CN108020427B - Pure electric vehicle gear shifting quality evaluation method based on GA-BP neural network - Google Patents
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Abstract
The invention discloses a pure electric vehicle gear shifting quality evaluation method based on a GA-BP neural network, which comprises the following steps of collecting evaluation index data comprising gear shifting time t, gear shifting success rate η, gear shifting impact degree j and longitudinal speed change rate omega, determining standard scale values of the index data, quantizing the weights of the evaluation indexes to obtain weights, and calculating comprehensive scores according to the standard scale values and the weights, wherein the comprehensive scores W ═ Sigma WiRiIn the formula, WiIs the score of the ith evaluation item, RiThe weight of the ith evaluation item, W is the comprehensive score; step four, correcting the comprehensive score W to obtain a corrected evaluation score W'; and step five, establishing an evaluation model based on the three-layer GA-BP neural network to obtain the gear shifting quality.
Description
Technical Field
The invention relates to the field of electric automobile gear shifting quality evaluation, in particular to a pure electric automobile gear shifting quality evaluation method based on a GA-BP neural network.
Background
Based on the current situation that the gear shifting quality evaluation of the automobile for a long time is manually and subjectively evaluated, objectivity and stability are lacked, a mature system and method specially aiming at the gear shifting quality evaluation of a pure electric vehicle are lacked, and the electric automobile is compared with a traditional automobile: 1. noise of the engine is eliminated, and noise and vibration from the gearbox are amplified; 2. the electric automobile has power deviation due to the motor and the power supply, so that gear shifting selection is important, and the evaluation of gear shifting needs to take power performance into consideration; 3. the emission of the electric automobile is 0, so the emission problem does not need to be considered; as shown in fig. 1, the gear shifting process of the AMT of the electric vehicle adopts the characteristics of the motor and realizes synchronous gear shifting by controlling the motor mode, and the specific process is divided into the following stages: 1. in the gear shifting request stage, the TCU judges through signals of an accelerator pedal, vehicle speed, a current gear and the like, when the gear shifting condition is met, a gear shifting request is sent to the VCU, the VCU authorizes the VCU after judgment, and the TCU determines whether to shift gears according to the condition; 2. in the motor torque reduction stage, as clutch-free gear shifting is adopted, in order to smoothly pick up a gear, the motor performs torque reduction and prepares for gear shifting; 3. in the gear-off stage, after the torque of the motor is reduced, the transmission starts to be off; 4. in the motor speed regulation stage, after gear picking is finished, in order to finish gear shifting, the linear speeds of two engaged teeth of a target are equal to achieve a synchronous state, so that the motor is regulated; 5. in the gear engaging stage, when the difference of the rotating speeds meets a certain range, the TCU controls the gear shifting mechanism to shift gears, but at the moment, the difference of the relative acceleration is possibly large due to the action of the residual torque, so that the gear shifting generates large impact and even fails; 6. in the torque recovery stage, after the transmission is successfully shifted, the power requirement of the target gear needs to be met, so that the torque needs to be recovered as soon as possible. The gear shifting quality is formulated according to the gear shifting process, the characteristics of the electric automobile and the gear shifting quality requirement.
In the chinese patent application No. 200710056088.9, in order to overcome the disadvantage of subjective evaluation of vehicle shift quality, the inventor provides a data sample trained by a neural network model to evaluate the shift quality, but in this application, the evaluation of the output by the inventor is only artificially evaluated in real time by a driver, and the output data is obtained by scoring through trial driving.
Therefore, based on the above problems in the prior art, it is necessary to provide an index and a method for evaluating the gear shifting quality of the pure electric vehicle clutch-free AMT.
Disclosure of Invention
The invention designs and develops a pure electric vehicle gear shifting quality evaluation method, and aims to correct the subjective evaluation value and perform more accurate optimization evaluation on the pure electric vehicle gear shifting quality.
The technical scheme provided by the invention is as follows:
a pure electric vehicle gear shifting quality evaluation method based on a GA-BP neural network comprises the following steps:
acquiring evaluation index data, including gear shifting time t, gear shifting success rate η, gear shifting impact degree j and longitudinal speed change rate omega;
determining a standard scale value for the index data, and carrying out quantization processing on the weight of the evaluation index to obtain a weight;
thirdly, calculating a comprehensive score according to the standard scale value and the weight value, wherein the comprehensive score formula is W ═ Σ WiRiIn the formula, WiIs the score of the ith evaluation item, RiThe weight of the ith evaluation item, W is the comprehensive score;
step four, correcting the comprehensive score W to obtain a corrected evaluation score W ', wherein the correction formula of W' is as follows
Where t is the shift time, V is the driving speed, and V is1、V2、V3For empirical calibration of vehicle speed, R1、R2、R3The method comprises the following steps of (1) obtaining an empirical calibration constant, wherein r is the radius of a wheel, and n is the rotating speed of an output shaft of a gearbox;
establishing an evaluation model based on a three-layer GA-BP neural network to obtain the gear shifting quality; wherein, input layer vector x ═ { x of three-layer GA-BP neural network is determined1,x2,x3,x4Get the output layer vector y ═ y1-input layer vectors are mapped to intermediate layers, said intermediate layer vectors o ═ o1,o2,…,omIn the formula, m is the number of intermediate layer nodes, x1Is the shift time coefficient, x2For the success rate coefficient of shifting, x3For the shift shock factor, x4Is the coefficient of longitudinal velocity change, y1The evaluation score after correction.
Preferably, in the fifth step, the optimizing the weights and the threshold in the GA-BP neural network includes the following steps:
step a, initializing a weight value and a threshold value in a BP neural network;
b, calculating the fitness of the weight and the threshold, wherein the fitness calculation formula is
step c, obtaining a final weight and a threshold when the fitness calculation result meets the output condition; when the fitness calculation result does not meet the output condition, performing genetic operation calculation until the fitness calculation result meets the output condition;
and d, calculating errors of the weight and the threshold, and updating the weight and the threshold to obtain an output result.
Preferably, the genetic manipulation comprises:
selecting operation calculation with the formula
In the formula (f)iThe fitness value of an individual i is shown, and n is the number of the population;
calculating the cross operation with the formula of amj＝(1-b)amj+anjb，amj＝(1-b)amj+anjb, in the formula, a set of paired chromosomes amAnd anPerforming single-point cross operation on the jth gene position, wherein b is a random number between 0 and 1; and
calculating the variation operation by the formula
In the formula (I), the compound is shown in the specification,
is XkUpper bound of r2Random number, G is the current iteration number, GmaxR is a random number between 0 and 1 for the maximum number of evolutions.
Preferably, the standard scale table makes the data follow a normal distribution, and values falling within a normal interval are calculated by interpolation to obtain a score.
Preferably, an interpolation calculation formula is adopted as
In the formula, x0,x1,x2Is an interpolation point, y0,y1,y2Is the value of the interpolation point, x is the point to be evaluated, and L (x) is the score after the interpolation calculation.
Preferably, the quantifying the weight of the evaluation index includes: and determining the relative importance of the evaluation index data, comparing the upper and lower adjacent items, writing each weight from bottom to top, and normalizing the weights.
Preferably, in the fifth step, the formula for normalizing the shift time t, the shift success rate η, the shift shock degree j and the longitudinal speed change rate ω is as follows:
in the formula, xkThe collected evaluation index data t, η, j, ω, k is 1,2,3,4, xmin,xmaxAnd respectively the minimum value and the maximum value in the corresponding evaluation index data monitoring parameters.
Preferably, in the fifth step, the number of intermediate layer nodes is 7.
The invention has the following beneficial effects:
1. in the face of nonlinear evaluation indexes and subjective evaluation standards, the BP neural network evaluation method adopts BP neural network evaluation which can fit any nonlinear mapping, adopts genetic algorithm for optimization, adapts to the intelligence and automation of evaluation, and has the advantages of high convergence rate and strong prediction capability;
2. in order to enable the weight of the gear shifting quality index to be more objective and better quantized, the ancient forest method is adopted for quantizing the gear shifting quality index, the comprehensive score calculated by the weight is corrected, the comprehensive score is mutually corrected with the subjective index of an expert survey method and used as the output of a training network, and the gear shifting quality evaluation is more comprehensive and accurate.
Drawings
FIG. 1 is a schematic diagram of a conventional gear shifting of an electric vehicle.
FIG. 2 is a schematic diagram of correction of shift quality weight assignment and subjective comprehensive evaluation to neural network training.
FIG. 3 is a flow chart of a neural network optimized by a genetic algorithm.
Fig. 4 is a diagram showing a neural network structure of an evaluation index.
Fig. 5 is a model diagram of gear shift quality evaluation of an electric vehicle.
Detailed Description
The present invention is further described in detail below with reference to the attached drawings so that those skilled in the art can implement the invention by referring to the description text.
As shown in fig. 2, the invention provides a pure electric vehicle gear shifting quality evaluation method based on a GA-BP neural network, wherein the gear shifting quality refers to a degree of rapid and smooth gear shifting under the condition of ensuring vehicle dynamic performance and power train life, and is reflected in reliability, dynamic performance, comfort and durability. These several qualities are coupled and mutually constrained. Due to the fact that the AMT is complex in gear shifting process, long in time and large in impact, evaluation of gear shifting quality of the AMT is more complex. A more extensive evaluation of the shift quality also relates to vibrations and noise, economy, emissions, since it is electric vehicles here that emissions are not taken into account, while from the viewpoint of energy conservation, a low impact means better economy and better comfort. Less noise and vibration.
Therefore, the index of the gear shift quality evaluation can embody the characteristics essentially and has better availability.
The AMT gear shifting process is more complex than AT, power performance is insufficient due to power interruption in gear shifting, the power interruption for a long time can cause great reduction of the vehicle speed, alternate gear shifting is easily caused, and inconvenience is brought to gear shifting. The shift duration should therefore be an important shift quality indicator.
Meanwhile, the success rate of gear shifting should be guaranteed primarily, so the success rate of gear shifting should also be an index for evaluating the gear shifting quality.
The gear shifting impact degree is used as a quantity closely related to the acceleration, is a good characteristic value of the speed of the change rate of the motor torque, can cause pitting, abrasion and breakage of gears of a gearbox, causes relatively large noise, has good measurability, and is used as an important index.
Research shows that the longitudinal speed fluctuation of the automobile can bring great influence on comfort, so the longitudinal speed change rate of the automobile also needs to be a more important index.
Therefore, the evaluation indexes are selected from gear shifting time t, gear shifting success rate η, gear shifting impact degree j and longitudinal speed change rate omega.
The invention provides an AMT gear shifting quality evaluation method for a pure electric vehicle, which specifically comprises the following steps:
the method comprises the following steps of firstly, obtaining data through tests, wherein the data are mainly used for urban highways according to use conditions, and the electric automobiles respectively run for 30 minutes at speeds of 20km/h, 40km/h and 60km/h on road surfaces with the same road conditions (test conditions are selected according to use requirements), and collecting the data;
step two, an expert investigation method is adopted, an evaluation standard scale table is made for each gear shifting quality according to actual conditions and is made to be as normal distribution as possible, values falling in an interval are calculated by interpolation, any three points in the interval are selected as interpolation points, and x is respectively selected as the interpolation point0,x1,x2Determining the value y of the interpolation point0,y1,y2Calculating a formula from the difference
Solving an interpolation result L (x), namely a value to be solved;
among them, since the difference in the degree of stringency of the evaluation requirements may be changed as appropriate, it is represented here only by symbols, as shown in table 1;
TABLE 1 evaluation Standard Scale Table
And step three, according to the characteristics and the actual importance of the pure electric vehicle, the weights of all indexes are quantized by adopting an A. ancient forest method, so that the weights of all evaluation indexes are more precise and accurate, and as shown in Table 2, the weights are as follows:
1. determining the relative importance of each index, and comparing the upper and lower adjacent items;
2. writing each weight from bottom to top;
3. normalizing the weight;
TABLE 2 weight calculation by ancient forest method
W hereinjIs the weight value of each shift index, RjIs the ratio of the upper term to the lower term, KjThe weight value after each shift quality index is normalized;
step four, calculating the comprehensive score of each gear and each speed according to the weight and the score of each item obtained by table lookup, and then calculating to obtain the final weighted comprehensive score W, wherein the calculation formula is W ∑ WiRiIn the formula, WiIs the score of the ith evaluation item, RiThe weight of the ith evaluation item, W is the comprehensive score;
step five, carrying out mutual evaluation on the scoring standard of the subjective evaluation by using the results to correct the subjective evaluation, wherein the results are shown in a table 3;
TABLE 3 subjective comprehensive evaluation chart of shift quality
Step six, evaluating by utilizing a neural network (GA-BP for short) improved by a genetic algorithm, normalizing values measured by the four evaluated items to be input, weighting the acquired data by utilizing the previous step to obtain a comprehensive score, correcting the comprehensive score, normalizing the corrected evaluation score to be output, performing network training in a determined network structure to obtain a mapping relation between input and output, and modeling by utilizing a neural network toolbox of an MATLAB (matrix laboratory) to obtain an evaluation model;
wherein the total score W is corrected by the following formula to obtain a corrected evaluation score W ', and the correction formula of W' is
In the formula, t is gear shifting time with the unit of s; v is the running speed, and the unit is km/h; v1、V2、V3Calibrating the speed for experience, wherein the unit is km/h; r1、R2、R3Is an empirical calibration constant, in units of s2(ii) a r is the wheel radius in m; n is the rotating speed of the output shaft of the gearbox, and the unit is r/min; in this embodiment, V1＝10km/h，V2＝30km/h，V3＝50km/h，R1＝0.014s2，R2＝0.114s2，R3＝0.214s2And pi takes a value of 3.14.
In another embodiment, the weights and thresholds of the neural network are optimized by using a genetic algorithm, and the process comprises the following steps: preprocessing input data, determining a BP neural network structure, optimizing a genetic algorithm and training the BP neural network;
(1) data preprocessing, including selection of a sample of data and normalization processing of the data, wherein the data is selected after processing of data collected in a test, and the output target is an evaluation score obtained by weighting and correcting the collected data; the normalization processing of the data is to synchronously integrate and send the data to the (0,1) interval to prevent the error caused by too large difference of numerical magnitude of each index, wherein the maximum and minimum method is adopted, and the calculation formula is
In the formula, xmin,xmax,xkRespectively the minimum and maximum values of each evaluation in the sample and a certain value in the sampleIn the present embodiment, the input layer vector x ═ x of the three-layer GA-BP neural network is determined1,x2,x3,x4}，x1Is the shift time coefficient, x2For the success rate coefficient of shifting, x3For the shift shock factor, x4Is the longitudinal velocity change rate coefficient;
(2) determining a neural network structure, wherein four evaluation indexes are provided, so that four inputs exist, one output exists, the double-invisible neural network structure can approach any one nonlinear function, a single hidden layer structure is selected, the number of nodes of a hidden layer is 7, and the number of nodes is selected as shown in FIGS. 3-5, wherein the number of nodes corresponds to the minimum average error of each calculation expected result and actual result obtained by programming;
(3) determining a weight value and a threshold value of a genetic algorithm optimized BP network, wherein the weight value and the threshold value comprise genetic codes, individual fitness calculation and genetic operation; wherein the genetic operation comprises selection operation calculation, cross operation calculation and mutation operation calculation;
fitness calculation function:
in the formula, ykIs the desired output of the BP neural network,
is the prediction output of the BP neural network, and z is the number of training samples;
when the fitness calculation result meets the output condition, obtaining a final weight and a threshold; when the fitness calculation result does not meet the output condition, performing genetic operation calculation until the fitness calculation result meets the output condition;
the genetic operation calculation comprises selection operation calculation, cross operation calculation and mutation operation calculation:
and calculating the selection operation: probability of selection for each individual i:
in the formula (f)iIs the fitness value of an individual i, n isThe number of the population;
and (3) calculating the cross operation: a set of paired chromosomes a by using a real single-point crossing methodmAnd anPerforming single-point cross operation on the jth gene position: a ismj＝(1-b)amj+anjb，anj＝(1-b)anj+amjb, in the formula, b is a random number between 0 and 1;
calculating the variation operation by the formula
In the formula (I), the compound is shown in the specification,
is XkUpper bound of r2Random number, G is the current iteration number, GmaxR is a random number between 0 and 1 for the maximum number of evolutions.
Through the operations, BP network training can be carried out, and an objective model for evaluating the gear shifting quality of the electric automobile based on the BP neural network of the genetic algorithm is established by using the trained network and combining functions provided by a neural network toolbox and a genetic algorithm toolbox in MATLAB.
In another embodiment, the weight of each gear and the use of each gear at each speed are quantitatively determined by the ancient forest method according to the use condition, the shift quality of the common vehicle speed of the common gear is heavily weighted (as described in step two), and the comprehensive score is obtained by a weighted summation method.
While embodiments of the invention have been described above, it is not limited to the applications set forth in the description and the embodiments, which are fully applicable in various fields of endeavor to which the invention pertains, and further modifications may readily be made by those skilled in the art, it being understood that the invention is not limited to the details shown and described herein without departing from the general concept defined by the appended claims and their equivalents.
Claims (7)
1. A pure electric vehicle gear shifting quality evaluation method based on a GA-BP neural network is characterized by comprising the following steps:
acquiring evaluation index data, including gear shifting time t, gear shifting success rate η, gear shifting impact degree j and longitudinal speed change rate omega;
determining a standard scale value for the index data, and carrying out quantization processing on the weight of the evaluation index to obtain a weight;
thirdly, calculating a comprehensive score according to the standard scale value and the weight value, wherein the comprehensive score formula is W ═ Σ WiRiIn the formula, WiIs the score of the ith evaluation item, RiThe weight of the ith evaluation item, W is the comprehensive score;
step four, correcting the comprehensive score W to obtain a corrected evaluation score W ', wherein a correction formula of W' is as follows:
where t is the shift time, V is the driving speed, and V is1、V2、V3For empirical calibration of vehicle speed, R1、R2、R3The method comprises the following steps of (1) obtaining an empirical calibration constant, wherein r is the radius of a wheel, and n is the rotating speed of an output shaft of a gearbox;
establishing an evaluation model based on a three-layer GA-BP neural network to obtain the gear shifting quality; wherein, input layer vector x ═ { x of three-layer GA-BP neural network is determined1,x2,x3,x4Get the output layer vector y ═ y1-input layer vectors are mapped to intermediate layers, said intermediate layer vectors o ═ o1,o2,…,omIn the formula, m is the number of intermediate layer nodes, x1Is the shift time coefficient, x2For the success rate coefficient of shifting, x3For the shift shock factor, x4Is the coefficient of longitudinal velocity change, y1The evaluation score after correction;
in the fifth step, the weight and the threshold in the GA-BP neural network are optimized, and the method comprises the following steps:
step a, initializing a weight value and a threshold value in a BP neural network;
b, calculating the fitness of the weight and the threshold, wherein the fitness calculation formula is as follows:
step c, obtaining a final weight and a threshold when the fitness calculation result meets the output condition; when the fitness calculation result does not meet the output condition, performing genetic operation calculation until the fitness calculation result meets the output condition;
and d, calculating errors of the weight and the threshold, and updating the weight and the threshold to obtain an output result.
2. The pure electric vehicle gear-shifting quality evaluation method based on the GA-BP neural network as claimed in claim 1, wherein the genetic operation comprises:
selecting operation calculation with the formula
In the formula (f)iThe fitness value of an individual i is shown, and n is the number of the population;
calculating the cross operation with the formula of amj＝(1-b)amj+anjb，amj＝(1-b)amj+anjb, in the formula, a set of paired chromosomes amAnd anPerforming single-point cross operation on the jth gene position, wherein b is a random number between 0 and 1; and
and (3) performing variation operation calculation, wherein the calculation formula is as follows:
in the formula (I), the compound is shown in the specification,
is XkUpper bound of r2Random number, G is the current iteration number, GmaxR is a random number between 0 and 1 for the maximum number of evolutions.
3. A GA-BP neural network-based pure electric vehicle gear-shifting quality evaluation method as claimed in claim 2, wherein the standard ruler table makes the data obey normal distribution, and values falling within a normal interval are calculated by interpolation to obtain a score.
4. The pure electric vehicle gear-shifting quality evaluation method based on the GA-BP neural network as claimed in claim 3, wherein the interpolation calculation formula is adopted as follows:
in the formula, x0,x1,x2Is an interpolation point, y0,y1,y2Is the value of the interpolation point, x is the point to be evaluated, and L (x) is the score after the interpolation calculation.
5. A pure electric vehicle gear-shifting quality evaluation method based on GA-BP neural network as claimed in claim 4, wherein the quantization processing of the weight of the evaluation index comprises: and determining the relative importance of the evaluation index data, comparing the upper and lower adjacent items, writing each weight from bottom to top, and normalizing the weights.
6. A GA-BP neural network-based pure electric vehicle gear-shifting quality evaluation method as claimed in claim 5, wherein in the fifth step, the formula for normalizing the gear-shifting time t, the gear-shifting success rate η, the gear-shifting impact rate j and the longitudinal speed change rate ω is as follows:
7. A pure electric vehicle gear-shifting quality evaluation method based on a GA-BP neural network according to any one of claims 5-6, wherein in the fifth step, the number of intermediate layer nodes is 7.
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| CN109979035B (en) * | 2019-04-03 | 2020-06-19 | 吉林大学 | Static gear shifting quality evaluation method for vehicle carrying hydraulic mechanical automatic transmission |
| CN111027618B (en) * | 2019-12-09 | 2022-05-20 | 西华大学 | Automobile dynamic property and economic expectation quantification method |
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| CN112581029A (en) * | 2020-12-29 | 2021-03-30 | 淮阴工学院 | AMT gear shifting quality evaluation method |
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