为了准确表征车辆实际运动学特征，并为行驶工况的采集、生成和识别开辟新的方向，提出了一种基于单元工况的新能源汽车行驶工况生成和识别方法。通过核主成分分析和高斯混合模型聚类对预处理后的行驶工况进行运动学片段分类。随后，基于马尔可夫链对各类单元工况组进行状态划分和行驶工况构建，根据特征参数通过构建的工况生成模型生成自定义单元工况。以上述方法作为训练样本来源，搭建基于反向传播神经网络的工况识别模型。结果表明：所构建的行驶工况与原始样本数据特征参数的平均误差仅为2.69%，自定义单元工况与对应类别样本数据特征参数的平均误差仅为1.85%，神经网络工况识别模型的综合准确度达93.8%。

In order to accurately reflect the actual kinematic characteristics of vehicles and open up new directions for driving cycle acquisition, generation and recognition, a new energy vehicle driving cycle generation and recognition method was proposed based on unit driving conditions. Kernel principal component analysis and Gaussian mixture model clustering were used to classify the kinematic fragments of pretreated driving cycle. Based on the Markov chain, the fragment state division and characteristic condition construction were carried out for various unit condition groups, and the customized unit driving condition was constructed by the operating condition generation model according to various characteristic parameters. Using the aforementioned method as the training sample source, a driving condition recognition model was established based on a backpropagation neural network. The results show that the constructed driving conditions exhibit an average error of only 2.69% compared to the feature parameters of original sample data. Customized unit conditions show an average error of merely 1.85% relative to the corresponding category sample data feature parameters. The neural network driving condition recognition model achieves an overall accuracy of 93.8%.