基于混沌粒子群算法的某款纯电动汽车动力系统参数优化

发布时间：2018-03-27 08:15

本文选题：纯电动汽车　切入点：动力参数匹配　出处：《长安大学》2016年硕士论文

【摘要】：我国的能源正逐步耗尽以及环境问题变得越来越严重,新能源汽车已经成为发展热点。纯电动汽车作为真正的零排放,低消耗的产品也不断的被国家政策扶持发展、被汽车企业所推广、被消费者所认可。而电动汽车的动力系统参数的合理性直接影响着汽车的动力性和经济性,同时决定着汽车整体的成本,而这些因素都是消费者关注的内容,从而影响着纯电动汽车的普及程度。所以动力系统参数的合理性选择在整个研发周期中是至关重要的。本文以吉利汽车公司某款纯电动汽车为研究平台,对其动力部件的主要数值进行匹配和计算。在国家政策驱使和公司发展需要下吉利汽车基于一款传统车研发设计并生产出一款纯电动汽车。本文以此为研究对象,根据目标性能指标参考理论基础知识计算出动力系统匹配参数;根据理论计算选择需要匹配的动力系统部件,包括电机驱动系统、动力电池及其管理系统、传动部件;选定各个部件之后在仿真软件GT-suite中建立整车模型,进行性能能仿真,同时对样车Mucar进行底盘测功实验用来验证整车的性能指标和校准仿真软件偏差结果。建立汽车纵向力学的数学模型从而得到动力系统参数的目标函数。提出一种新的优化思想,即在MATLAB的M文件中编辑混沌粒子群优化算法,将不同的目标函数导入优化算法优化,得出不同的优化参数带入GT-suite中建立的物理模型中进行仿真,得出优化之后的动力和经济性能。通过对比优化方案与最初设计方案的性能数值得出,优化思想的可行性及混沌粒子群优化算法可用于纯电动汽车动力系统参数的匹配计算及性能提升,而且得到结果较为准确,并可以用此方法优化电动汽车纵向力学特性并得出预期的汽车性能指标。
[Abstract]:China's energy is gradually depleted and environmental problems become more and more serious. New energy vehicles have become a hot spot of development. Pure electric vehicles as a true zero emissions, low-consumption products are also constantly supported by national policies to develop. The rationality of the parameters of the power system of the electric vehicle directly affects the power performance and economy of the vehicle, and determines the overall cost of the car. And these factors are what consumers are concerned about. Therefore, the rational choice of power system parameters is very important in the whole research and development cycle. This paper takes a pure electric vehicle of Geely Motor Company as the research platform. The main values of its power components are matched and calculated. Geely has designed and produced a pure electric vehicle based on a traditional vehicle, driven by national policy and required by the development of the company. The matching parameters of the power system are calculated according to the theoretical basis knowledge of the target performance index, the components of the power system which need to be matched are selected according to the theoretical calculation, including the motor drive system, the power battery and its management system, and the transmission parts. After each component is selected, the whole vehicle model is established in the simulation software GT-suite, and the performance can be simulated. At the same time, the chassis power measurement experiment on the Mucar of the prototype vehicle is carried out to verify the performance index of the whole vehicle and the deviation result of the calibration simulation software. The mathematical model of the longitudinal mechanics of the vehicle is established and the objective function of the parameters of the power system is obtained. A new optimization idea is proposed. That is, edit chaotic particle swarm optimization algorithm in M file of MATLAB, import different objective function into optimization algorithm, and get different optimization parameters into the physical model established in GT-suite for simulation. The dynamic and economic performance after optimization is obtained. By comparing the performance values of the optimized scheme with the original design scheme, The feasibility of the optimization idea and the chaos particle swarm optimization algorithm can be applied to the parameter matching and performance improvement of pure electric vehicle power system, and the results are more accurate. This method can be used to optimize the longitudinal mechanical properties of electric vehicles and obtain the expected performance index.
【学位授予单位】：长安大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：U469.72

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