为降低多模式混合动力汽车的动态油耗，设计了在线工况分类的动态协调控制策略，采用改进粒子群优化K均值聚类（K-means）算法进行工况分类，将行驶工况分为平缓工况和突变工况，并实现在线工况的识别；针对平缓工况，建立一阶马尔科夫链模型对车速进行预测，实时优化扭矩的分配；针对突变工况，设计了动态协调控制程序，通过调节节气门开度变化率，减少动态油耗，改善模式切换过程中的冲击。仿真结果表明：采用工况分类的动态协调控制策略有效减小了发动机扭矩和车速的波动，提高了行驶平顺性，综合油耗下降10.88%，电池荷电状态能够维持在效率较高的区域。

In order to reduce the dynamic fuel consumption of multi-mode hybrid vehicles, the dynamic coordination control strategy of online operating condition classification was designed. The driving conditions were divided into smooth and aggressive conditions by the optimized K-means clustering(K-means) algorithm with improved particle swarm optimization and then the online operating condition identificaiton was realized. For the smooth conditions, the first-order Markov chain model was established to predict the vehicle speed and optimize the distribution of torque in real time. For the aggressive conditions, the dynamic coordination control program was designed to reduce the dynamic fuel consumption and improve the step change in the process of mode switching by adjusting the change rate of throttle opening. The simulation results show that the dynamic coordination control strategy of operating condition classification effectively reduces the fluctuation of engine torque and vehicle speed and improves the smoothness of driving with the overall fuel consumption decreased by 10.88% and the battery load state well maintained in the high efficiency area.