考虑不同路面特征的军用履带车辆循环工况构建

doi:10.12382/bgxb.2022.0828

摘要/Abstract

摘要：

为全面有效地评估车辆动力系统的燃油经济性,建立考虑不同路面特征的履带车辆循环工况。收集铺面路、砂石路和起伏土路3种路面的实车运行数据作为原始数据样本,并利用小波分析对数据进行滤波处理。基于处理后的实车数据,综合利用主成分分析法、K均值聚类和马尔可夫链构建3种路面下的典型循环工况,并对构建的工况进行相似度分析。研究结果表明:采用新方法能够有效地构建出符合军用履带车辆运行特点的循环工况,3种路面下循环工况的特征均与原始数据有极高的相似度;3种路面车速-加速度联合概率分布与原始数据的均方根误差分别为0.0356、0.0039和0.0196,新构建的工况能够有效反映履带车辆的真实运行情况。

关键词: 军用履带车辆, 工况构建, 路面特征, 马尔可夫链, 聚类

Abstract:

A tracked vehicle driving cycle considering road features is established to effectively evaluate the fuel economy of the vehicle powertrain. The operating data of vehicle on paved road, gravel road and undulating dirt road are collected as original data, and the data are filtered by wavelet analysis. Based on the processed vehicle data, the typical driving cycles under the condition of the three road surfaces are constructed by using principal component analysis, K-means clustering and Markov chain, and the similarity analysis of the constructed driving cycle is carried out. The research results show that the proposed method can be used to effectively construct the driving cycle that resembles the operating characteristics of military tracked vehicles, and the characteristics of the driving cycles under the condition of the three road surfaces have high similarity with the original data, the root mean square errors of the joint vehicle speed-acceleration probability distribution with the original data for the three road surfaces are 0.0356, 0.0039 and 0.0196, respectively, so the driving cycle constructed in this paper can effectively reflect the real operation of tracked vehicles.

Key words: military tracked vehicle, driving cycle construction, road feature, Markov chain, clustering

中图分类号:

U467

王绪, 李睿, 黄英, 沈继伟, 商显赫. 考虑不同路面特征的军用履带车辆循环工况构建[J]. 兵工学报, 2024, 45(3): 907-915.

WANG Xu, LI Rui, HUANG Ying, SHEN Jiwei, SHANG Xianhe. Construction of Driving Cycle for Military Tracked Vehicles Considering Road Features[J]. Acta Armamentarii, 2024, 45(3): 907-915.

图/表 14

图1 数据网络

Fig.1 Data network

图2 履带车辆车速采集

Fig.2 Tracked vehicle speed acquisition

表1 特征参数

Table 1 Feature parameters

特征参数	符号	特征参数	符号
平均车速/(km·h^-1)	$\bar{v}$	平均加速度/(m·s^-2)	$\bar{a}$
最大车速/(km·h^-1)	v_max	最大加速度/(m·s^-2)	a_max
最小车速/(km·h^-1)	v_min	最小加速度/(m·s^-2)	a_min
车速标准差/(km·h^-1)	v_std	加速度标准差/(m·s^-2)	a_std
加速比例/%	P_acc	减速比例/%	P_dec

图3 平均稳定度变化趋势

Fig.3 Trend of average stability

图4 小波变换后数据对比

Fig.4 Data comparison after wavelet transform

图5 循环工况构建方法

Fig.5 Driving cycle construction method

图6 特征参数相关性分析

Fig.6 Correlation analysis of feature parameters

表2 累积贡献率

Table 2 Cumulative contribution rate

主成分	铺面路	砂石路	起伏土路
1	0.3488	0.3143	0.3888
2	0.6623	0.6059	0.7416
3	0.8853	0.8721	0.8974
4	0.9528	0.9505	0.9522
5	0.9924	0.9958	0.9968
6	0.9979	0.9992	0.9974
7	0.9991	0.9997	0.9988
8	0.9996	0.9998	0.9997
9	0.9999	0.9999	0.9999
10	1.0000	1.0000	1.0000

图7 K均值聚类结果

Fig.7 K-means clustering results

图8 状态转移概率

Fig.8 State transition probability

图9 驾驶循环构建流程

Fig.9 Driving cycle construction process

图10 考虑3种路面特征的循环工况

Fig.10 Driving cycle considering three road features

表3 联合概率分布的RMSE

Table 3 RMSE of joint probability distribution

铺面路	砂石路	起伏土路
0.0356	0.0039	0.0196

图11 车速-加速度联合概率分布对比

Fig.11 Comparison of joint probability distributions of vehicle speed and acceleration

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