This paper proposes a bi-level energy management strategy of plug-in hybrid electric vehicles with intelligent state-of-charge (SOC) reference for satisfactory fuel economy and battery lifetime. In the upper layer, Q-learning algorithm is delegated to generate the SOC reference before departure, by taking the model nonlinearities and physical constraints into account while paying less computing labor. In the lower layer, with the short-term drive velocity accurately predicted by the radial basis function neural network, the model predictive control (MPC) controller is designed to online distribute the system power flows and track the SOC reference for the superior fuel economy and battery lifetime extension. Moreover, the terminal SOC constraints are transferred as soft ones by the relaxation operations to guarantee the solving feasibility and smooth tracking effects. Finally, the simulations are carried out to validate the effectiveness of the proposed strategy, which shows the considerable improvements in fuel economy and battery lifetime extension compared with the charge-depleting and charge-sustaining method. More importantly, the great robustness of the proposed approach is verified under the cases of inaccurately pre-known drive information, indicating the favorable adaptability for practical application.

本文提出了具有智能充电状态（SOC）参考的插电式混合动力汽车的双层能源管理策略，以实现令人满意的燃油经济性和电池寿命。在上层，通过考虑模型的非线性和物理约束，同时减少了计算工作量，委托Q学习算法在出发前生成SOC参考。在较低的层中，通过径向基函数神经网络准确预测短期驱动速度，模型预测控制（MPC）控制器设计为在线分配系统功率流并跟踪SOC参考，以实现卓越的燃油经济性和延长电池寿命。此外，终端SOC约束通过松弛操作作为软约束传递，以保证求解的可行性和平滑的跟踪效果。最后，进行了仿真以验证所提出策略的有效性，与耗电量和维持电量的方法相比，该策略显示出燃油经济性和电池寿命延长方面的显着改善。更重要的是，在已知驱动信息不正确的情况下，验证了该方法的强大鲁棒性，表明了其在实际应用中的良好适应性。与耗电量和维持电量的方法相比，这表明燃油经济性和电池寿命的延长有了显着改善。更重要的是，在已知驱动信息不正确的情况下，验证了该方法的强大鲁棒性，表明了其在实际应用中的良好适应性。与耗电量和维持电量的方法相比，这表明燃油经济性和电池寿命的延长有了显着改善。更重要的是，在已知驱动信息不正确的情况下，验证了该方法的强大鲁棒性，表明了其在实际应用中的良好适应性。