Road motor vehicle exhaust emission prediction method based on improved attention bidirectional long-short-term memory network

A long-short-term memory and tail gas emission technology, which is applied in the direction of prediction, neural learning methods, biological neural network models, etc., can solve the problems of manpower consumption, resource and time costs, waste of human resources and time, and reduce detection accuracy. The effect of generalization ability, shortening test time and improving detection accuracy

Active Publication Date: 2020-09-01
HEFEI INSTITUTES OF PHYSICAL SCIENCE - CHINESE ACAD OF SCI
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Problems solved by technology

[0005](2) After the preheating of the PEMS instrument is completed, zeroing and calibration of the gas to be measured is required. This work not only requires mixed gas, NO2 and N2 three types of standard sample gas cylinders and FID ignition aid H2 gas cylinders, and require trained professional and technical personnel to operate, resulting in the detection premise The conditions are relatively harsh, and it takes a certain amount of manpower, resources and time costs
[0006](3) During the actual road pollutant emission test of PEMS, FEM, NOx and FID modules often have equipment failures, communication interruptions with the host computer, etc., and relevant Professional and technical personnel accompany and follow the car throughout the process, resulting in a waste of human resources and time, as well as certain safety hazards
[0007] (4) After continuous monitoring by PEMS for about two hours, as the measurement time goes by, the baseline drift of the exhaust emission test data becomes more and more obvious, which reduces the detection precision

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  • Road motor vehicle exhaust emission prediction method based on improved attention bidirectional long-short-term memory network
  • Road motor vehicle exhaust emission prediction method based on improved attention bidirectional long-short-term memory network
  • Road motor vehicle exhaust emission prediction method based on improved attention bidirectional long-short-term memory network

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Embodiment Construction

[0051] In this example, if figure 1 As shown, a road vehicle exhaust emission prediction method based on the improved attention two-way long-short-term memory network is carried out as follows:

[0052] Step 1. Use the PEMS detection equipment and the OBD on-board diagnostic system to jointly collect the exhaust emission data of road motor vehicles in p days, and collect the data of q working conditions every day, and the collection time of each working condition is T, so as to obtain m features The n=p×q×T tail gas emission data set, the tail gas emission data set includes PEMS and OBD, and the PEMS data includes real-time CO 2 , CO, NO, NO 2 , THC, O 2 Concentration, ambient humidity, temperature, sampling mass flow rate, sampling volume flow rate, sampling tube temperature, etc. OBD data includes vehicle instantaneous speed, engine instantaneous power, engine speed, engine load, etc., a total of m items of test data, that is, m features, record for D origin =(d ij ) n...

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Abstract

The invention discloses a road motor vehicle exhaust emission prediction method for improving an attention bidirectional long-short-term memory network. The method comprises the steps of 1, using PEMSand OBD detection equipment for jointly collecting motor vehicle exhaust emission data; 2, performing missing data compensation and normalization preprocessing on the tail gas emission data set; 3, establishing an improved Attention-Bi-LSTM attention bidirectional long and short term memory network model; 4, determining hyper-parameters of the model by adopting a pre-experiment; and 5, optimizingmodel parameters by adopting an adaptive learning rate algorithm to finish prediction model training. According to the method, all characteristic factors influencing road motor vehicle tail gas emission can be fully considered, the tail gas emission prediction precision is improved, and the method has a large application range, so that the PEMS tail gas emission test time can be effectively shortened, and the consumption of manpower, resources and time cost is reduced.

Description

technical field [0001] The invention relates to the technical field of road motor vehicle exhaust emission prediction algorithms, in particular to an actual road pollutant emission prediction method based on an improved two-way long-short-term memory network. Background technique [0002] In recent years, the number of motor vehicles in the country has grown rapidly, resulting in road motor vehicle exhaust emissions becoming one of the main factors that pollute the urban environment. Effective monitoring methods for road motor vehicle exhaust emissions are of great significance for improving urban air quality. At present, the commonly used methods for road vehicle exhaust emission monitoring mainly include: chassis dynamometer method, tunnel test method, laser telemetry method, smoke plume chasing measurement method, and vehicle-mounted portable emission measurement method (Portable Emission Measurement System, PEMS). The experimental results of the chassis dynamometer metho...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/26G06N3/04G06N3/08
CPCG06Q10/04G06Q50/26G06N3/049G06N3/084G06N3/045Y02T10/40
Inventor 张玉钧谢皓何莹尤坤李潇毅范博强余冬琪李梦琪雷博恩刘建国刘文清
Owner HEFEI INSTITUTES OF PHYSICAL SCIENCE - CHINESE ACAD OF SCI
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