The neural network energy management strategy can be implemented online and effectively save the energy for the plug-in hybrid electric vehicles (PHEVs). However, the selection of input features and application to untrained driving cycles are two issues faced by the strategy. This paper proposes an energy management strategy with the optimal input features for the PHEV. The global optimal datasets are first obtained based on the dynamic programming (DP) algorithm. Then the random forest classification models are trained with different combinations of input features to select the input feature combination with the highest classification accuracy. A neural network with the selected input features is finally trained using the optimal datasets for online control. With the optimal input features, the strategy can be adopted to both trained driving cycles and untrained driving cycles. Results demonstrate that the proposed strategy can save 4.44% to 7.75% energy compared to the charge depleting and charge sustaining strategy on trained cycles and 3.80% to 7.70% on untrained cycles respectively. The efficiency of the proposed strategy is only less than 2.41% worse than the DP algorithm.

神经网络能量管理策略可以在线实施，有效地为插电式混合动力汽车（PHEV）节省能源。然而，输入特征的选择和在未经训练的驾驶循环中的应用是该策略面临的两个问题。本文提出了一种具有最佳输入特性的 PHEV 能源管理策略。首先基于动态规划（DP）算法获得全局最优数据集。然后用不同的输入特征组合训练随机森林分类模型，以选择分类精度最高的输入特征组合。最终使用最佳数据集训练具有选定输入特征的神经网络以进行在线控制。通过最佳输入特征，该策略可以适用于经过训练的驾驶循环和未经训练的驾驶循环。结果表明，与训练循环上的电荷消耗和电荷维持策略相比，所提出的策略可以节省 4.44% 至 7.75% 的能量，在未训练的循环上可以分别节省 3.80% 至 7.70% 的能量。所提出策略的效率仅比DP算法差不到2.41%。