借鉴随机动态规划的思想，将新欧洲标准行驶循环NEDC下的驾驶员需求功率抽象为随车速变化的随机过程。根据需求功率的转移概率矩阵，将整车的能量管理策略求解问题简化为对发动机输出扭矩的优化问题，并利用离散粒子群算法对挡位选择进行优化。在维持动力电池组荷电状态（SOC）平衡和挡位效率最佳的约束下，以等效燃油消耗量最小为目标，通过策略迭代法得出当前发动机的最优输出扭矩。利用Matlab建模仿真，试验表明，与基于规则的能量管理策略相比，提出的控制策略在满足标准循环工况NEDC车速要求的前提下，燃油消耗量降低了9.42%。

Referring to the idea of stochastic dynamic programming (SDP), the required drive power in the new European driving cycle(NEDC) was abstracted as a stochastic process that changed with the vehicle velocity. According to the transition probability matrix of required drive power, the issue of energy management strategy was simplified as the optimization problem of engine torque and the discrete particle swarm optimization (DPSO) was applied to optimize the gear selection. Based on the state balance of charge and the optimum of gear efficiency, the optimal engine torque was obtained through the strategy iteration method to achieve the minimal fuel consumption. The simulation was carried out with Matlab. The test results show that the proposed strategy can reduce fuel consumption by 9.42% without the loss of vehicle velocity in the NEDC cycle in comparison with that of the rulebased energy management strategy.