time of the vehicle controller, when the response time is greater than or equal to the response time reference value, indicating that the response time of the vehicle controller is longer, and when the response time is less than the response time reference value, indicating that the response time of the vehicle controller is shorter;
it should be noted that, the setting of the response time reference value is not specifically limited herein, and according to the design and performance requirements of the vehicle control system, an expected response time may be set as a reference, and this range may be formulated according to actual requirements and safety standards;
s2, acquiring all response time lengths of the vehicle controller in the T time, and calibrating the response time lengths as XY (X, Y) h H represents the number of the response time of the vehicle controller in the time T, h=1, 2, 3, 4, … …, H being a positive integer;
it should be noted that, by inserting a time stamp or a timer into the controller, the controller can start timing after receiving an input signal, then record the response time outputted by the controller, and real-time monitoring can provide accurate response time information;
s3, calibrating the response time length larger than the response time length reference value as XY k And will be greater than the response duration XY of the response duration reference value k Establishing a data set, wherein k represents the number of the response time length larger than the response time length reference value, and k=1, 2, 3, 4, … … and M, wherein M is a positive integer;
s4, calculating a response time abnormal index according to the response time and the response time reference value in the data set, wherein the calculation formula is as follows:
in which XY Ginseng radix A reference value representing a response time length;
the calculation expression of the response time length abnormality index shows that the larger the expression value of the response time length abnormality index generated when the vehicle controller operates in the T time is, the worse the operation state of the vehicle controller is, the larger the risk of abnormal control of the vehicle is, otherwise, the better the operation state of the vehicle controller is, and the smaller the risk of abnormal control of the vehicle is;
s102, establishing a data analysis model with the prediction model information and the system data processing information, generating an abnormal evaluation index, and establishing an analysis set with the generated plurality of abnormal evaluation indexes;
acquisition of the speed-related coefficient stability index SPC x And response time length abnormality index XYY x Then, a data analysis model is established to generate an abnormality evaluation index PG x The formula according to is:
wherein f1 and f2 are respectively the velocity-related coefficient stability index SPC x And response time length abnormality index XYY x F1, f2 are both greater than 0;
from the calculation formula, the stability index SPC of the speed correlation coefficient generated by the controller during the T time x The greater the response time length abnormality index XYY x The larger, i.e. abnormality assessment index PG x The greater the expression value of (a) indicates that the worse the running state of the vehicle controller is, the greater the risk of abnormal control of the vehicle is, and the stability index SPC of the speed correlation coefficient generated when the controller runs in the T time x Smaller response time abnormality index XYY x The smaller, i.e. abnormality assessment index PG x The smaller the expression value of (c) is, the better the running state of the vehicle controller is, and the smaller the risk of abnormal control of the vehicle is;
acquiring an abnormality evaluation index PG generated during operation of the controller in T time x Afterwards, several are arrangedAbnormality evaluation index PG x Establishing an analysis set, and calibrating the analysis set as R, wherein R= { PG x }＝{PG 1 、PG 2 、PG 3 、…、PG m X represents the number of abnormality assessment indices within the analysis set, x=1, 2, 3, 4, … …, m being a positive integer;
s103, analyzing a plurality of abnormal evaluation indexes in the analysis set, and solving an abnormal evaluation index average value and an abnormal evaluation index standard deviation of the plurality of abnormal evaluation indexes in the analysis set;
solving a plurality of abnormality evaluation indexes PG in an analysis set x And calibrating the mean value and standard deviation of the abnormality evaluation index as PG Average of And PG Standard deviation of The calculation formula of the mean value of the abnormality evaluation index is:
wherein, the calculation formula of the mean value of the abnormality evaluation index is as follows:
s104, respectively comparing the abnormal evaluation index average value and the abnormal evaluation index standard deviation with an abnormal evaluation index reference threshold value and a standard deviation reference threshold value to generate a risk level signal, and sending out a corresponding early warning prompt or not sending out an early warning prompt for the risk level signal;
if the abnormality evaluation index is greater than or equal to the abnormality evaluation index reference threshold, the running state of the vehicle controller is indicated to be poor, the probability of the occurrence of abnormal control risk of the vehicle is indicated to be greater, and if the abnormality evaluation index is smaller than the abnormality evaluation index reference threshold, the running state of the vehicle controller is indicated to be better, and the probability of the occurrence of abnormal control risk of the vehicle is indicated to be smaller;
comparing the abnormality evaluation index reference threshold value and the standard deviation reference threshold value with a preset abnormality evaluation index reference threshold value and a preset standard deviation reference threshold value respectively, and generating a risk level signal as follows:
if the average value of the abnormal evaluation indexes is larger than or equal to the reference threshold value of the abnormal evaluation indexes, or if the average value of the abnormal evaluation indexes is smaller than the reference threshold value of the abnormal evaluation indexes and the standard deviation of the abnormal evaluation indexes is larger than or equal to the reference threshold value of the standard deviation, a large amount of abnormal evaluation indexes exist in the analysis set or the abnormal evaluation indexes are larger than or equal to the reference threshold value of the abnormal evaluation indexes, a high risk grade signal is generated, a high risk early warning prompt is sent to the high risk signal, the running state of the vehicle controller of the vehicle driver is prompted to possibly have abnormality, the driver is prompted to know the situation in time, the situation that the controller cannot correctly recognize and respond to dangers is effectively avoided, meanwhile, the situation that the speed, the steering or the braking force of the vehicle cannot be correctly controlled by the controller is effectively avoided, the vehicle is out of control, deviates from a preset track or uncontrolled movement is caused, and the accident risk is reduced;
if the average value of the abnormal evaluation indexes is smaller than the reference threshold value of the abnormal evaluation indexes and the standard deviation of the abnormal evaluation indexes is smaller than the reference threshold value of the standard deviation, generating a low risk level signal, and not sending a low risk early warning prompt to the low risk signal;
according to the invention, through monitoring the running state of the vehicle controller, the possible abnormal problem of the vehicle controller is perceived in time, when the possible abnormal problem of the vehicle controller occurs, the situation is prompted to be known by a vehicle driver in time, the situation that the controller cannot correctly recognize and respond to dangers is effectively avoided, meanwhile, the situation that the controller cannot correctly control the speed, steering or braking force of the vehicle, and the vehicle is out of control, deviates from a preset track or does uncontrolled movement is effectively avoided, and the accident risk is reduced;
according to the invention, by comprehensively analyzing a plurality of groups of evaluation data generated when the vehicle controller operates, rather than only analyzing single evaluation data, the emergency situation caused by single analysis can be effectively prevented, the accuracy of the operation state evaluation of the vehicle controller is further improved, the vehicle controller is ensured to control the vehicle efficiently and accurately, and the risk of accidents is further reduced.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in accordance with the embodiments of the present application are all or partially produced. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center by wired or wireless means (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains one or more sets of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.
It should be understood that, in various embodiments of the present application, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present application.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the several embodiments provided in this application, it should be understood that the disclosed systems and methods may be implemented in other ways. For example, the embodiments described above are merely illustrative, e.g., the division of the elements is merely a logical functional division, and there may be additional divisions in actual implementation, e.g., multiple elements or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The foregoing is merely specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the present application, and the changes and substitutions are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (8)
1. The new energy automobile control method is characterized by comprising the following steps of:
s101, acquiring information of a vehicle controller during operation, wherein the information comprises prediction model information and system data processing information;
s102, establishing a data analysis model with the prediction model information and the system data processing information, generating an abnormal evaluation index, and establishing an analysis set with the generated plurality of abnormal evaluation indexes;
s103, analyzing a plurality of abnormal evaluation indexes in the analysis set, and solving an abnormal evaluation index average value and an abnormal evaluation index standard deviation of the plurality of abnormal evaluation indexes in the analysis set;
s104, comparing the abnormal evaluation index average value and the abnormal evaluation index standard deviation with an abnormal evaluation index reference threshold value and a standard deviation reference threshold value respectively to generate a risk level signal, and sending out a corresponding early warning prompt or not sending out an early warning prompt to the risk level signal.
2. The method of claim 1, wherein the predictive model information includes a speed-related coefficient stability index, and wherein the speed-related coefficient stability index is calibrated to SPC after the acquisition x The system data processing information comprises response time length abnormality indexes, and after acquisition, the response time length abnormality indexes are calibrated as XYY x 。
3. The method for controlling a new energy vehicle according to claim 2, wherein the logic for obtaining the stability index of the velocity-related coefficient is as follows:
s1, obtaining the predicted vehicle speed and the actual vehicle speed of a vehicle controller at different moments in T time, and calibrating the predicted vehicle speed and the actual vehicle speed as VX respectively i And VY i I represents the time when the vehicle controller is different in T timeThe numbers of the predicted vehicle speed and the actual vehicle speed are carved, i=1, 2, 3, 4, … …, n is a positive integer;
s2, calculating a speed correlation coefficient of the vehicle controller, wherein the calculated expression is as follows:
，
in the method, in the process of the invention,predicted vehicle speed VX for different times of time T for vehicle controller i The obtained calculation formula is as follows: /> For the actual vehicle speed VY of the vehicle controller at different moments in time T i The obtained calculation formula is as follows: />ρ represents a speed-related coefficient of the vehicle controller;
s3, calibrating speed correlation coefficients of the vehicle controller at different moments in the T time as rho y Y represents the number of the speed correlation coefficient of the vehicle controller at different moments in time T, y=1, 2, 3, 4, … …, N and N are positive integers, the standard deviation of the speed correlation coefficient of the vehicle controller is calculated, and the standard deviation is calibrated as E, and then the calculation formula of the standard deviation E is as follows:
，
wherein,for the average value of the velocity correlation coefficients of the vehicle controller at different moments in the T time, the acquired calculation formula is as follows: />
S4, acquiring a speed correlation coefficient stability index through a standard deviation E of a vehicle controller in a time T, wherein the acquired calculation formula is as follows: SPC (SPC) x ＝E*e E+1 。
4. The control method of a new energy automobile according to claim 3, wherein the logic for obtaining the response time length abnormality index is as follows:
s1, setting a response time reference value for the response time of the vehicle controller, when the response time is greater than or equal to the response time reference value, indicating that the response time of the vehicle controller is longer, and when the response time is less than the response time reference value, indicating that the response time of the vehicle controller is shorter;
s2, acquiring all response time lengths of the vehicle controller in the T time, and calibrating the response time lengths as XY (X, Y) h H represents the number of the response time of the vehicle controller in the time T, h=1, 2, 3, 4, … …, H being a positive integer;
s3, calibrating the response time length larger than the response time length reference value as XY k And will be greater than the response duration XY of the response duration reference value k Establishing a data set, wherein k represents the number of the response time length larger than the response time length reference value, and k=1, 2, 3, 4, … … and M, wherein M is a positive integer;
s4, calculating a response time abnormal index according to the response time and the response time reference value in the data set, wherein the calculation formula is as follows:
in which XY Ginseng radix Representing a response time reference.
5. The method of claim 4, wherein a speed-related coefficient stability index SPC is obtained x And response time length abnormality index XYY x Then, a data analysis model is established to generate an abnormality evaluation index PG x The formula according to is:
wherein f1 and f2 are respectively the velocity-related coefficient stability index SPC x And response time length abnormality index XYY x And f1 and f2 are both greater than 0.
6. The method for controlling a new energy automobile according to claim 5, wherein the abnormality evaluation index PG generated when the controller is operated in the T time is obtained x Then, several abnormality evaluation indexes PG are used x Establishing an analysis set, and calibrating the analysis set as R, wherein R= { PG x }＝{PG 1 、PG 2 、PG 3 、…、PG m X represents the number of abnormality assessment indices within the analysis set, x=1, 2, 3, 4, … …, m being a positive integer.
7. The method for controlling a new energy automobile according to claim 6, wherein a plurality of abnormality evaluation indexes PG in the analysis set are obtained x And calibrating the mean value and standard deviation of the abnormality evaluation index as PG Average of And PG Standard deviation of The calculation formula of the mean value of the abnormality evaluation index is:
，
wherein, the calculation formula of the mean value of the abnormality evaluation index is as follows:
8. the method for controlling a new energy vehicle according to claim 7, wherein the process of comparing the abnormality evaluation index reference threshold and the standard deviation reference threshold with the abnormality evaluation index reference threshold and the standard deviation reference threshold, respectively, to generate the risk level signal is as follows:
if the average value of the abnormal evaluation indexes is larger than or equal to the reference threshold value of the abnormal evaluation indexes, or if the average value of the abnormal evaluation indexes is smaller than the reference threshold value of the abnormal evaluation indexes and the standard deviation of the abnormal evaluation indexes is larger than or equal to the reference threshold value of the standard deviation, a high risk level signal is generated, and a high risk early warning prompt is sent to the high risk signal;
if the average value of the abnormal evaluation indexes is smaller than the reference threshold value of the abnormal evaluation indexes and the standard deviation of the abnormal evaluation indexes is smaller than the reference threshold value of the standard deviation, a low risk grade signal is generated, and a low risk early warning prompt is not sent to the low risk signal.
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