混合动力汽车能量管理策略（EMS）优化问题是一类需要综合优化混合动力汽车多个性能指标的多目标多阶段决策问题，而传统的多目标优化算法在求解EMS这类问题时面临求解效率低、收敛性难以保证等挑战。本文结合非支配排序算法的思想，将传统的动态规划法（DP）拓展到多目标优化领域，提出了非支配排序动态规划法（NSDP）。该算法首先将行驶工况划分为多个阶段，在每个阶段中求取混合动力汽车在不同控制策略产生的累积目标值向量，并通过非支配排序算法获得当前的非支配解集以及对应的控制策略，然后利用各个阶段的非支配解集依次逆向迭代，直至获取整个行驶工况的非支配解集前沿以及对应的能量管理控制策略。在仿真实验中，分别应用加权动态规划法（WDP）和非支配排序动态规划法求解功率分流式混合动力汽车和串并联式混合动力汽车在匀加速工况的多目标能量管理策略优化问题，结果表明NSDP能够有效完成求解并保证收敛性，且求解结果在解集均匀性和求解效率方面具有显著的优势。进一步，运用NSDP求解在世界轻型车辆测试工况（WLTC）下串并联式混合动力汽车能量管理优化问题，所得非支配解集可用于分析汽车的工作特性，并能够为实际能量管理策略的制定提供可靠的参考。

Hybrid electric vehicle Energy Management Strategy (EMS) optimization is a multi-objective and multi-stage decision-making problem that needs to comprehensively optimize several performance indicators of hybrid electric vehicles. The traditional multi-objective optimization algorithm faces challenges such as low efficiency and difficult to guarantee convergence when dealing with these problems. Combined with the idea of non-dominated sorting algorithm, this paper extended the traditional Dynamic Programming (DP) to the field of multi-objective optimization, and proposed Non-dominated Sorting Dynamic Programming (NSDP). When using this algorithm, the driving condition was divided into several stages firstly. In each stage, the cumulative target value vector generated by the hybrid electric vehicle in different control strategies was obtained, and the current non dominated solution set and the corresponding control strategy were obtained through the non dominated sorting algorithm. Then, the non dominated solution set of each stage was used for reverse iteration in turn, until the leading edge of the non dominated solution set and the corresponding energy management control strategy of the whole driving cycle were obtained. In the simulation experiment, Weighting Dynamic Programming (WDP) and Non-dominated Sorting Dynamic Programming were applied to solve the optimization problem of multi-objective energy management strategy for power split hybrid electric vehicles and series parallel hybrid electric vehicles under constant acceleration conditions. The results show that NSDP not only can effectively complete the solution and ensure convergence, but also has significant advantages in homogeneity of solution set and solving efficiency. Furthermore, NSDP was used to solve the energy management optimization problem of series parallel hybrid electric vehicles running in Worldwide Harmonized Light Duty Vehicle Test Cycle (WLTC). The non dominated solution set can be used to analyze the working characteristics of vehicles and provides a reliable reference for the formulation of actual energy management strategy.