基于SA-PSO的Plug-In混合动力汽车模糊控制策略优化研究

发布时间：2018-01-19 08:00

本文关键词： Plug-In混合动力汽车能量管理策略模糊规则再生制动模拟退火粒子群算法工况识别　出处：《山东大学》2017年硕士论文　论文类型：学位论文

【摘要】：面临着全球能源短缺和环境污染的严峻现实,传统汽车行业必须转型以实现汽车行业的可持续发展,在这一转型探索中,开发低油耗、低排放的新能源汽车已然成为当今汽车行业首要任务。在众多的新能源汽车中,Plug-In混合动力汽车(Plug-In Hybrid Electric Vehicle,简称PHEV)以其节能环保、性能优良的特点深受人们欢迎。整车能量管理作为PHEV 一大核心控制问题,直接影响着车辆的燃油经济性和动力性能,是PHEV实现低油耗和低排放的关键。能量管理的作用是通过协调控制发动机和动力电池间能量流动的方向和大小,从而满足车辆动力需求,并提升PHEV的燃油经济性。本文针对PHEV的能量管理问题,以提高燃油经济性为目标,从驱动和制动两个方面着手研究。动力系统的总成匹配是实现能量管理的基础。本文针对并联PHEV,在AVL CRUISE仿真软件中建立了整车模型,结合车辆参数及动力性能要求对发动机、电动机、动力电池等部件进行参数匹配,并通过仿真实验对动力性能进行了验证。本文首先设计了基于模糊规则的PHEV能量管理策略,选择电池SOC、车速及发动机工作效率作为策略的控制依据。考虑到PHEV在行驶过程中因工作模式的不断切换导致发动机频繁启停并引起动力系统冲击,从而影响行车平顺性和乘车舒适性。本文在能量管理策略中添加了发动机的启停控制策略,显著减少了发动机的启停频率。目前大多数能量管理策略侧重于对驱动阶段的能量流进行优化。事实上,在启停频繁的市区工况存在着可观的制动能量,充分回收这些制动能量对于改善PHEV的燃油经济性非常重要。但是,制动能量的回收常与制动性能相矛盾,在设计再生制动策略时还需要考虑制动性能。因此,本文对PHEV的再生制动系统进行了综合分析,并设计了模糊再生制动策略。在三种典型循环工况下的仿真结果表明,该再生制动策略能够显著提高PHEV的燃油经济性,并且相对其他再生制动策略有着一定优越性。针对模糊控制策略依然存在着依赖工程经验的缺陷,本文采用了模拟退火粒子群算法对文中设计的整车能量管理模糊控制器进行参数优化。同时,为了提升优化参数对不同工况的适应性,本文采用两种方法进行改进:(1)采用综合工况优化方式,将 HWFET、LA92、Ja1015、NEDC、MANHATTAN 和 NYCC典型工况组成一个综合工况,然后在综合工况进行能量管理优化。(2)采用工况识别技术,将(1)中的综合工况划分成低速、中速、高速三种不同工况,并对每种工况进行能量管理优化,最后通过在线工况识别技术,选择适当的控制参数。两种策略还分别在其他工况下进行了仿真验证。结果表明,综合工况优化方式和工况识别技术都能进一步提升PHEV的燃油经济性,且优化结果同样适用于其他工况。
[Abstract]:Facing the severe reality of global energy shortage and environmental pollution, the traditional automobile industry must be transformed to realize the sustainable development of the automobile industry. In this transformation exploration, the development of low fuel consumption. Low-emission new energy vehicles have become the top priority of the automotive industry today. Among the many new energy vehicles. Plug-In hybrid vehicle Plug-In Hybrid Electric vehicle (PHEV) is energy saving and environmental protection. As a core control problem of PHEV, vehicle energy management has a direct impact on vehicle fuel economy and power performance. The function of energy management is to control the direction and size of energy flow between engine and power battery in order to meet the vehicle power demand. And improve the fuel economy of PHEV. This paper aims at improving the fuel economy of PHEV. The power system assembly matching is the basis of energy management. The whole vehicle model is established in the AVL CRUISE simulation software for parallel pHEV. Combined with vehicle parameters and dynamic performance requirements of the engine, motor, power battery and other components for parameter matching. The dynamic performance is verified by simulation experiments. Firstly, the PHEV energy management strategy based on fuzzy rules is designed, and the battery SOC is selected. The speed and efficiency of the engine are considered as the control basis of the strategy. Considering the frequent start and stop of the engine and the impact of the power system due to the continuous switching of the working mode during the driving process of PHEV. In order to affect the ride comfort and ride comfort, this paper adds the engine start and stop control strategy to the energy management strategy. Most of the current energy management strategies focus on optimizing the energy flow in the drive phase. In fact, there is considerable braking energy in the urban areas where the engine starts and stops frequently. Fully recovering these braking energy is very important to improve the fuel economy of PHEV. However, the recovery of braking energy is often inconsistent with the braking performance. The braking performance should be considered when designing regenerative braking strategy. Therefore, the regenerative braking system of PHEV is comprehensively analyzed in this paper. The fuzzy regenerative braking strategy is designed. The simulation results under three typical cycle conditions show that the regenerative braking strategy can significantly improve the fuel economy of PHEV. Compared with other regenerative braking strategies, the fuzzy control strategy still has the defect of relying on engineering experience. In this paper, the simulated annealing particle swarm optimization algorithm is used to optimize the parameters of the vehicle energy management fuzzy controller designed in this paper. At the same time, in order to improve the adaptability of the optimized parameters to different working conditions. In this paper, two methods are used to improve the system. (1) the HWFETT LA92Ja1015NDC is optimized under the integrated working conditions. MANHATTAN and NYCC typical operating conditions constitute a comprehensive working condition, and then in the integrated conditions of energy management optimization. 2) the use of condition identification technology. The integrated working conditions are divided into three different working conditions: low speed, medium speed and high speed, and the energy management is optimized for each condition. Finally, the on-line working condition identification technology is adopted. The two strategies are also simulated under other operating conditions. The results show that the integrated mode of operation optimization and condition identification technology can further improve the fuel economy of PHEV. The optimization results are also applicable to other conditions.
【学位授予单位】：山东大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：U469.7;TP273.4

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