基于差分进化算法的混合动力系统多目标优化  

Multi-objective Optimization for Hybrid Power System based on the Differential Evolution Algorithm

作　　者：邓涛[1] 林椿松 李亚南[1] 卢任之 Deng Tao Lin Chunsong Li Ya＇nan Lu Renzhi(School of Mechatronics ＆ Automotive Engineering, Chongqing Jiaotong University, Chongqing 400074, Chin)

机构地区：[1]重庆交通大学机电与车辆工程学院,重庆400074

出　　处：《机械传动》2016年第10期80-84,共5页Journal of Mechanical Transmission

基　　金：国家自然科学基金(51305473);中国博士后科学基金(2014M552317);重庆市基础与前沿研究计划项目(cstc2013jcyj A60007);重庆市教委科学技术研究项目(KJ120421);重庆市博士后研究人员科研项目特别资助(xm2014032)

摘　　要：针对混合动力能量管理系统多目标优化问题通常采用加权求和等方法的缺点,采用Pareto最优原理处理燃油经济性与排放性(CO、NOx、HC)评价指标,提出一种基于非支配排序的自适应差分进化算法,并应用于混合动力能量管理多目标优化系统之中并进行仿真分析。结果表明,提出的优化方法能够得到一组非支配Pareto最优解集,且燃油经济性最大提高了5.30%,CO排放物最大下降了3.65%,NOx最大下降了14.40%,HC最大下降了3.26%。Considering the disadvantages of energy management multi - objectives optimization for hybrid electric vehicle （HEV） which usually adopts weighted sum method, by using the Pareto optimum algorithm, based on non - dominated sorting method to deal with the fuel economy and emissions （ CO, NOx and HC） eval- uation indexes, then a new adaptive differential evolution algorithm is proposed and simulated which can be applied to multi -objective optimization of HEV energy management. The simulation results show that, a Pareto optimal solution set can be obtained by the proposed multi -objectives optimization method. And the maximum fuel economy increases by 5.30% , the largest CO emissions reduces by 3.65%, in the biggest NOx falls by 14.40% the max HC drops down by 3.26%.

关 键 词：混合动力 多目标优化 非支配排序 自适应差分进化算法 能量管理 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]