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A fuel consumption prediction method based on least squares support vector machine

A support vector machine and least squares technology, applied in the field of transportation, can solve problems such as the difficulty of determining the number of network nodes and over-learning of the neural network, and achieve the effect of making up for the large deviation of the actual fuel consumption of the vehicle and improving the prediction accuracy

Active Publication Date: 2020-06-12
CHINA AUTOMOTIVE TECH & RES CENT
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Cheng Xiaojuan, Zhou Daoliang and others used the neural network to quantitatively predict the fuel consumption of automobiles, but the neural network has problems such as over-learning, under-learning, and the number of hidden layer network nodes is difficult to determine

Method used

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  • A fuel consumption prediction method based on least squares support vector machine
  • A fuel consumption prediction method based on least squares support vector machine
  • A fuel consumption prediction method based on least squares support vector machine

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Embodiment Construction

[0031] It should be noted that, in the case of no conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other.

[0032] The present invention will be described in detail below with reference to the accompanying drawings and examples.

[0033] A fuel consumption prediction method based on the least squares support vector machine, the specific steps are as follows.

[0034] 1. Data collection

[0035] The test used the autonomous driving method to collect the operating data of 300 light vehicles. The collection time was from May 1, 2016 to January 31, 2017, and the accumulated mileage was 1.5 million kilometers. The test system consists of two parts: vehicle-mounted data acquisition terminal (sampling frequency 1Hz) and data management platform. Data collection methods such as figure 2 As shown, the vehicle-mounted data acquisition terminal encodes the collected information according to a unified data protocol, and s...

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Abstract

The invention provides a method for predicting vehicle fuel consumption based on the improved particle swarm optimization algorithm for least squares support vector machine, using the improved particle swarm optimization algorithm to optimize the kernel function parameters and penalty factors of the least squares support vector machine model, and using the well-trained The improved particle swarm optimization algorithm optimizes the vehicle fuel consumption prediction model of the least squares support vector machine to predict the fuel consumption of the test samples. The invention adopts symmetric uncertainty to screen out sensitive characteristic parameters of fuel consumption, adopts improved particle swarm algorithm to obtain accurate kernel function parameters and penalty factors, improves the prediction accuracy of the least squares support vector machine model, and effectively compensates for type certification fuel consumption There is a large deviation from the actual fuel consumption of the vehicle.

Description

technical field [0001] The invention belongs to the field of transportation, and in particular relates to a fuel consumption prediction method based on a least square support vector machine. Background technique [0002] With the increasing pressure on energy and the environment, automobiles, which are large energy consumers, are facing increasingly severe low-carbon challenges. my country clearly stipulates that by 2020, the comprehensive fuel consumption of automobiles should reach the level of 5L / 100km. And in 2010, a light vehicle fuel consumption publicity system was established. The system stipulates that in addition to publicizing on the website of the Ministry of Industry and Information Technology, car companies must paste the fuel consumption mark on the vehicle body before leaving the factory. The announced fuel consumption is obtained through the type certification test, and the result of the type certification is about 20-30% lower than the actual fuel consump...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06Q10/04G06K9/62G06Q50/30
CPCG06Q10/04G06F18/2411G06F18/214G06Q50/40
Inventor 刘昱李孟良徐月云贺可勋汪洋郭谨玮秦孔建张诗敏
Owner CHINA AUTOMOTIVE TECH & RES CENT