工况特征参数对客车燃油经济性影响分析

为了获得实际车辆运行特征对车辆燃油经济性的影响,为车辆研发和能量控制策略制定提供重要的参考,对实际运行的城市公交和通勤车辆采集的大量数据进行了数据分析和预测方程的构建。运行数据来自于3辆公交车,6个月内118 d/10 s次的出行数据;测量的数据包括:时间、地理位置、车速、油耗等。通过数据计算和分析,获得了34个不同的运行工况特征参数(包括:负荷率,最高速度,平均速度,平均行驶速度,平均加速度,速度时间比例,加速度时间比例等)。采用统计方法研究了特征参数对于油耗的影响。结果表明,负荷率、平均速度和车速所占百分比与车辆油耗有强烈的相关性;且具有很高的稳定性;基于多元线性回归的方法预测特征参数变化下的燃油经济性,由于存在特征参数之间很强的线性关系,导致结果的不稳定。为了避免这种不稳定性,采用了基于因子分析的方法,获得了相互独立的若干个能够代表所有特征参数信息90%以上的代表性因子;然后采用多元线性回归分析得到的方程能够满足油耗预测的需要。

Analysis of the Influence of Operating Condition Characteristic Parameters on Fuel Economy of the Bus

In order to get impacts of actual driving characteristics on vehicle economy and provide important reference for vehicle development and formulation of energy control strategies, data analysis was made on substantial data gathered in urban buses and commuting shuttles in actual operation and forecast formula was built in the study. Driving data were obtained from 3 company shuttle bus, whose trip data were measured 10 times/s for 118 days in 6 months. Measured data cover: time, geographical location, driving speed, fuel consumption, etc. Through data computation and analysis, totally 34 different driving cycle characteristic parameters (including: load rate, maximum speed, average speed, average running speed, average acceleration, ratio of speed and time and ratio of acceleration and time) were acquired. Statistical method was adopted in study on the impacts of parameters on fuel consumption. The results showed that the percentage of load rate, average speed and driving speed were highly relevant with vehicle fuel consumption and were highly stable; the method based on multiple linear regression was adopted to predict fuel economy in varying parameters and results were unstable due to high linear relation between parameters; in order to avoid this instability, the method based on factor analysis was adopted to get several mutually independent representative factor on behalf of over 90% of information of all the parameters and the formula drawn from MLR analysis was able to satisfy demand for fuel consumption forecast.