智能网联环境下的PHEV实时优化能量管理策略法

Research on real-time optimization energy management strategy of PHEV under intelligent networking environment

为了进一步挖掘插电混合动力汽车(PHEV)的节油潜力,借助智能网联环境提供的通勤车辆历史运行数据,提出了一种PHEV实时优化能量管理策略。该策略由离线与在线2个部分组成。离线部分对通勤车辆历史运行车速等数据进行分析处理,获得能够反映该通勤路线特征的循环工况曲线,进而采用Pontryagin最小值原理(PMP)算法构建PHEV燃油经济性最优控制问题,其中协态变量的求解采用打靶法实现以得到该特征工况下的PHEV燃油经济性全局最优。在线部分结合离线特征工况下的协态变量优化结果,针对PHEV实际运行工况进行协态变量的实时修正,实现了最优控制律的在线应用。结果表明:该策略适用于不同运行工况,可实现PHEV燃油经济性全局最优解与瞬时最优解的高效融合;在随机工况下与等效油耗最小策略(ECMS)相比,燃油经济性提升了18.64%,发动机的工作区域得到明显改善。该策略为PHEV能量管理实时优化问题的解决提供了新的思路。

A real-time optimization energy management strategy for plug-in hybrid electric vehicles(PHEVs)was proposed to further explore the fuel-saving potential of PHEVs based on the historical operation data of commuter vehicles provided by the intelligent networking environment.This strategy consists of offline part and online part.The data such as the historical operating speed of commuter vehicles was analyzed and processed in the offline part,obtaining a cycle operating curve that can reflect the characteristics of the commuter route,and then the Pontryagin’s minimum principle(PMP)algorithm was used to construct the optimal control problem of the PHEVs in fuel economy,in which the solution of the co-state was realized by the shooting method,so as to obtain the global optimal solution of PHEVs in fuel economy under this typical driving cycle.The online part combines the optimization results of the co-state variables under the offline typical driving cycle to correct the co-state variables in real time according to the actual operating conditions of the PHEVs;and the online application of the optimal control law was realized.The results show that the strategy is suitable for different operating conditions,and can realize the efficient integration of the global optimal solution and the instantaneous optimal solution in fuel economy of PHEVs;Compared with the equivalent consumption minimum strategy(ECMS)under random driving cycles,and the fuel economy has increased by 18.64%,the working area of the engine has been significantly improved.Therefore this strategy provides a new idea for solving the real-time optimization problem of PHEVs energy management.