摘要
为充分考虑汽车跟车制动的安全性,同时提高电动汽车能源利用率,提出一种基于加速踏板的主动制动能量回收控制策略。设计了以加速踏板位移、加速踏板位移变化率、车头时距为输入变量,以车辆状态为输出的模糊控制器,识别的车辆状态为主动轻度制动和主动中度制动及车辆加速。对于轻度制动,仅电机参与制动;对于中度制动,建立综合考虑汽车制动安全性和节能性的目标函数,利用粒子群优化(particle swarm optimization,PSO)算法寻找目标函数最小值,来控制汽车前后轴机械制动力和再生制动力分配。将开发的再生制动控制策略嵌入AVL Cruise整车仿真模型,进行联合仿真。结果表明:所提的主动制动能量回收控制策略相对被动制动策略能有效减小制动距离和制动时间,1个新欧洲行驶循环(new European driving cycle,NEDC)工况下的节能贡献度为10.05%,相对优化前提高了3.57%。
In order to fully consider the safety of vehicle braking and improve the energy utilization of electric vehicles,an active braking energy recovery control strategy based on accelerator pedal is proposed.A fuzzy controller was designed,which took the acceleration pedal displacement,the change rate of the acceleration pedal displacement and the headway as the input variables and the vehicle status as the output variables.The vehicle status identified were active mild braking,active moderate braking and vehicle acceleration..For mild braking,only motor braking was involved;while for moderate braking,a target letter considering braking safety and energy saving was established.Particle swarm optimization(PSO)algorithm was used to find the minimum objective function to control the distribution of mechanical braking force and regenerative braking force between front and rear axles.The developed regenerative braking control strategy was embedded in the AVL Cruise vehicle simulation model,and the joint simulation was carried out.The results show that the active braking energy recovery control strategy can effectively reduce the braking distance and braking time compared with the passive braking strategy.The contribution of energy saving under a new European driving cycle(NEDC)is 10.05%,which is 3.57%higher than that before optimization.
作者
李争争
周志刚
杨文豪
孟祥明
LI Zhengzheng;ZHOU Zhigang;YANG Wenhao;MENG Xiangming(School of Vehicle and Traffic Engineering, Henan University of Science and Technology, Luoyang, Henan 471003, china;Ningbo Shenglong Co. , Ltd. , Ningbo, Zhejiang 315100, China)
出处
《中国科技论文》
CAS
北大核心
2020年第8期935-941,共7页
China Sciencepaper
基金
国家自然科学基金资助项目(51305126)
河南省高等学校青年骨干教师培养计划项目(2017GGJS063)
河南科技大学研究生创新基金资助项目(CXJJ-2018-KJ13)。
关键词
主动制动能量回收
模糊控制策略
粒子群优化算法
节能贡献率
active braking energy recovery
fuzzy control strategy
particle swarm optimization(PSO)algorithm
energy conservation contribution rate
