考虑坡度工况的FCHEV队列分层优化控制策略研究

Hierarchical optimal control strategy for FCHEV queue considering gradient operation condition

在面对坡度工况时,如何开发同时兼顾车辆间协同控制与能耗经济性的控制策略是提高交通效率与发挥车辆节能潜力的关键技术之一。以燃料电池混合动力汽车队列为研究对象,以安全行驶及优化能耗为目标,提出了一种基于改进粒子群优化算法与Q学习的燃料电池混合动力汽车队列分层优化控制策略。该策略中上层控制器在保证满足与前车距离或速度限制等安全约束的前提下,利用改进的粒子群优化算法获取节能速度轨迹,并使用模型预测控制框架实时调整主车速度遵循节能速度轨迹行驶。下层控制器根据上层求解的车速和需求功率等信息建立Q学习控制器,实现燃料电池混合动力汽车动力电池与燃料电池之间的最优能量分配。仿真结果表明,本文所提出的分层控制策略在坡度工况下,表现出良好的跟踪性能和安全性能,且优化结果与动态规划策略相似,表明该策略的能耗经济性及可行性。

In the face of gradient operation conditions,the development of control strategies that simultaneously take into account inter-vehicle cooperative control and energy economy is one of the key technologies for improving traffic efficiency and exploiting the energy-saving potential of vehicles.A hierarchical optimization control strategy based on improved particle swarm optimization algorithm and Q-learning for fuel cell hybrid electric vehicles queue is proposed with the objective of safe driving and optimizing energy consumption.In this strategy,the upper layer controller uses the improved particle swarm optimization algorithm to obtain the energy-saving speed trajectory under the premise of ensuring that safety constraints such as distance or speed limit from the preceding vehicle are satisfied,and utilizes the model predictive control framework to adjust the vehicle speed in real time to ensure the vehicle follows the energy-saving speed trajectory.The lower layer controller builds the Q-learning controller based on the information such as vehicle speed and demand power solved by the upper layer to realize the optimal energy distribution between the fuel cell hybrid electric vehicles power cell and the fuel cell.Simulation results show that the hierarchical control strategy proposed in this paper exhibits good tracking performance and safety performance under slope conditions,and the optimization results are similar to the dynamic planning strategy,indicating the energy consumption economy and feasibility of the strategy.