【作者】 赵鑫；

【导师】 刘宏伟；

【作者基本信息】 吉林大学 ， 车辆工程， 2019， 硕士

【摘要】 基于粒子群优化算法的增程式电动客车驱动控制策略研究随着传统汽车保有量的不断增加,能源危机和环境污染问题日益严重,因此世界各国均着手发展新能源汽车。纯电动汽车具有高效、清洁和零排放的优点,但同时也存在续航里程短、充电时间长和配套设施不完善等缺点。增程式电动汽车不仅具有纯电动汽车的优点,还能弥补其续驶里程短的不足,可以作为传统燃油汽车向纯电动汽车过渡的理想车型。因此研究增程式电动汽车的驱动控制策略对推动新能源汽车的应用具有重要意义。本文依托于企业合作项目“增程式电动客车系统开发”,以增程式电动客车为研究对象,根据整车设计任务完成了该车动力系统参数匹配、制定了基于理想参考SOC曲线的驱动控制策略、建立了整车及控制策略模型,采用基于模拟退火的粒子群优化算法对控制参数进行了优化。本文的主要研究内容如下:(1)根据市场需求确定整车控制系统架构和性能指标,以此为依据对驱动电机、动力电池和增程器进行选型和参数匹配,并对整车动力性和纯电动续驶里程进行校核。(2)基于整车性能指标和动力系统参数匹配结果,制定整车驱动控制策略。首先选用具有相同工况特性不同里程的四个循环工况,应用全局优化的动态规划算法,获得以行驶成本函数最小为目标的理想参考SOC曲线。(3)基于工况信息和动力电池额定放电功率,确定增程器的最佳工作区间,选择实际SOC与参考SOC的差值和整车需求功率作为判断量,依据理想参考SOC曲线制定发动机两点+功率跟随型驱动控制策略。(4)基于Cruise仿真平台,建立了车辆正向仿真模型,并在MATLAB/Simulink软件中搭建了驱动控制策略模型。通过编译DLL文件实现整车模型和驱动控制策略模型的集成,在Cruise中进行多任务仿真计算,验证了驱动系统参数匹配的合理性和控制策略的有效性。(5)为使整车在满足动力性的条件下达到更好的燃油经济性,因此采用基于模拟退火的粒子群优化算法,以总行驶成本最小为优化函数对控制策略中的阈值进行优化,优化结果表明,在保证动力性和续驶里程前提下整车燃油经济性得到提升,实现了能源的有效利用。(6)基于xPC-Target宿主机-目标机的“双机”模式,搭建了整车控制器硬件在环测试平台。模拟整车的运行环境,检测整车控制器的输入输出性能和控制响应的实时性,试验结果表明,制定的驱动控制策略在控制器中能够实现相应的控制功能,使车辆较好的跟随目标车速,满足了整车控制系统的实时性和控制逻辑的有效性要求。更多还原

【Abstract】 With the increasing number of traditional car ownership,the energy crisis and environmental pollution problems are becoming more and more serious.Therefore,many countries are embarking on the development of new energy vehicles.Pure electric vehicles have the advantages of high efficiency,cleanliness and zero emissions,but they also have shortcomings such as short cruising range,long charging time and imperfect supporting facilities.The extended-range electric vehicle not only has the advantages of pure electric vehicles,but also compensates for the shortcomings of short driving range.It can be used as an ideal model for the transition from traditional fuel vehicles to pure electric vehicles.Therefore,it is of great significance to study the driving control strategy of the extended-range electric vehicle to promote the application of new energy vehicles.This article relies on the enterprise cooperation project "The System Development of Extended-range Electric Bus",with the extended-range electric bus as the research object,according to the design indicators,completed the parameter matching of powertrain.Established the driving control strategy based on the ideal reference SOC curve.Built the model of the vehicle and the driving control stratagy.Optimized the parameters by particle swarm optimization algorithm based on simulated annealing.The main research contents are as follow:Firstly,determining the vehicle control system architecture and performance indicators based on the market demand.According to these,matching the characteristic parameters of the drive motor,power battery and range extender.Check the vehicle’s power and pure electric driving range.Secondly,formulating the vehicle driving control strategy based on the vehicle performance index and power system parameter matching results.Selecting four cycle conditions which have the same characteristics and different distances,then using the dynamic programming algorithm with the target of the least driving cost to obtain the optimal trajectory of the reference SOC.Thirdly,based on the information of condition and rated discharge power of power battery,determining the optimal working range of the range extender,and selecting the difference between the actual SOC and the reference SOC and the power demand of the vehicle as the judgment amount.Formulating the two points plus power-following driving control strategy based on the ideal reference SOC curve.Fourthly,building the vehicle forward simulation model on the Cruise simulation platform,and building the driving control strategy in MATLAB/Simulink software.Integrate control strategy model and vehicle model by compiling DLL file.Setting and simulating some calculation tasks in the Cruise.The results show that the matching calculation of the power system is reasonable,and the designed drive control strategy is also effective and feasible.Fifthly,in order to achieve a better fuel economy under the condition of satisfying the dynamic conditions,thus using the particle swarm optimization algorithm based on simulated annealing to optimize the judgment threshold of the control strategy with the total driving cost as the optimization target.The result shows that the vehicle fuel economy is improved under the premise of ensuring the dynamic conditions and drving range,and the rational use of energy is achieved.Lastly,building the hardware-in-the-loop test platform which is based on the the xPC-Target.Simulating and checking the operate environment,detecting the real-time input and output performanc,and verifying the control response of the vehicle controller.The results show that the control program can realize the corresponding control function in the controller,and the vehicle can follow the target speed well.The test system meet the real-time performance of the vehicle control system and the validity of the control logic.更多还原