摘要
为了提升电动汽车动力性能、降低车辆成本,以复合电源成本和车辆电耗最小为目标,通过交叉熵(crossentropy,CE)算法对车载复合电源的参数优化进行了研究.首先,以某款纯电动汽车为研究对象,根据能量与功率性能指标确定锂离子电池和超级电容的容量范围;其次,选取复合电源成本和车辆电耗建立多目标优化函数,并在ADVISOR环境中搭建车辆仿真模型;接着,采用CE算法,通过种群的不断迭代,更新高斯概率密度函数的均值和方差,找到复合电源参数的Pareto最优解集;最后,从最优Pareto解集中选取典型的匹配参数,分析复合电源成本、车辆电耗和整车性能.研究结果表明:在满足基本约束的前提下,得到了由100个解组成的Pareto最优解集.与第二代非劣排序遗传算法(non-dominated sorting genetic algorithm-Ⅱ,NSGA-Ⅱ)比较,CE算法有更好的收敛性与分布性;复合电源成本平均降低了9.49%,车辆电耗平均降低了22.81%;此外,城市道路循环工况(urban dynamometer driving schedule,UDDS)下车速误差最大值降低16.15%,整车动力性也有显著提升,百公里加速时间缩短7.81%,最高车速提升1.98%.
In order to improve the dynamic performance of electric vehicles and reduce costs,a parameter optimization method for vehicle-mounted hybrid power supply based on cross-entropy(CE)algorithm is explored with the intent of minimizing the hybrid power supply cost and power consumption.Firstly,a hybrid electric vehicle is used as the object,and the capacity ranges of its lithium-ion batteries and super-capacitors are determined according to the energy and power performance indexes.Secondly,the multi-objective optimization function of minimizing power supply cost and power consumption and the vehicle simulation model are established in ADVISOR.Subsequently,with CE algorithm,the mean and variance of the Gaussian probability density function are updated by the continuous iterations of populations to find out the optimal Pareto solution set.Finally,the typical solutions are selected to analyze the cost,power consumption and vehicle performance.The results show that under the basic requirements,100 optimal solutions are found,which constitute an optimal Pareto solution set.Compared with the results of(non-dominated sorting genetic algorithm-Ⅱ)NSGA-Ⅱ,the convergence and distribution of CE algorithm are better,the cost of hybrid power supply is reduced by 9.49%and the vehicle power consumption by 22.81%on average.Furthermore,the maximum error of vehicle speed is reduced by 16.15%under UDDS cycle condition,and the vehicle dynamic performance is improved significantly with the acceleration time of 100 km reduced by 7.81%and the maximum speed increased by 1.98%.
作者
戴朝华
刘洋
黄晨曦
赵舵
郭爱
陈维荣
刘楠
DAI Chaohua;LIU Yang;HUANG Chenxi;ZHAO Duo;GUO Ai;CHEN Weirong;LIU Nan(School of Electrical Engineering,Southwest Jiaotong University,Chengdu 610031,China;CRRC TANGSHAN Co.Ltd.,Tangshan 063035,China)
出处
《西南交通大学学报》
EI
CSCD
北大核心
2020年第4期839-846,共8页
Journal of Southwest Jiaotong University
基金
国家重点研发计划(2017YFB1201003,2017YFB1201005)。
关键词
复合电源
CE算法
多目标优化
参数匹配
hybrid power supply
cross-entropy algorithm
multi-objective optimization
parameter matching
