基于遗传算法优化模糊控制器的电动汽车复合电源能量管理策略
朱峰1，王朋波2，刘永辉1*
1.山东交通学院汽车工程学院，山东 济南 250357；2.山东飞控信息科技有限公司，山东 济南 250013
摘要：为了合理分配电动汽车动力电池和超级电容器的实际输出功率并延长电池寿命，基于MATLAB/Simulink仿真对比新欧洲驾驶循环（new European driving cycle，NEDC）工况下，逻辑门限控制策略、模糊控制策略、遗传算法（genetic algorithm，GA）优化模糊控制策略下的复合能量管理系统和整车行驶性能。仿真结果表明：GA优化模糊控制策略有效减小动力电池荷电状态下降速度及动力电池温度上升速度，提高车辆续驶里程；相比模糊控制策略，优化后电动汽车续驶里程增大12.1 km，最大爬坡性能提高0.3%，最高车速提高0.2 km/h；相比逻辑门限控制策略，优化后电动汽车续驶里程增大18.8 km，最大爬坡度提高0.6%，最高车速提高0.6 km/h。
关键词：纯电动汽车；复合电源；遗传算法；模糊控制
Composite power energy management strategy for electric vehicle based on optimized fuzzy controller of genetic algorithm
ZHU Feng1, WANG Pengbo2, LIU Yonghui1*
1.School of Automotive Engineering, Shandong Jiaotong University, Jinan 250357, China；
2.Shandong Flight Control Information Technology Co., Ltd., Jinan 250013, China
Abstract: In order to allocate the actual output power of electric vehicle power batteries and supercapacitors reasonably and extend the life of batteries, MATLAB/Simulink software is used to simulate and compare the composite energy management system and vehicle driving performance under the logic threshold control strategy, fuzzy control strategy, and genetic algorithm (GA) optimized fuzzy control strategy under the new European driving cycle (NEDC) operating conditions. The simulation results show that the GA optimized fuzzy control strategy effectively reduces the rate of decrease in the state of charge of the power battery and the rate of temperature rise of the power battery, and improves the driving range of the vehicle. Compared with the fuzzy control strategy, the optimized electric vehicle has an increased driving range of 12.1 km, improved maximum climbing performance by 0.3%, and increased maximum speed by 0.2 km/h. Compared with the logic threshold control strategy, the driving range of electric vehicles is increased by 18.8 km，the maximum climbing slope is increased by 0.6% and maximum speed is increased by 0.6 km/h.
Keywords: pure electric vehicle; composite power supply; genetic algorithm; fuzzy control
