CN109948237B - Method for predicting emission of bicycle - Google Patents
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Abstract
To comprehensively understand the emission indexes of new vehicles and in-use vehicles, evaluate the current situation of emission levels, predict the development trend of emission levels, formulate limit values and emission control policies, and make it difficult to achieve the intended purpose by small-scale sampling of detection institutions and enterprise data submission alone. Especially for important treatment areas or old vehicle types, a large number of real vehicle detection is high in cost, low in efficiency and extremely difficult to execute. The invention provides a method for predicting the emission quantity of a bicycle, which establishes a related vector machine model by utilizing the characteristics of the actual working condition of the bicycle and corresponding emission data, realizes the screening of key characteristics by introducing a mutual information algorithm when establishing the model, and optimizes the calculation process of the kernel function parameters of the related vector machine by introducing a frog-leaping algorithm. By using the model, for vehicles with unknown emission levels, only a small amount of random driving data is acquired by investment in low cost and is used as model input, and the model can directly output emission results, so that the model is simple, convenient and easy to implement.
Description
Technical Field
The invention belongs to the field of transportation, and particularly relates to a method for predicting emission of a bicycle.
Background
At present, for oil consumption prediction, each cart enterprise and engine enterprise have formed simulation software (such as AVL-Cruise), so that the oil consumption in the actual running of the vehicle can be accurately predicted. For macroscopic fleet emission prediction, there are models shaped by MOVES, etc. A user establishes effective association among specific power of the motor vehicle, operating condition speed of the vehicle and transient emission rate of pollutants by utilizing VSPbin distribution, and can determine or estimate the current and future vehicle activity level, technical condition and overall emission level under management factors by taking the driving characteristic data and adjustment factors of a fleet into consideration on the basis of a large amount of emission data. However, for the emission prediction of a single vehicle, the conventional modeling method is complex and cannot truly reflect the actual emission condition of the vehicle because the emission is affected by the operation conditions in addition to the in-machine purification technology and the post-treatment device.
Disclosure of Invention
The invention provides a method for predicting the emission of a bicycle, which adopts the following technical scheme:
a method for predicting the single vehicle emission comprises two parts of idle speed emission and motion segment emission accumulation, wherein each motion segment emission is predicted by an RVM model, and the idle speed emission is the product of idle speed duration and idle speed emission rate.
Further, when training the RVM model, the model is input as part of characteristic values of the motion segment, and the type of the characteristic values input as the model is determined through mutual information values of the characteristic values and the emission in the motion segment.
Further, parameters of the kernel function are determined through a frog-leaping algorithm when training the RVM model.
Further, when predicting the motion segment emissions by the RVM model, the inputs include the motion segment duration, the relative positive acceleration, and the average velocity.
Compared with the prior art, the invention has the beneficial effects that: the emission prediction method provided by the invention is used for predicting the emission of the bicycle, can well describe the relation between the running speed of the bicycle and the emission level in the current state, is simple and feasible, and can directly output the emission result by inputting a small amount of random driving data acquired by low cost as a model input for the bicycle with unknown emission level.
Drawings
FIG. 1 is a flow chart for vehicle emission model building and prediction;
fig. 2 is a histogram of the eigenvalues and the mutual information values of the emission.
Detailed Description
In this embodiment, the emission amount of the motion segment is predicted by an RVM (correlation vector machine) model, wherein the RVM model building step includes:
s1, selecting 300 groups of PEMS test data of heavy goods vehicles, including vehicle speed, NOx emission and the like, and dividing each group of test data into an idle speed segment and a motion segment;
s2, calculating an idle emission rate through the ratio of the total duration and the total displacement of all idle segments in the test data;
s3, calculating characteristic values of each motion segment in each group of test data, wherein the characteristic values comprise time length (t 1 ) Average velocity (t 2 ) Maximum speed (t) 3 ) Acceleration section average acceleration (t 4 ) Average deceleration of deceleration segment (t) 5 ) Acceleration ratio (t 6 ) Ratio of deceleration (t 7 ) Constant speed ratio (t 8 ) Relative positive acceleration (t 9 ) Maximum acceleration (t 10 ) And the corresponding discharge amount C for each motion segment is recorded.
In this embodiment, the duration, average speed, maximum speed and discharge amount are directly acquired through the raw data.
The calculation formula of the average acceleration of the acceleration section is as follows:
wherein alpha is i For the acceleration corresponding to the acceleration point, α is the average acceleration of the acceleration section, i is the sampling moment, vi is the speed of the vehicle in the ith second, T is the total working condition duration of the motion segment, and the calculation method of the average deceleration of the deceleration section and the average acceleration of the acceleration section is similar, and will not be described again.
The acceleration (deceleration/uniform) ratio is the ratio of the length of all acceleration (deceleration/uniform) working conditions of the motion segment to the total length of the working conditions of the motion segment.
The relative positive acceleration is calculated as:
where vi is the speed of the vehicle at the ith second, T is the total duration of the motion segment conditions,
is acceleration greater than 0m/s 2 And x is the running mileage of the motion segment.
In this embodiment, the characteristic values of 5 motion segments are shown in the following table:
TABLE 1
TABLE 2
S4, calculating mutual information values MI of 10 characteristic values and emission in each motion segment by using a mutual information algorithm, counting the characteristic value with the largest mutual information value in all motion segments, and selecting three characteristic values with the largest occurrence frequency as key characteristic values. The calculation formula of the mutual information value is as follows:
MI(t,C)＝H(t)+H(C)-H(t,C)
wherein t is a certain characteristic value of the motion segment, C is the total discharge amount of the motion segment, MI (t, C) is the mutual information value of the motion segment and the motion segment, H (t) is the entropy of the characteristic value t, H (C) is the entropy of the discharge amount C, and H (t, C) is the joint entropy of t and C.
In this embodiment, the three key feature values selected are the duration of the motion segment, the relative positive acceleration, and the average velocity.
S5, carrying out normalization processing on the key characteristic values to remove units and magnitude orders of all the characteristics.
S6, selecting 200 groups of PEMS test data, taking the key characteristic value corresponding to each motion segment after normalization processing as an input value, taking the discharge quantity corresponding to each motion segment as an output value, training and establishing an RVM model, and optimizing the calculation process of the kernel function parameters through a frog-leaping algorithm during training.
In this embodiment, when the kernel function calculation process is optimized by using the frog-leaping algorithm, the method includes:
s601, initializing total population, namely randomly generating P L-dimensional vectors, wherein the component w i Is interval [0, C]Setting the total population iteration number as g', setting the sub population number as Q and setting the sub population iteration number as g.
One of w i Corresponding to one training sample, L is the number of training samples, and C is the set upper limit value.
S602, calculating each component w i If the fitness value of (a) is in violation of the constraint treaty
(constraint treaty y) i Is w i Corresponding expected value), the fitness value of the component is defined as an infinity positive number, otherwise the fitness value is kept unchanged, and the sub-population is divided.
S603, aiming at each sub-population, searching the optimal component in the sub-population by using an updating formula of the worst component in the frog-leaping algorithm, then mixing all the sub-populations to generate a new total population, returning to S602, repeating the steps until the iteration times of the total population are met, and returning to the component w with the best adaptability g 。
S604, calculating w g The non-zero solution in (2) is the parameter corresponding to the kernel function.
In this embodiment, the kernel function may be a common kernel function such as a polynomial kernel function, an RBF kernel function, or a Sigmoid kernel function.
S7, selecting key characteristic values corresponding to each motion segment after normalization processing in another 100 groups of PEMS test data as input values, and comparing the output discharge amount with the actual discharge amount to verify the established RVM type.
In this embodiment, when predicting the emission of a bicycle, dividing the original data of the bicycle into an idle segment and a motion segment, calculating the total duration of the idle segment, and multiplying the total duration by the idle emission rate to obtain the idle emission K 1 Calculating key characteristic values in each motion segment, normalizing, inputting the normalized key characteristic values into an RVM model to calculate the corresponding discharge amount of the motion segment, and accumulating the discharge amounts of all the motion segments to obtain the motion discharge amount K 2 ，K 1 、K 2 The sum is the emission of the bicycle.
The above embodiments are merely preferred embodiments of the present invention and are not intended to limit the present invention, and any modifications, equivalent substitutions, improvements, etc. within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (2)
1. A method for predicting the single vehicle emission quantity is characterized in that the single vehicle emission quantity consists of an idle speed emission quantity and a motion segment emission accumulation quantity, each motion segment emission quantity is predicted by an RVM model, the idle speed emission quantity is the product of the idle speed duration and the idle speed emission rate,
when training RVM model, the input of model is the partial eigenvalue of motion segment, through each eigenvalue and mutual information value of emission in motion segment, confirm as the kind of the eigenvalue of model input, specifically as follows:
calculating mutual information value MI of characteristic value and emission in each motion segment by using mutual information algorithm, counting the characteristic value with maximum mutual information value in all motion segments, selecting three characteristic values with maximum occurrence frequency as key characteristic values, wherein the key characteristic values comprise motion segment duration, relative positive acceleration and average speed,
the relative positive acceleration is calculated as:
wherein v is i For the speed of the vehicle at the ith second, T is the total duration of the working condition of the movement section,
is acceleration greater than 0m/s 2 X is the running mileage of the motion segment,
when the emission of a bicycle is predicted, vehicle original data are divided into an idle speed section and a motion section, the total duration of the idle speed section is calculated, and the idle speed emission rate is multiplied by the total duration to obtain the idle speed emission K 1 Calculating key characteristic values in each motion segment, normalizing, inputting into RVM model to calculate the corresponding discharge amount of the motion segment, and accumulating the discharge amounts of all motion segmentsObtaining the movement emission K 2 ，K 1 、K 2 The sum is the emission of the bicycle.
2. A method for predicting bicycle emissions as in claim 1, wherein parameters of the kernel function are determined by a frog-leaping algorithm when training the RVM model.
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| CN201910199075.XA CN109948237B (en) | 2019-03-15 | 2019-03-15 | Method for predicting emission of bicycle |
| PCT/CN2020/078891 WO2020187113A1 (en) | 2019-03-15 | 2020-03-12 | Method for predicting emission amount of single vehicle |
| LU102193A LU102193B1 (en) | 2019-03-15 | 2020-03-12 | Method for predicting single-vehicle emission |
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| CN109948237B (en) * | 2019-03-15 | 2023-06-02 | 中国汽车技术研究中心有限公司 | Method for predicting emission of bicycle |
| CN112529104B (en) * | 2020-12-23 | 2024-06-18 | 东软睿驰汽车技术（沈阳）有限公司 | Vehicle fault prediction model generation method, fault prediction method and device |
| CN113806675B (en) * | 2021-08-06 | 2023-06-23 | 中汽研汽车检验中心（天津）有限公司 | NOx emission and oil consumption characteristic analysis method |
| CN113781771B (en) * | 2021-08-17 | 2022-10-28 | 四川省生态环境科学研究院 | Online operation method of IVE model |
| CN114821854B (en) * | 2022-03-28 | 2023-06-09 | 中汽研汽车检验中心（天津）有限公司 | Method for evaluating influence of working condition switching on vehicle fuel consumption |
| CN114991922B (en) * | 2022-05-30 | 2024-01-23 | 中国汽车工程研究院股份有限公司 | Real-time early warning method for exceeding of NOx emission of vehicle |
| CN117074046B (en) * | 2023-10-12 | 2024-01-02 | 中汽研汽车检验中心(昆明)有限公司 | Automobile laboratory emission test method and device in plateau environment |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2005291081A (en) * | 2004-03-31 | 2005-10-20 | Yamatake Corp | Method, device and program for creating fuel consumption or co2 emission estimation model |
| JP2008155903A (en) * | 2006-11-28 | 2008-07-10 | Noriyuki Sugimoto | Carbon dioxide indicator of automobile having evaluation device |
| CN101519073A (en) * | 2009-04-07 | 2009-09-02 | 北京大学 | Method for forecasting running load of hybrid electric vehicle |
| CN105966195A (en) * | 2016-05-31 | 2016-09-28 | 中国汽车技术研究中心 | Intelligent management system for air quality in vehicle and use method thereof |
| CN108846526A (en) * | 2018-08-08 | 2018-11-20 | 华北电力大学 | A kind of CO2 emissions prediction technique |
Family Cites Families (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8896682B2 (en) * | 2008-12-19 | 2014-11-25 | The Johns Hopkins University | System and method for automated detection of age related macular degeneration and other retinal abnormalities |
| FR2984557B1 (en) * | 2011-12-20 | 2014-07-25 | IFP Energies Nouvelles | SYSTEM AND METHOD FOR PREDICTING EMISSIONS OF POLLUTANTS OF A VEHICLE WITH SIMULTANEOUS CALCULATIONS OF CHEMICAL KINETICS AND EMISSIONS |
| US20130274952A1 (en) * | 2012-04-16 | 2013-10-17 | Feisel Weslati | Predictive powertrain control using driving history |
| US9600541B2 (en) * | 2014-05-02 | 2017-03-21 | Kookmin University Industry Academy Corporation Foundation | Method of processing and analysing vehicle driving big data and system thereof |
| CN104680232A (en) * | 2014-10-28 | 2015-06-03 | 芜湖杰诺瑞汽车电器系统有限公司 | RVM (Relevance Vector Machine)-based engine failure detecting method |
| CN106446398B (en) * | 2016-09-20 | 2019-08-20 | 中山大学 | Light-duty vehicle rate of discharge calculation method based on traffic circulation data and deterioration rate |
| CN106845371B (en) * | 2016-12-31 | 2019-10-25 | 中国科学技术大学 | A kind of city road network automotive emission remote sensing monitoring system |
| CN107368913B (en) * | 2017-06-15 | 2020-06-12 | 中国汽车技术研究中心 | Oil consumption prediction method based on least square support vector machine |
| JP7162814B2 (en) * | 2017-07-31 | 2022-10-31 | 本田技研工業株式会社 | Control device |
| CN107545122A (en) * | 2017-09-27 | 2018-01-05 | 重庆长安汽车股份有限公司 | A kind of simulation system of the vehicle gaseous effluent based on neutral net |
| CN107886188B (en) * | 2017-10-18 | 2021-12-10 | 东南大学 | Liquefied natural gas bus tail gas emission prediction method |
| CN108596104B (en) * | 2018-04-26 | 2021-01-05 | 安徽大学 | Wheat powdery mildew remote sensing monitoring method with disease characteristic preprocessing function |
| CN108629450A (en) * | 2018-04-26 | 2018-10-09 | 东南大学 | A kind of liquefied natural gas bus exhaust emissions prediction technique |
| CN108717165A (en) * | 2018-05-28 | 2018-10-30 | 桂林电子科技大学 | Lithium ion battery SOC on-line prediction methods based on data-driven method |
| CN109443779A (en) * | 2018-11-07 | 2019-03-08 | 中国汽车技术研究中心有限公司 | A kind of dynamic diagnosis extracts diesel vehicle actual motion NOXThe method and apparatus of maximum discharge bad working environments |
| CN109948237B (en) * | 2019-03-15 | 2023-06-02 | 中国汽车技术研究中心有限公司 | Method for predicting emission of bicycle |
-
2019
- 2019-03-15 CN CN201910199075.XA patent/CN109948237B/en active Active
-
2020
- 2020-03-12 WO PCT/CN2020/078891 patent/WO2020187113A1/en active Application Filing
- 2020-03-12 LU LU102193A patent/LU102193B1/en active IP Right Grant
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2005291081A (en) * | 2004-03-31 | 2005-10-20 | Yamatake Corp | Method, device and program for creating fuel consumption or co2 emission estimation model |
| JP2008155903A (en) * | 2006-11-28 | 2008-07-10 | Noriyuki Sugimoto | Carbon dioxide indicator of automobile having evaluation device |
| CN101519073A (en) * | 2009-04-07 | 2009-09-02 | 北京大学 | Method for forecasting running load of hybrid electric vehicle |
| CN105966195A (en) * | 2016-05-31 | 2016-09-28 | 中国汽车技术研究中心 | Intelligent management system for air quality in vehicle and use method thereof |
| CN108846526A (en) * | 2018-08-08 | 2018-11-20 | 华北电力大学 | A kind of CO2 emissions prediction technique |
Non-Patent Citations (4)
| Title |
|---|
| CNG公交车燃气消耗预测的研究;王爱娟 等;《汽车工程》;第33卷(第03期);241-245 * |
| LNG公交车尾气排放预测模型;唐旭 等;《交通信息与安全》;第36卷(第05期);75-82 * |
| VMT,energy consumption,and GHG emissions forecasting for passenger transportation;Rentziou A 等;《Transportation research part a-policy and practice》;第46卷(第03期);487-500 * |
| 基于GIS的机动车排放模型及其应用;William Hendricks Bachman1;王新军;苏海龙;周锐;;城市交通(第06期);91-99 * |
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