基于速度预测与自适应差分进化算法的混合动力汽车能量管理策略

为提高单行星排构型的混合动力汽车（hybrid electric vehicle, HEV）的燃油经济性，降低车辆燃油消耗量，提出了一种基于门控循环单元神经网络（gated recurrent unit neural network, GRU-NN）速度预测模型与自适应差分进化（adaptive differential evolution, A-DE）算法的能量管理策略，在模型预测控制（model predictive control, MPC）框架下预测未来车辆的行车速度，将整个工况内的全局优化求解问题转化为在预测时域内的局部优化求解，以发动机燃油消耗量最低与行车过程电池荷电状态（state of charge, SOC）平衡为目标，利用A-DE算法实现预测域内的最优控制序列求解。仿真结果表明，在实车采集的道路工况下，基于GRU-NN与A-DE算法的能量管理策略相较于ECMS燃油消耗量减少了4.55%，相较于动态规划燃油经济性达到了93.04%。

Energy management strategy for hybrid electric vehicles based on speed prediction and adaptive differential evolution algorithm

In order to improve the fuel economy of hybrid electric vehicle (HEV) with single row planetary gear and reduce the fuel consumption of the HEV, an energy management strategy based on gated recurrent unit neural network (GRU-NN) speed predictive model and adaptive differential evolution (A-DE) algorithm is proposed. The future speed of HEV is predicted under the framework of model predictive control (MPC). The energy management strategy converts the global optimization solution problem in the entire working condition into a local optimization solution in the prediction time domain. Aiming at the lowest fuel consumption of the engine and the balance of battery state of charge (SOC) during driving, the optimal control sequence in the prediction domain is solved by A-DE. The simulation results show that the energy management strategy based on the GRU-NN and A-DE reduces fuel consumption by 4.55% compared with that of ECMS, and the fuel economy reaches 93.04 % compared with that of dynamic programming (DP) under the driving cycle collected by vehicle.