针对庞特里亚金极小值原理(Pontryagin's minimum principle, PMP)仅适用于离线计算且难以实车应用的问题,提出了一种基于麻雀搜索优化密度聚类(density-based spatial clustering of applications with noise, DBSCAN)的工况在线...针对庞特里亚金极小值原理(Pontryagin's minimum principle, PMP)仅适用于离线计算且难以实车应用的问题,提出了一种基于麻雀搜索优化密度聚类(density-based spatial clustering of applications with noise, DBSCAN)的工况在线识别能量管理策略。该策略结合离线训练与在线控制,并充分利用公交车线路固定性与片段性特征,以公交站为节点,将线路划分为多个驾驶片段。在车辆靠站期间,对上一个驾驶片段的电机输出功率进行工况识别,从而计算下一个驾驶片段的协态变量(co-state);在车辆开始运行时,将计算好的co-state应用于PMP算法中完成功率的实时分配。最后,通过构建基于实车数据的仿真实验,将所提出的策略移植到整车控制器中。结果显示,与当前实车运行的规则能量管理策略相比,该策略可减少等效氢耗17.6%,并能有效维持动力电池荷电状态(state of charge, SOC)。且每步计算均在60 ms以内,具有良好的实时性,能够满足燃料电池公交车实际运行中对能量管理策略的应用要求。展开更多
The utilization of traffic information received from intelligent vehicle highway systems(IVHS) to plan velocity and split output power for multi-source vehicles is currently a research hotspot. However, it is an open ...The utilization of traffic information received from intelligent vehicle highway systems(IVHS) to plan velocity and split output power for multi-source vehicles is currently a research hotspot. However, it is an open issue to plan vehicle velocity and distribute output power between different supply units simultaneously due to the strongly coupling characteristic of the velocity planning and the power distribution. To address this issue, a flexible predictive power-split control strategy based on IVHS is proposed for electric vehicles(EVs) equipped with battery-supercapacitor system(BSS). Unlike hierarchical strategies to plan vehicle velocity and distribute output power separately, a monolayer model predictive control(MPC) method is employed to optimize them online at the same time. Firstly, a flexible velocity planning strategy is designed based on the signal phase and time(SPAT) information received from IVHS and then the Pontryagin’s minimum principle(PMP) is adopted to formulate the optimal control problem of the BSS. Then, the flexible velocity planning strategy and the optimal control problem of BSS are embedded into an MPC framework, which is online solved using the shooting method in a fashion of receding horizon. Simulation results verify that the proposed strategy achieves a superior performance compared with the hierarchical strategy in terms of transportation efficiency, battery capacity loss, energy consumption and computation time.展开更多
文摘针对庞特里亚金极小值原理(Pontryagin's minimum principle, PMP)仅适用于离线计算且难以实车应用的问题,提出了一种基于麻雀搜索优化密度聚类(density-based spatial clustering of applications with noise, DBSCAN)的工况在线识别能量管理策略。该策略结合离线训练与在线控制,并充分利用公交车线路固定性与片段性特征,以公交站为节点,将线路划分为多个驾驶片段。在车辆靠站期间,对上一个驾驶片段的电机输出功率进行工况识别,从而计算下一个驾驶片段的协态变量(co-state);在车辆开始运行时,将计算好的co-state应用于PMP算法中完成功率的实时分配。最后,通过构建基于实车数据的仿真实验,将所提出的策略移植到整车控制器中。结果显示,与当前实车运行的规则能量管理策略相比,该策略可减少等效氢耗17.6%,并能有效维持动力电池荷电状态(state of charge, SOC)。且每步计算均在60 ms以内,具有良好的实时性,能够满足燃料电池公交车实际运行中对能量管理策略的应用要求。
基金supported by the National Natural Science Foundation of China (62173303)the Fundamental Research for the Zhejiang P rovincial Universities (RF-C2020003)。
文摘The utilization of traffic information received from intelligent vehicle highway systems(IVHS) to plan velocity and split output power for multi-source vehicles is currently a research hotspot. However, it is an open issue to plan vehicle velocity and distribute output power between different supply units simultaneously due to the strongly coupling characteristic of the velocity planning and the power distribution. To address this issue, a flexible predictive power-split control strategy based on IVHS is proposed for electric vehicles(EVs) equipped with battery-supercapacitor system(BSS). Unlike hierarchical strategies to plan vehicle velocity and distribute output power separately, a monolayer model predictive control(MPC) method is employed to optimize them online at the same time. Firstly, a flexible velocity planning strategy is designed based on the signal phase and time(SPAT) information received from IVHS and then the Pontryagin’s minimum principle(PMP) is adopted to formulate the optimal control problem of the BSS. Then, the flexible velocity planning strategy and the optimal control problem of BSS are embedded into an MPC framework, which is online solved using the shooting method in a fashion of receding horizon. Simulation results verify that the proposed strategy achieves a superior performance compared with the hierarchical strategy in terms of transportation efficiency, battery capacity loss, energy consumption and computation time.
文摘为解决燃料电池混合动力公交车中基于优化的能量管理策略难以实车应用的问题,在分析燃料电池公交车(Fuel cell hybrid bus,FCHB)行驶路线的固定性和片段性的基础上,提出了一种基于SOM-K-means(Self-organized mapping K-means)工况识别的能量管理策略。首先,根据公交车站点将行驶路线划分为多个行驶片段,在车辆停站时,运用SOM-K-means二阶聚类模型完成工况识别,获取车辆下一行驶片段的识别协态变量;当车辆在下一个行驶片段运行时,运用识别协态变量完成基于庞特里亚金极值原理(Pontryagin s maximum principle,PMP)求解的能量管理策略的实时应用。其次,建立基于公交车实际运行数据的仿真实验,最后建立硬件在环实验,将所提出的策略移植入整车控制器(Vehicle control unit,VCU)中进行实验。实验结果表明,与基于规则的能量管理策略相比,本研究提出的能量管理策略降低了19.77%的平均等效氢气消耗。且该策略在VCU中每一步的计算时间大约为30 ms,计算结果与仿真结果完全一致,满足车辆对能量管理策略的时效性和准确性的要求。
