CAJ | 학술논문

为在满足动力性前提下，降低混合动力汽车（HEV）的油耗和排放，提出了一种新的参数优化方法。以ADVISOR为仿真平台，应用正交试验设计，找出了对油耗和排放性能影响最显著的5个动力系统部件及控制策略的系统参数。建立了HEV多目标优化模型。用多目标遗传算法和最小二乘意义下的主客观组合赋权法，得到该模型的Pareto最优解集合，并从中选出了最优参数组合。结果表明：与优化前相比较，优化后的参数下，每100 km 的油耗降低25.3%，每1 km的CO的排放质量降低35.5%，每1 km的HC+NOx的排放质量降低13.7%。因而，验证了该方法的有效性。
위재만족동력성전제하，강저혼합동력기차（HEV）적유모화배방，제출료일충신적삼수우화방법。이ADVISOR위방진평태，응용정교시험설계，조출료대유모화배방성능영향최현저적5개동력계통부건급공제책략적계통삼수。건립료HEV다목표우화모형。용다목표유전산법화최소이승의의하적주객관조합부권법，득도해모형적Pareto최우해집합，병종중선출료최우삼수조합。결과표명：여우화전상비교，우화후적삼수하，매100 km 적유모강저25.3%，매1 km적CO적배방질량강저35.5%，매1 km적HC+NOx적배방질량강저13.7%。인이，험증료해방법적유효성。
A parameter optimization method for hybrid electric vehicle (HEV) was proposed to improve fuel economy and reduce emission within requisite power performances. An orthogonal experimental design was used with ADVISOR platform to ifnd out the ifrst iffth notable system parameters, which severely inlfuence the fuel economy and emission of HEV, among power components and control strategies. An optimization model was built using a multi-objective genetic algorithm to obtain a set of Pareto-optimal solution. An optimal parameter combination from the solution set was selected using a combination weighting method between subjective and objective evaluation in a least squares sense. The results show that with the optimized parameters, the fuel consumption per 100km is reduced by 25.3%, the CO emissions per kilometer is reduced by 35.5%, and the total HC and NOx emission is reduced by 13.7%. These facts verify the effectiveness of the method.