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为了提高插电式混合动力汽车(PHEV)电量保持模式下的燃油经济性与排放性,设计以需求转矩、电池荷电状态(SOC)和电机转速为输入,发动机转矩为输出的模糊控制器,将用来描述模糊集合隶属度函数的形状选为左右两边为开口梯形,中间为三角形。为便于模糊控制的实现,将参数量化到[0,1]的区间;根据PHEV的不同工作模式,确定输入与输出变量的模糊子集,将隶属度函数和控制规则编码,利用粒子群算法(PSO)对其隶属度函数值和控制规则进行优化。在保证动力性的前提下,以电池SOC值尽可能稳定且最大限度地提高燃油经济性和排放性为目标,引入加权系数建立CO,NOx及HC的排放指标和整车等效燃油消耗构成的价值函数,形成一种基于粒子群优化的多目标模糊控制策略(PSO-fuzzy)。综合理论建模和试验数据建模方法,基于MATLAB/Simulink搭建PHEV动力传动系统关键部件的数值模型、整车动力学模型和驾驶人模型。采用中国典型城市循环工况和高速公路循环工况的联合工况进行验证。结果表明:利用PSO算法优化的模糊控制策略相比于优化前,电池SOC的运行更为平稳;更为合理地分配了发动机与电机之间的转矩,发动机工作点更多地分布在高效区的同时使得电机绝大部分工作点都工作在高效区;百公里油耗降低12.4%,CO排放量减少2.7%,NOx排放量减少4.4%,HC排放量减少4.3%。
In order to improve the fuel economy and emission under the PHEV power conservation mode, a fuzzy control system is designed with input torque, battery state of charge (SOC) and motor speed as input and engine torque as output The shape of the membership function that will be used to describe the fuzzy set is chosen to be an open trapezoid on either side and a triangle in the middle. In order to facilitate the realization of fuzzy control, the parameters are quantified to the interval of [0,1]. According to the different working modes of PHEV, the fuzzy subsets of input and output variables are determined. The membership functions and control rules are coded and the particle swarm optimization PSO) to optimize its membership function value and control rules. Under the premise of ensuring the power, we take the battery SOC value as stable as possible and maximize the fuel economy and emission as the goal, and introduce the weighted coefficient to establish the emission index of CO, NOx and HC and the equivalent fuel consumption of the whole vehicle Value function to form a multi-objective fuzzy control strategy (PSO-fuzzy) based on particle swarm optimization. Comprehensive theoretical modeling and experimental data modeling method, based on MATLAB / Simulink PHEV powertrain system to build the key components of the numerical model, the vehicle dynamics model and the driver model. Using the typical urban cycle conditions and the highway cycle conditions for joint verification. The results show that the fuzzy control strategy based on PSO algorithm is more stable than that before optimization, and the SOC of the battery is more stable. The torque between the engine and the motor is more rationally distributed, and the engine operating point is more distributed in the high efficiency zone At the same time, most of the work points of the motor are operated in the high-efficiency zone. Fuel consumption per hundred kilometers is reduced by 12.4%, CO emissions by 2.7%, NOx emissions by 4.4% and HC emissions by 4.3%.