双行星排混合动力汽车凭借发动机与车速完全解耦的优势，在燃油效率和排放方面表现突出，但其复杂结构导致多模运行时动力源协调困难。基于等效树图法设计一种双行星排混合动力系统构型，构建AVL CRUISE与Simulink整车联合仿真模型，提出动态混沌正余弦多目标粒子群优化算法(dynamic chaos sine-cosine multi-objective particle swarm optimization, DCSC-MOPSO)，以动力性、经济性和平顺性为目标进行多目标优化。仿真结果表明，DCSC-MOPSO算法在Pareto解集搜索和目标平衡方面具有显著优势，动力性、经济性和平顺性相较于初始方案分别平均提升了39.79%，17.77%和25.24%，显著提升了系统综合性能。

Dual-planetary hybrid electric vehicles excel in fuel efficiency and emissions due to the complete decoupling of engine from vehicle speed. However, the complex structures pose challenges in coordinating power sources during multi-mode operation. A dual-planetary gear hybrid system was designed based on the equivalent tree graph method and a co-simulation model was established by combining AVL CRUISE and Simulink. A dynamic chaos sine-cosine multi-objective particle swarm optimization (DCSC-MOPSO) algorithm was proposed to perform multi-objective optimization targeting power, fuel economy, and smoothness. The simulation results demonstrate that the DCSC-MOPSO algorithm exhibits significant advantages in Pareto solution set search and objective balancing. Compared to the initial solution, the power, fuel economy, and smoothness improve by an average of 39.79%, 17.77%, and 25.24%, respectively, indicating a substantial enhancement in the overall system performance.