并联式混动汽车双模糊控制能量管理策略研究  

Research on Energy Management Strategy for Dual Fuzzy Control of Parallel Hybrid Vehicles

作　　者：李智豪 肖平[1,2] 裴文俊 LI Zhihao;XIAO Ping;PEI Wenjun(School of Mechanical Engineering,Anhui Polytechnic University,Wuhu Anhui 241000,China;Anhui Province Key Laboratory of Intelligent Car Wire-Controlled Chassis System,Wuhu Anhui 241000,China)

机构地区：[1]安徽工程大学机械工程学院,安徽芜湖241000 [2]智能汽车线控底盘系统安徽省重点实验室,安徽芜湖241000

出　　处：《佳木斯大学学报（自然科学版）》2024年第4期63-67,87,共6页Journal of Jiamusi University：Natural Science Edition

基　　金：安徽省重点研究与开发计划项目(2022A05020007);国家自然基金面上项目(52375227)。

摘　　要：为了解决基于规则的能量管理策略中发动机与电机的扭矩分配不能达到最大限度降低油耗的问题,提出了一种群智能算法优化模糊控制的能量管理策略。首先,在基于规则的能量管理策略的基础上设计行车充电模式以及混动行驶模式下的模糊控制器;然后,在AVL Cruise和MATLAB/Simulink环境下分别建立整车仿真模型及混动汽车模糊控制能量管理策略;最后,将油耗、电池SOC变化及排放综合最小化作为优化目标,采用粒子群算法求解模糊控制器最优的隶属度函数。结果表明,优化后的能量管理策略可以合理地分配发动机与电机的输出扭矩,并且能够有效降低燃油消耗、电池SOC变化率及排放物。To address the issue of suboptimal torque distribution between the engine and electric motor in a rule-based energy management strategy,a swarm intelligence algorithm is proposed to optimize the fuzzy control-based energy management strategy.Initially,fuzzy controllers are designed for driving and charging modes based on the rule-based energy management strategy.Subsequently,vehicle simulation models and fuzzy control-based energy management strategies for hybrid driving are established using AVL Cruise and MATLAB/Simulink.Finally,minimizing fuel consumption,battery State of Charge(SOC)variation,and emissions is set as the optimization goal,employing a particle swarm algorithm to determine the optimal membership functions for the fuzzy controller.The results indicate that the optimized energy management strategy can judiciously allocate torque outputs between the engine and electric motor,effectively reducing fuel consumption,SOC variation,and emissions.

关 键 词：混合动力汽车 AVL Cruise 能量管理策略 模糊控制 粒子群算法 

分 类 号：U469.7[机械工程—车辆工程]