基于改进NSGA-Ⅱ算法的拖拉机传动系统匹配优化
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国家重点研发计划项目(2017YFD0700101)


Matching Optimization for Tractor Powertrain Based on Improved NSGA-Ⅱ Algorithm
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    摘要:

    为实现拖拉机动力传动系统的最优化匹配,提高整机动力性和燃油经济性,提出一种基于改进非支配排序遗传算法(Nondominated sorting genetic algorithmⅡ,NSGA-Ⅱ)的拖拉机传动系统匹配优化方法。该方法引入正态分布交叉算子,在保证解集质量的基础上,扩大空间搜索范围,同时加入差分进化变异算子,抽取其中的差分向量与NSGA-Ⅱ算法结合,从而避免算法陷入局部最优,改善种群分布性。随后,以变速箱各挡传动比为输入变量,以驱动功率损失率和比燃油消耗损失率均最低为优化目标,通过分析拖拉机设计理论车速、传动比公比、驱动附着力限制等约束条件,建立了变速箱传动比匹配优化模型,利用改进算法对拖拉机变速箱传动比进行优化,并与原NSGA-Ⅱ算法及加权遗传算法进行对比。分析结果表明,改进NSGA-Ⅱ算法求得的解集分布评价指标SP优于原NSGA-Ⅱ算法,表明Pareto最优解分布更均匀,且更接近测试函数的真实Pareto前沿。经本文算法优化后,理论上拖拉机驱动功率损失率和比燃油消耗损失率分别降低了41.62%和62.8%,运输挡头挡爬坡度可提高2.35%,整机综合性能得到明显改善,且优化效果均优于对比算法,验证了本文方法的有效性,可为拖拉机传动系统设计与优化提供一定参考。

    Abstract:

    In order to optimize matching of the tractor powertrain and improve the power and fuel economy of the tractor, a new matching optimization method for tractor powertrain was put forward based on the improved nondominated sorting genetic algorithmⅡ. The normal distribution crossover operator (NDX) was introduced to expand the spatial search range on the premise of ensuring the quality of the nondominated solution set. And meanwhile, the differential evolution mutation operator based differential evolutionary algorithm was used as directional guiding ideology to avoid falling into the local optimum and improve the uniformity of population distribution. Subsequently, by analyzing the design requirements and powershift transmissions produced by New Holland, Case IH and John Deere, the optimization model of transmission ratios was established with constraints such as vehicle speed, ratio of gear ratios, driving adhesion restriction, and so on. In this model, gear ratios were taken as input variables, and the optimization objective was to get the lowest drive power loss rate and the lowest specific fuel consumption loss rate. The proposed algorithm was used to optimize the tractor powertrain and compared with the original NSGA-Ⅱ and the weighted genetic algorithm. The experimental results showed that the distributed index SP of the proposed algorithm was smaller than that of the original NSGA-Ⅱ, which meant that the improved NSGA-Ⅱ could obtain a more uniformly distributed and precise optimal solutions. And after optimization of the improved NSGA-Ⅱ, the drive power loss rate and the specific fuel consumption loss rate of the tractor could be theoretically reduced by 41.62% and 62.8%, respectively, and the climbing angle of the first transport gear could be increased by 2.35% than before, which was better than NSGA-Ⅱ and the weighted genetic algorithm. The overall performance of the tractor was improved obviously which verified the effectiveness of the improved NSGA-Ⅱ algorithm. To sum up, this method could provide a certain reference for the design and optimization of the tractor transmission.

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傅生辉,李 臻,杜岳峰,毛恩荣,朱忠祥.基于改进NSGA-Ⅱ算法的拖拉机传动系统匹配优化[J].农业机械学报,2018,49(11):349-357. FU Shenghui, LI Zhen, DU Yuefeng, MAO Enrong, ZHU Zhongxiang. Matching Optimization for Tractor Powertrain Based on Improved NSGA-Ⅱ Algorithm[J]. Transactions of the Chinese Society for Agricultural Machinery,2018,49(11):349-357

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  • 收稿日期:2018-07-15
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  • 在线发布日期: 2018-11-10
  • 出版日期: 2018-11-10