Abstract: : Driving conditions are one of the key factors that affect the overall performance of the vehicle, the focus of each enterprise's products is different, and the fuel consumption of each market is different. Based on the differences, it is necessary to develop driving conditions in line with market demand for vehicle power matching. Firstly, vehicle driving conditions clustering is completed to identify the proportion of subdivided market road conditions.Driving data fragments are cut and the driving characteristic parameters are removed by principal component analysis. Features that do not have much impact avoid wasting computing resources. Simulation verification shows that the road spectrum simulation results based on this method are closer to the real market performance than those of Chinese-world transient vehicle cycle (C-WTVC), China heavy-duty commercial vehicle test cycle for tractor trailers (CHTC-TT), with an error of no more than 7.6%.

Key words: commercial vehicle; driving conditions; fragments; principal component analysis; cluster analysis

摘要： 行驶工况是对整车性能影响的关键因素之一，而各企业产品的侧重细分市场不同，各 细分市场的性能各不相同。基于以上差异需开发与市场契合的行驶工况，用于车型的动力匹 配及开发。首先完成车辆行驶工况聚类，识别细分市场路况占比。然后进行行驶数据片段切 割，行驶特征参数经过主成分分析，除去对车辆行驶特征影响不大的特征，避免浪费计算资 源。通过仿真验证表明，基于该方法制定的路谱仿真结果相比中国重型商用车瞬态工况 （C-WTVC）、中国重型商用车辆行驶工况-半挂牵引车（CHTC-TT）工况更接近于真实市场 表现，误差小于等于 7.6%。

关键词: 商用车；行驶工况；片段；主成分分析；聚类分析