基于深度强化学习的混合动力汽车智能跟车控制与能量管理策略研究

以研究智能混合动力汽车控制技术与深度强化学习算法为目标，首先，在两辆混合动力汽车的跟驰环境中，针对领航车提出一种基于深度值网络算法的能量管理策略，实现深度强化学习对发动机与机械式无级变速器的多目标协同控制；其次，针对跟随车建立基于深度强化学习的分层控制模型，实现面向智能混合动力汽车的上层跟车控制与下层能量管理；最后，仿真验证分层控制模型的有效性。结果表明，基于深度强化学习的跟车控制策略具有理想的跟踪性能；同时，基于深度强化学习的能量管理策略在领航车与跟随车中均实现了较好的燃油经济性；此外，基于深度强化学习的能量管理策略输出每组控制动作的平均时间为1.66 ms，保证了实时应用的潜力。

Research on Deep Reinforcement Learning-based Intelligent Car-following Control and Energy Management Strategy for Hybrid Electric Vehicles

Limited by the cost, high-speed on-off valves are often used in the electro-hydraulic braking system. However, the knocking noise of the solenoid valve, hydraulic impact noise and pressure fluctuation in the switch control of the hydraulic valve lead to the quality and precision of brake control to deteriorate. Therefore, the key to the design of the high-speed on-off valve is to realize the linearization by pulse width modulation (PWM) control. The dynamic characteristics of high-speed on-off valve are affected by transient flow force, nonlinear electromagnetic force, mechanical inertia and spring force. Therefore, the dynamic model and joint simulation model of high-speed on-off valve and hydraulic control unit (HCU) are established. Through simulation and experimental verification, the influence of structural parameters such as poppet angle, orifice diameter, air gap size on the linear characteristics of solenoid valve is analyzed. The reasonable structure parameters of the solenoid valve are designed and applied to a hydraulic control circuit to widen the linear working range and meet the requirements of automobile braking safety control.