针对一种采用双行星排结构的功率分流式混合动力汽车,采用杠杆法对动力耦合机构进行动力学行为分析,得到电动机转速、转矩和功率与发动机的比值关系.在此基础上,求取整车传动系统的传动效率和电功率比,同时对功率分流和功率循环现象进...针对一种采用双行星排结构的功率分流式混合动力汽车,采用杠杆法对动力耦合机构进行动力学行为分析,得到电动机转速、转矩和功率与发动机的比值关系.在此基础上,求取整车传动系统的传动效率和电功率比,同时对功率分流和功率循环现象进行分析,进一步制定了机械点(MP)控制策略,通过优选合适的切换阈值K,避免功率循环,并保证了整车传动高效率.基于AVL/Cruise平台搭建了整车动力学仿真模型,同时在MATLAB/Simulink中搭建整车能量管理策略,并进行联合仿真分析.结果表明:相比于最优工作曲线(OOL)控制策略,基于传动效率最优的机械点控制策略可以保证整车传动系统的高效率,电池荷电状态(state of charge,SOC)处于较好的范围,其等效100 km油耗下降了8. 5%,有效提高了燃油经济性.展开更多
本文针对偏好信息由中智集(NS)表示的多属性群决策问题(MAGDM)进行研究,将静态决策环境下的中智集扩展为动态决策环境下的非线性中智集,并开发了相应的投影模型和集结算法。首先,本文给出了非线性中智集的定义及运算法则。然后,将非线...本文针对偏好信息由中智集(NS)表示的多属性群决策问题(MAGDM)进行研究,将静态决策环境下的中智集扩展为动态决策环境下的非线性中智集,并开发了相应的投影模型和集结算法。首先,本文给出了非线性中智集的定义及运算法则。然后,将非线性中智数投影为三维空间中的曲线,用曲线之间所围成曲面的面积大小来描述决策者偏好之间的差异,从而完成非线性中智集空间投影模型的建立。最后,开发基于模拟植物生长算法(PGSA)的空间曲线集结算法,通过寻找与所有偏好曲线围成曲面面积之和最小的最优集结曲线来完成非线性中智集的集结,并结合TOPSIS算法完成多属性群决策问题中的方案排序工作。文章的实验部分通过一个具体案例来说明本文所提出方法的有效性。This paper investigates the problem of multi-attribute group decision making (MAGDM) where preference information is represented by a neutrosophic set (NS). It extends the concept of neutral set from static decision environments to nonlinear neutrosophic set in dynamic decision environments, and develops a corresponding projection model and aggregation algorithm. Firstly, we provide the definition and algorithm for nonlinear neutrosophic sets. Then, we project the nonlinear neutral set onto a curve in three-dimensional space, describing differences in decision makers’ preferences through the surface area between curves. This allows us to establish a projection model for the space of nonlinear neutral sets. Finally, we develop a space curve aggregation algorithm based on the plant growth simulation algorithm (PGSA). By identifying an optimal aggregation curve with minimal sum of surface areas between all preference curves, we assemble the nonlinear neutral set and combine it with TOPSIS algorithm to sort schemes in multi-attribute group decision making problems. The experimental section demonstrates the effectiveness of our proposed method through a specific case.展开更多
文摘针对一种采用双行星排结构的功率分流式混合动力汽车,采用杠杆法对动力耦合机构进行动力学行为分析,得到电动机转速、转矩和功率与发动机的比值关系.在此基础上,求取整车传动系统的传动效率和电功率比,同时对功率分流和功率循环现象进行分析,进一步制定了机械点(MP)控制策略,通过优选合适的切换阈值K,避免功率循环,并保证了整车传动高效率.基于AVL/Cruise平台搭建了整车动力学仿真模型,同时在MATLAB/Simulink中搭建整车能量管理策略,并进行联合仿真分析.结果表明:相比于最优工作曲线(OOL)控制策略,基于传动效率最优的机械点控制策略可以保证整车传动系统的高效率,电池荷电状态(state of charge,SOC)处于较好的范围,其等效100 km油耗下降了8. 5%,有效提高了燃油经济性.
文摘本文针对偏好信息由中智集(NS)表示的多属性群决策问题(MAGDM)进行研究,将静态决策环境下的中智集扩展为动态决策环境下的非线性中智集,并开发了相应的投影模型和集结算法。首先,本文给出了非线性中智集的定义及运算法则。然后,将非线性中智数投影为三维空间中的曲线,用曲线之间所围成曲面的面积大小来描述决策者偏好之间的差异,从而完成非线性中智集空间投影模型的建立。最后,开发基于模拟植物生长算法(PGSA)的空间曲线集结算法,通过寻找与所有偏好曲线围成曲面面积之和最小的最优集结曲线来完成非线性中智集的集结,并结合TOPSIS算法完成多属性群决策问题中的方案排序工作。文章的实验部分通过一个具体案例来说明本文所提出方法的有效性。This paper investigates the problem of multi-attribute group decision making (MAGDM) where preference information is represented by a neutrosophic set (NS). It extends the concept of neutral set from static decision environments to nonlinear neutrosophic set in dynamic decision environments, and develops a corresponding projection model and aggregation algorithm. Firstly, we provide the definition and algorithm for nonlinear neutrosophic sets. Then, we project the nonlinear neutral set onto a curve in three-dimensional space, describing differences in decision makers’ preferences through the surface area between curves. This allows us to establish a projection model for the space of nonlinear neutral sets. Finally, we develop a space curve aggregation algorithm based on the plant growth simulation algorithm (PGSA). By identifying an optimal aggregation curve with minimal sum of surface areas between all preference curves, we assemble the nonlinear neutral set and combine it with TOPSIS algorithm to sort schemes in multi-attribute group decision making problems. The experimental section demonstrates the effectiveness of our proposed method through a specific case.
