基于驾驶意图与行驶工况的混合动力电动汽车能量管理策略

发布时间：2018-12-20 22:08

【摘要】：混合动力电动汽车(Hybrid Electric Vehicle,HEV)能量管理策略是影响混合动力系统性能优劣的关键,合理有效的能量管理策略可以提高整车的燃油经济性。本文以某公司的某款混合动力电动汽车为基础,依托国家自然科学基金资助项目“双离合器式单电机重度混合动力系统匹配与控制研究”(51305468),开展了基于驾驶意图与行驶工况的混合动力电动汽车能量管理策略研究,主要研究内容如下:(1)本文以某混合动力电动汽车的动力系统关键零部件参数为基础,利用多种不同类型的行驶工况协调优化混合动力电动汽车动力系统与能量控制策略参数以提高汽车的燃油经济性,提出了一种采用模拟退火粒子群算法的基于多工况优化混合动力电动汽车动力系统与控制策略参数的方法。利用MATLAB/SIMULINK仿真平台搭建整车仿真模型,对所提出的控制策略优化方法进行仿真验证,结果表明,利用该方法可以获取一组参数,在保持汽车动力性以及电池SOC平衡的基础上,相对于优化前,在综合工况下油耗降低了5.49%。(2)针对基于多工况的优化方法存在一定的局限性,以及现有基于工况识别的能量管理策略在车辆行驶过程中未全面考虑动力电池荷电状态范围对电池工作效率的影响,开展了基于工况识别的混合动力电动汽车能量管理策略研究。从ADVISOR中选取23个典型的循环工况,用聚类分析方法将其划分为五类,把SOC平衡油耗引入目标函数,采用模拟退火粒子群算法对各类工况下能量管理策略中关键参数进行离线优化,建立优化参数数据库,在此基础上提出了一种基于工况识别的能量管理策略优化方法,利用构建的综合测试工况对所制定的能量管理策略进行仿真分析。(3)考虑驾驶意图对整车能耗经济性的影响,通过车辆行驶过程中的车体冲击度与识别工况的平均冲击度对驾驶意图进行分析识别,从而确定车辆需求转矩修正系数,利用电机的快速瞬态响应特性,对电机的转矩进行控制,结合基于工况识别的能量管理策略,建立了基于驾驶意图与工况识别的综合能量管理策略,利用构建的综合测试工况对所制定的能量管理策略进行仿真分析。结果表明,所制定的综合能量管理策略与单独采用基于工况识别的能量管理策略相比,在综合工况下油耗降低了1.71%,同时电机的能量回收率有所提高。(4)基于D2P平台和Matlab/Simulink/Stateflow开发环境下开发了整车控制软件,采用ATI-VISION平台建立了数据采集和标定控制系统,利用实车进行道路试验对提出的基于逻辑门限的改进型能量管理策略进行了验证。结果表明,本文制定的改进型能量管理策略能够很好地分配发动机与电机的扭矩,显著的改善了整车的经济性能,部分验证了所提出策略的有效性。
[Abstract]:The energy management strategy of hybrid electric vehicle (Hybrid Electric Vehicle,HEV) is the key to affect the performance of hybrid electric vehicle (HEV). A reasonable and effective energy management strategy can improve the fuel economy of the whole vehicle. Based on a hybrid electric vehicle of a certain company, this paper relies on the project "matching and Control of heavy Hybrid Power system with double clutch Type single Motor" (51305468), supported by the National Natural Science Foundation of China. The energy management strategy of hybrid electric vehicle based on driving intention and driving condition is studied. The main contents are as follows: (1) this paper is based on the parameters of the key parts of the power system of a hybrid electric vehicle. In order to improve the fuel economy of the vehicle, the power system and the energy control strategy parameters of the hybrid electric vehicle are optimized by using a variety of different driving conditions. This paper presents a method of optimizing the parameters of hybrid electric vehicle power system and control strategy based on simulated annealing particle swarm optimization algorithm. The simulation model of the whole vehicle is built by using the MATLAB/SIMULINK simulation platform, and the simulation results show that a set of parameters can be obtained by using this method, and on the basis of maintaining the vehicle power performance and the balance of the battery SOC, the proposed control strategy optimization method is verified. Compared with before optimization, the fuel consumption is reduced by 5.49 in the comprehensive working condition. (2) there are some limitations in the optimization method based on multiple working conditions. And the existing energy management strategy based on condition identification does not fully consider the influence of the range of charged state of the power battery on the efficiency of the battery during the vehicle driving. The energy management strategy of HEV based on condition identification is studied. In this paper, 23 typical cycle conditions are selected from ADVISOR, which are divided into five categories by cluster analysis, and SOC equilibrium oil consumption is introduced into objective function. The simulated annealing particle swarm optimization algorithm is used to optimize the key parameters of energy management strategy under various operating conditions offline, and the database of optimization parameters is established. On the basis of this, an optimization method of energy management strategy based on condition identification is proposed. The energy management strategy is simulated and analyzed by using the constructed comprehensive test conditions. (3) considering the effect of driving intention on the energy consumption economy of the whole vehicle, The driving intention is analyzed and identified by the impact degree of the vehicle body and the average impact degree of the identification condition during the vehicle driving process, so as to determine the torque correction coefficient of the vehicle demand and make use of the fast transient response characteristics of the motor. The torque of motor is controlled, combined with the energy management strategy based on condition identification, a comprehensive energy management strategy based on driving intention and condition identification is established. The energy management strategy is simulated and analyzed by using the constructed comprehensive test conditions. The results show that compared with the single energy management strategy based on condition identification, the fuel consumption of the proposed integrated energy management strategy is 1.71% lower than that of the energy management strategy based on condition identification alone. At the same time, the energy recovery rate of the motor has been improved. (4) based on D2P platform and Matlab/Simulink/Stateflow development environment, the whole vehicle control software has been developed, and the data acquisition and calibration control system has been established by using ATI-VISION platform. The improved energy management strategy based on logic threshold is verified by real vehicle road test. The results show that the improved energy management strategy can distribute the torque between engine and motor and improve the economic performance of the whole vehicle. The effectiveness of the proposed strategy is partly verified.
【学位授予单位】：重庆大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：U469.7

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