混合动力汽车（hybrid electrical vehicle,HEV）的能量管理策略直接决定了车辆的燃油经济性、驾驶性能和寿命，为解决HEV能量管理策略的最优性与实时行驶工况不确定性之间的矛盾，以混联式HEV为研究对象，提出一种基于模型预测控制（model predictive control,MPC）与蜣螂优化算法(dung beetle optimizer,DBO)的HEV能量管理策略。首先，该策略采用基于堆叠式长短时记忆神经网络（stacked longshort term memory neural network,Stacked LSTMNN）的车速预测模型预测未来行驶车速。其次，根据预测车速将混合动力汽车的功率分配问题描述为MPC预测范围内的滚动优化问题，提出考虑燃料消耗和电池保护的成本函数，利用DBO算法对预测时域内发动机功率进行优化求解。最后，在城市道路循环(urban dynamometer driving schedule,UDDS)工况下分别对所提策略的车速预测精度和经济性与其他策略进行仿真对比验证。结果表明：与传统LSTM速度预测模型相比，Stacked LSTM速度预测模型的RMSE降低了13.9%，每步平均预测时间减少1 ms；与基于规则的策略相比，基于DBO-MPC的策略模型节油率达到25.3%，同时SOC状态波动更为平稳，对电池的保护效果更好。

The energy management strategy of hybrid electric vehicle(HEV) directly determines the fuel economy, driving performance and life of vehicle. In order to solve the contradiction between the optimal energy management strategy of HEV and the uncertainty of real-time driving conditions, the energy management strategy of HEV based on model predictive control(MPC) and Dung beetle optimizer(DBO) was proposed based on the research object of hybrid HEV. First, the strategy used the vehicle speed prediction model to predict future driving speed based on stacked long-short term memory neural network(Stacked LSTM-NN). Then, according to the predicted vehicle speed, the power distribution problem of HEV was described as a rolling optimization problem within the MPC prediction range. Considering the cost function of fuel consumption and battery protection, the DBO algorithm was used to optimize the engine power in the forecast time domain. Finally, under urban dynamometer driving schedule(UDDS) conditions, the speed prediction accuracy and fuel economy of proposed strategy were simulated and compared with other strategies. Compared with the traditional LSTM speed prediction model, the RMSE of Stacked LSTM speed prediction model reduces by 13.9%, and the average prediction time of each step reduces by 1 ms. Compared with the rule-based strategy, the fuel saving rate of DBOMPC strategy model reached 25.3%, and the SOC state is more stable and the battery protection effect is better.