基于最小二乘支持向量机的怠速时间预测

doi:10.13229/j.cnki.jdxbgxb20170904

摘要/Abstract

摘要： 汽车起停系统因其可以在汽车怠速时关闭发动机减少车辆怠速油耗而被大量应用。但在交通拥堵的实际工况下,在短时停车时使用自动起停功能反而会增加燃油消耗。针对这一问题使用基于时空学习的最小二乘支持向量机对怠速时长进行预测,在短时怠速时不关闭发动机。实验表明,本文提出的基于时空学习的最小二乘支持向量机能够对怠速时长进行预测,准确率在80%以上。将此方法应用于怠速起停控制,基于怠速预测的起停控制系统在早晚高峰节油2%左右,在平峰时节油0.2%。同时,高峰期减少50%以上的起停次数,平峰期减少15%以上的起停次数,能有效提升驾驶舒适性。

关键词: 车辆工程, 起停系统, 怠速时间预测, 时空学习, 最小二乘支持向量机, 节油控制

Abstract: Engine start-stop system is widely used as it can reduce fuel consumption by turning off the engine while idling. But in traffic congestion conditions, the engine start-stop system increases fuel consumption because of turning off engine during short time idling. To solve this problem, Least Square Support Vector Machine (LS-SVM) is applied to predict the idling time, that the engine will not be turned off during short time idling. Experiment results show that the accuracy of idling time prediction using LS-SVM can reach 80%. The engine start-stop system using this method can reduce fuel consumption by about 0.2% during smooth hour. Meanwhile the driving comfort can be improved by reducing more than 50% start-stop numbers during rush hour and more than 15% during smooth hour.

Key words: vehicle engineering, start-stop system, idling time prediction, time and space learning, least squares support vector machines(LS-SVM), fuel saving control

中图分类号:

U471.1

隗海林, 包翠竹, 李洪雪, 李明达. 基于最小二乘支持向量机的怠速时间预测[J]. 吉林大学学报(工学版), 2018, 48(5): 1360-1365.

KUI Hai-lin, BAO Cui-zhu, LI Hong-xue, LI Ming-da. Idling time prediction method based on least square support vector machine[J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(5): 1360-1365.

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