CN111272420A - Four-dimensional integrated big data AMT comprehensive evaluation method - Google Patents
Four-dimensional integrated big data AMT comprehensive evaluation method Download PDFInfo
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- CN111272420A CN111272420A CN202010140409.9A CN202010140409A CN111272420A CN 111272420 A CN111272420 A CN 111272420A CN 202010140409 A CN202010140409 A CN 202010140409A CN 111272420 A CN111272420 A CN 111272420A
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- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M13/00—Testing of machine parts
- G01M13/02—Gearings; Transmission mechanisms
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
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Abstract
The invention discloses a four-dimensional integrated big data AMT comprehensive evaluation method which is used for collecting the operation decision of a driver, the performance parameters of a vehicle and the actual working condition of a road, sending the collected data to an evaluation model and finally obtaining the evaluation result of an AMT vehicle. The invention integrates the influence of three basic elements of people, vehicles and roads, coordinates the relationship among people, vehicles and roads, analyzes the operation behavior of a driver, analyzes the running state parameters of the vehicle, regulates the running of the road under the working condition, finishes the data acquisition of the regulated mileage, and establishes an evaluation algorithm to evaluate by integrating the data. The invention can organically combine the subjective evaluation and the objective measurement of the vehicle driving performance, and comprehensively evaluate the driving performance through software calculation, namely quantitatively evaluate the vehicle driving performance.
Description
Technical Field
The invention belongs to the field of performance evaluation and test of a whole vehicle, and particularly relates to a four-dimensional integrated big data AMT comprehensive evaluation method.
Background
The AMT is an important transmission component of the automobile, and the whole automobile performances such as dynamic property, fuel economy, comfort and the like of the automobile are influenced to a great extent. Various large automobile companies at home and abroad match AMT application on commercial vehicles, and have already well played the role of AMT. The gear shifting quality evaluation of the AMT is an important research direction in the field of vehicle transmission systems, and in the process, whether a control target extraction method is reasonable and can comprehensively reflect the gear shifting quality, whether an evaluation method is correct and feasible and whether the advantages and disadvantages of all indexes can be comprehensively reflected, and the problems can directly influence the success or failure of control strategy development and reach the optimal degree. Therefore, a perfect comprehensive gear shifting quality evaluation system is a theoretical basis for comprehensive gear shifting control and is an effective guarantee for improving the gear shifting quality. So far, each automobile manufacturer and related enterprises have different measuring standards and evaluating methods and are rarely disclosed, so that a unified gear shifting quality evaluating standard does not exist, most of evaluations are tested in a fixed driving mode, and industrial production is carried out according to the respective standards. The running state of the automobile is personal behavior, so that the AMT performance cannot be judged by the evaluation of a single driver, the operation tests of multiple working conditions and multiple drivers are required, a certain running mileage is accumulated, the comprehensive performance of the AMT can be truly reflected by comprehensively evaluating the operation behaviors of different drivers under different working conditions, the AMT can be better judged, and the comprehensive score is finally given.
Disclosure of Invention
The invention aims to provide a four-dimensional integrated big data AMT comprehensive evaluation method, which combines the operation behavior of a driver, the AMT gear shifting quality and the driving road condition, and completes the comprehensive evaluation of the AMT performance by a mileage accumulation algorithm through the evaluation of a certain mileage number.
In order to achieve the above object, the method comprises the following steps:
step one, collecting an operation decision of a driver on an AMT vehicle;
collecting performance parameters, ride comfort data, gear shifting time and oil consumption of the AMT vehicle;
collecting the running road working condition of the AMT vehicle;
collecting the driving mileage of the AMT vehicle;
step two, sending all the collected information into an evaluation model, obtaining a driving behavior score from a driving behavior analysis model, obtaining a road difficulty coefficient from a road condition analysis model, and obtaining an AMT performance score from an AMT smoothness analysis model;
and thirdly, evaluating the driving behavior score, the road difficulty coefficient and the AMT performance score through a preset BP network neural algorithm by a set evaluation index and weight distribution module, and finally obtaining the evaluation result of the AMT vehicle.
The driving behavior analysis model obtains driving behavior big data of a driver under different road conditions by researching big data driving behaviors, calculates average scores through the operation decision of the driver on the AMT vehicle, and correspondingly adds scores or points to the AMT driver behaviors on the basis of the average scores to obtain driving behavior scores.
The road condition analysis model carries out difficulty level for different road conditions which are preset in the road condition analysis model, and the difficulty coefficient of the road is determined according to the running road condition of the AMT vehicle and the running mileage of the AMT vehicle.
The AMT ride comfort analysis model is a built-in ride comfort test result and a database, and corresponding points are added and subtracted through performance parameters, ride comfort data, gear shifting time and oil consumption of the AMT vehicle, so that the score of the AMT performance is obtained.
The operation decision of the AMT vehicle includes an accelerator pedal position OP, an accelerator pedal stroke rate OV, a brake pedal position BP, a brake stroke rate BV, a vehicle travel speed V, and an acceleration a.
The performance parameters of the AMT vehicle comprise data acquisition time, engine speed, torque, vehicle speed, current gear, target gear, input shaft speed, accelerator pedal position, brake pedal position, longitudinal acceleration, torque signal, altitude and gradient.
The smoothness of the AMT vehicle is acquired by a plurality of three-axis acceleration sensors and vibration sensors arranged on the AMT vehicle.
The fuel consumption includes instantaneous fuel consumption and cumulative fuel consumption.
The oil consumption is collected by adopting a weighing method and an accumulated oil consumption method.
The driving road conditions comprise national roads, ramps and high speeds.
Compared with the prior art, the method and the device have the advantages that the operation decision of a driver, the performance parameters of the vehicle and the actual working conditions of the road are collected, the collected data are sent to the evaluation model, and the evaluation result of the AMT vehicle is finally obtained. The invention integrates the influence of three basic elements of people, vehicles and roads, coordinates the relationship among people, vehicles and roads, analyzes the operation behavior of a driver, analyzes the running state parameters of the vehicle, regulates the running of the road under the working condition, finishes the data acquisition of the regulated mileage, and establishes an evaluation algorithm to evaluate by integrating the data. The invention can organically combine the subjective evaluation and the objective measurement of the vehicle driving performance, and comprehensively evaluate the driving performance through software calculation, namely quantitatively evaluate the vehicle driving performance.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a map of accelerator pedal opening;
FIG. 3 is a brake pedal opening profile;
FIG. 4 is a shift characteristic analysis chart;
FIG. 5 is a smoothness analysis chart.
Detailed Description
The invention will be further explained with reference to the drawings.
Referring to fig. 1, the present invention comprises the steps of:
step one, collecting an operation decision of a driver on an AMT vehicle;
collecting performance parameters, ride comfort data, gear shifting time and oil consumption of the AMT vehicle;
collecting the running road working condition of the AMT vehicle;
collecting the driving mileage of the AMT vehicle;
step two, sending all the collected information into an evaluation model, obtaining a driving behavior score from a driving behavior analysis model, obtaining a road difficulty coefficient from a road condition analysis model, and obtaining an AMT performance score from an AMT smoothness analysis model;
and thirdly, evaluating the driving behavior score, the road difficulty coefficient and the AMT performance score through a preset BP network neural algorithm by a set evaluation index and weight distribution module, and finally obtaining the evaluation result of the AMT vehicle.
The driving behavior analysis model obtains driving behavior big data of a driver under different road conditions by researching big data driving behaviors, calculates average scores through the operation decision of the driver on the AMT vehicle, and correspondingly adds scores or points to the AMT driver behaviors on the basis of the average scores to obtain driving behavior scores.
The road condition analysis model carries out difficulty level for different road conditions which are preset in the road condition analysis model, and the difficulty coefficient of the road is determined according to the running road condition of the AMT vehicle and the running mileage of the AMT vehicle.
The AMT ride comfort analysis model is a built-in ride comfort test result and a database, and corresponding points are added and subtracted through performance parameters, ride comfort data, gear shifting time and oil consumption of the AMT vehicle, so that the score of the AMT performance is obtained.
The operation decision of the AMT vehicle includes an accelerator pedal position OP, an accelerator pedal stroke rate OV, a brake pedal position BP, a brake stroke rate BV, a vehicle travel speed V, and an acceleration a.
The performance parameters of the AMT vehicle comprise data acquisition time, engine speed, torque, vehicle speed, current gear, target gear, input shaft speed, accelerator pedal position, brake pedal position, longitudinal acceleration, torque signal, altitude and gradient.
The smoothness of the AMT vehicle is acquired by a plurality of three-axis acceleration sensors and vibration sensors arranged on the AMT vehicle.
The fuel consumption includes instantaneous fuel consumption and cumulative fuel consumption. The oil consumption is collected by adopting a weighing method and an accumulated oil consumption method. The driving road conditions comprise national roads, ramps and high speeds.
The vehicle is influenced by three basic elements of a person, a vehicle and a road when running on the road, the person is a driver, the vehicle is an operated object, the road is a running environment, and the accumulated running mileage is used as a supplementary element to define large data acquisition amount. The invention starts from the four elements, establishes an AMT comprehensive evaluation method through a multi-person multi-vehicle mileage accumulation algorithm, and obtains a comprehensive score through vehicle-mounted data acquisition equipment and a background evaluation system. In order to coordinate the relationship among good people, vehicles and roads, the operation behavior of a driver is analyzed, the running state parameters of the vehicles are analyzed, the vehicles run under the specified road working condition, the data acquisition of the specified mileage is completed, and an evaluation algorithm is established by integrating the data for evaluation. The system can organically combine subjective evaluation and objective measurement of the vehicle driving performance, and comprehensively evaluate the driving performance through software calculation, namely quantitatively evaluate the vehicle driving performance.
The driver's output, see fig. 2 and 3, is the process of applying the driver's decision to the vehicle through the vehicle's operational behavior. The individual factors of the driver are mainly embodied in a series of driving operation actions forming the corresponding driving behaviors of the driver, and the driving characteristics of the driver are automatically analyzed by collecting the driving behavior operation data of the driver and the vehicle running state information.
(1) Accelerator pedal position OP, stroke rate OV, which data reflects the driver's intention to accelerate or decelerate, with a numerical interval of [0, 100 ]%;
(2) the data of the position BP of a brake pedal and the travel speed BV reflect the braking behavior of a driver and embody the deceleration intention of the driver, and the numerical interval is [0, 100 ]%;
(3) the vehicle running speed V and the acceleration reflect the running speed and the acceleration degree of a driver, and the numerical interval is [0, 100] km/h, [ -3, 3] m/s 2;
three different styles of driver models (type a, B and C) are defined by the above data.
Referring to fig. 4 and 5, the acquisition system is a computer and a vehicle data acquisition device, and acquires required vehicle performance parameters through the vehicle data acquisition device. The required basic signals acquire data time, engine rotating speed, torque, vehicle speed, current gear, target gear, input shaft rotating speed, accelerator pedal position, brake pedal position, longitudinal acceleration, torque signals, altitude, gradient, instantaneous fuel consumption, accumulated oil consumption, driving mileage and the like; arranging a plurality of triaxial acceleration sensors and vibration sensors for testing the smoothness and the gear shifting time in the gear shifting process of the vehicle;
and other derivative signals required in the evaluation process are obtained through certain operations on the basic signals input into the evaluation model through a signal processing module, and signals after longitudinal acceleration filtration, the total transmission ratio and the like are obtained. The signals provide judgment basis for a follow-up module of the gear shifting quality evaluation model.
And (3) calculating the impact degree:
in the formula, TcFor transmitting torque to the clutch, weFor the speed of rotation of the driving disk of the clutch, wcThe clutch driven plate rotational speed.
And (3) calculating the sliding wear rate:
a sliding grinding stage:
and (3) a synchronization stage:
in the formula, JeIs the rotational inertia of the engine, TrIs the resisting torque, i, acting on the clutchoIs the main reducer transmission ratio igA main gear ratio.
Fuel economy: the AMT influences the fuel economy and emission of the whole vehicle; and (3) carrying out graded evaluation (1 grade, 2 grade and 3 grade) according to the actual oil consumption, and testing the oil consumption result of the AMT by a weighing method and an accumulated oil consumption method.
Road conditions are distributed according to actual proportion, and national road tests, ramps and high speeds are carried out for a plurality of kilometers. Because the gear shifting is a dynamic process, the requirements on each index are different under different driving intentions and different working conditions. In view of the complexity of the mutual relationship among the indexes, the analysis of the respective influence factors and the mutual relationship is the basis for establishing a comprehensive evaluation system. Because the gear shifting is a dynamic process, the requirements on each index are different under different driving intentions and different working conditions.
The test needs to carry out multi-vehicle driving tests of different drivers, test big data are obtained, test analysis is carried out at the later stage, score output of each vehicle is carried out, and comprehensive evaluation of AMT is obtained by comprehensively utilizing a data algorithm; the big data is actual driving data of the whole vehicle, data collection and analysis are carried out from the perspective of multiple persons in multiple days, the driving data of the vehicle is cleaned and screened, contents such as format check, integrity check, rationality check, range check and the like are related, abnormal fluctuation values and other types of error values in the data are found, misalignment data in a database are adjusted or removed, and the data are evaluated by combining empirical knowledge and an intelligent algorithm, so that the data quality is ensured.
And (4) building an AMT comprehensive evaluation model. The MATLAB/Simulink model is built and completed by a computer through calculation, and the model consists of four modules:
a driver analysis module: a driving style recognition model for recognizing and outputting a driving style (3 types);
vehicle state parameter analysis module (3): respectively outputting respective scores (100 scores);
a shift time model; a ride comfort model; an oil consumption evaluation model;
road condition analysis model: changing the evaluation weight according to different road conditions;
a most comprehensive scoring module;
the scoring module is used as a follow-up module in the model to evaluate through a set evaluation index and a weight distribution module, and an evaluation score (100 scores) is given out most comprehensively.
Claims (10)
1. A four-dimensional integrated big data AMT comprehensive evaluation method is characterized by comprising the following steps:
step one, collecting an operation decision of a driver on an AMT vehicle;
collecting performance parameters, ride comfort data, gear shifting time and oil consumption of the AMT vehicle;
collecting the running road working condition of the AMT vehicle;
collecting the driving mileage of the AMT vehicle;
step two, sending all the collected information into an evaluation model, obtaining a driving behavior score from a driving behavior analysis model, obtaining a road difficulty coefficient from a road condition analysis model, and obtaining an AMT performance score from an AMT smoothness analysis model;
and thirdly, evaluating the driving behavior score, the road difficulty coefficient and the AMT performance score through a preset BP network neural algorithm by a set evaluation index and weight distribution module, and finally obtaining the evaluation result of the AMT vehicle.
2. The four-dimensional integrated big data AMT comprehensive evaluation method according to claim 1, wherein the driving behavior analysis model obtains driving behavior big data of a driver under different road conditions by researching big data driving behaviors, the driving behavior score is obtained by calculating the average score of the driver for the operation decision of the AMT vehicle and then performing corresponding adding or adding on AMT driver behaviors based on the average score.
3. The four-dimensional integrated big data AMT comprehensive evaluation method according to claim 1, wherein the road condition analysis model is used for carrying out difficulty level for different built-in preset road conditions, and the difficulty coefficient of the road is determined according to the running road condition of the AMT vehicle and the running mileage of the AMT vehicle.
4. The four-dimensional integrated big data AMT comprehensive evaluation method according to claim 1, wherein the AMT smoothness analysis model is a built-in smoothness test result and database, and corresponding points are added and subtracted through performance parameters, smoothness data, gear shifting time and oil consumption of the AMT vehicle, so as to obtain the point of the AMT performance.
5. The four-dimensional integrated big data AMT comprehensive evaluation method according to claim 1, wherein the operation decision of AMT vehicle comprises accelerator pedal position OP, accelerator pedal stroke rate OV, brake pedal position BP, brake stroke rate BV, vehicle running speed V and acceleration a.
6. The four-dimensional integrated big data AMT comprehensive evaluation method according to claim 1, wherein the performance parameters of AMT vehicle comprise data acquisition time, engine speed, torque, vehicle speed, current gear, target gear, input shaft speed, accelerator pedal position, brake pedal position, longitudinal acceleration, torque signal, altitude and gradient.
7. The four-dimensional integrated big data AMT comprehensive evaluation method according to claim 1, wherein the ride comfort of the AMT vehicle is collected by a plurality of three-axis acceleration sensors and vibration sensors arranged on the AMT vehicle.
8. The four-dimensional integrated big data AMT comprehensive evaluation method according to claim 1, wherein the oil consumption comprises instantaneous fuel consumption and accumulated oil consumption.
9. The four-dimensional integrated big data AMT comprehensive evaluation method according to claim 1, wherein the oil consumption is collected by a weighing method and an accumulated oil consumption method.
10. The four-dimensional integrated big data AMT comprehensive evaluation method according to claim 1, wherein the driving road conditions comprise national roads, ramps and high speeds.
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| CN108871788A (en) * | 2018-05-21 | 2018-11-23 | 吉林大学 | A kind of automatic transmission shift attribute test rack and its method of calibration and shift quality evaluation method |
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Patent Citations (5)
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| CN101516710A (en) * | 2006-09-15 | 2009-08-26 | 沃尔沃拉斯特瓦格纳公司 | Method for adapting an automatic mechanical transmission on a heavy vehicle |
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Application publication date: 20200612 |