【作者】 王鹏；

【导师】 耿聪；

【作者基本信息】 北京交通大学 ， 动力机械及工程， 2022， 硕士

【摘要】 插电式混合动力汽车是我国新能源汽车产业发展的重要方向之一,能量管理策略作为其核心技术之一,对提升能量利用效率及减低排放具有重要意义。本文以一款搭载双离合多挡混合动力专用变速箱(Dedicated Hybrid Transmission,DHT)的插电式混合动力乘用车为研究对象,进行能量管理策略研究。针对纯电动模式和并联驱动模式分别开发了基于离线优化结果的实时能量管理策略。研究工作对插电式混合动力汽车技术发展和整车性能提升有一定的学术价值和工程应用意义。基于AMESim软件建立了双离合多挡DHT混合动力乘用车整车及动力系统关键零部件模型,包括驾驶员模型、电机模型、动力电池模型、发动机模型、传动系统模型以及车辆动力学模型。在Matlab/Simulink中搭建控制策略模型,通过仿真接口实现二者联合仿真,并根据实车试验数据和仿真数据对比分析,校验了仿真平台的准确性,为能量管理策略开发奠定基础。开展了纯电动模式下的能量管理策略研究。结合双电机转矩特性,以电机功率消耗最低为目标,计算复合外特性范围内的最佳挡位分布和最优转矩分配。分析部分边界和交叉区域的工作点能耗差别,以次优解代替最优解以满足车辆平顺性和安全性要求,并通过仿真分析说明其节能效果。开展了并联驱动模式下的能量管理策略研究。首先,基于电量消耗-电量维持(Charge Depleting-Charge Sustaining,CD-CS)规则设计了并联驱动模式下的模式选择和转矩分配策略,在此基础上以动力系统综合效率最优为目标制定了混合动力汽车的经济性换挡规律。其次,建立了能量管理策略动态规划算法模型,通过限制转矩分配系数变化率实现对发动机变载幅度的控制,构建了包含发动机油耗和换挡动作惩罚项的目标函数,运用动态规划算法获得已知工况下的全局最优解。在不同测试工况下的仿真结果表明,采用动态规划算法相比于CD-CS策略,百公里节油率分别为21.15%和23.47%。开展了基于全局优化结果的实时能量管理策略研究。利用动态规划优化结果,建立神经网络算法模型解析车辆状态参数与最优控制变量之间的非线性函数关系。将优化结果作为神经网络的训练样本,分别设计了基于神经网络算法的转矩分配策略和挡位选择策略,在Matlab/Simulink中搭建实时能量管理策略模型进行仿真,仿真结果与动态规划优化结果接近,达到了基于离线优化结果的实时能量管理策略的开发目标。图60幅,表16个,参考文献95篇。更多还原

【Abstract】 The plug-in hybrid electric vehicles are one of the important directions in the development of China’s new energy vehicle industry.As one of its core technologies,the energy management strategy is of great significance to improve energy efficiency and reduce emissions.This paper takes a plug-in hybrid passenger vehicle with dual-clutch and multi-gear DHT as the research object to study the energy management strategy.A real-time energy management strategy based on offline optimization results is developed for pure electric mode and parallel drive mode.The research has certain academic value and engineering application significance for the development of plug-in hybrid vehicle technology and the improvement of vehicle performance.Based on AMESim,the model of the DHT hybrid passenger vehicle and key parts of its power system are established,including the driver model,motor model,power battery model,engine model,transmission model,and vehicle dynamics model.The control strategy model is built in Matlab / Simulink,and the joint simulation is realized through the simulation interface,The accuracy of the simulation platform is verified according to the comparative analysis of real vehicle test data and simulation data,which lays a foundation for the development of energy management strategy.Research on energy management strategy in pure electric mode is carried out.Combined with the torque characteristics of dual motors,the optimal gear distribution and torque distribution within the range of composite external characteristics are calculated with the goal of minimum motor power consumption.The energy consumption difference of working points in some boundary and crossover regions was analyzed,replacing the optimal solution with the suboptimal solution to meet the requirements of vehicle ride comfort and safety,and illustrate its energy-saving effect through simulation analysis.The research on energy management strategy in parallel drive mode is carried out.Firstly,the mode selection and torque distribution strategy under parallel drive mode is designed based on CD-CS rules.On this basis,the economic shift law of hybrid electric vehicles is formulated with the goal of optimal comprehensive efficiency of the power system.Secondly,the dynamic programming algorithm model of energy management strategy is established,and the range of engine load change is controlled by limiting the change rate of torque distribution coefficient.The objective function including engine fuel consumption and shift action penalty is constructed,and the global optimal solution under known conditions was obtained by using dynamic programming algorithm.The simulation results under different conditions show that the 100 km fuel savings are21.15% and 23.47% using the dynamic planning algorithm compared to the CD-CS strategy,respectively.The research on a real-time energy management strategy based on global optimization results is carried out.Using the optimization results of the dynamic programming algorithm,a neural network algorithm model is established to analyze the nonlinear functional relationship between the vehicle state parameters and the optimal control variables.Use the optimization result as a training sample for the neural network and the torque allocation strategy and gear selection strategy based on the neural network algorithm were designed respectively,The simulation model of real-time energy management strategy is built in Simulink for simulation.The simulation results are close to the dynamic programming optimization results,and the development goal of a real-time energy management strategy based on offline optimization results is achieved.There are 60 pictures,16 tables and 95 references.更多还原