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基于改進(jìn)NSGA-Ⅱ算法的拖拉機(jī)傳動(dòng)系統(tǒng)匹配優(yōu)化
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國(guó)家重點(diǎn)研發(fā)計(jì)劃項(xiàng)目(2017YFD0700101)


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

    為實(shí)現(xiàn)拖拉機(jī)動(dòng)力傳動(dòng)系統(tǒng)的最優(yōu)化匹配,提高整機(jī)動(dòng)力性和燃油經(jīng)濟(jì)性,提出一種基于改進(jìn)非支配排序遺傳算法(Nondominated sorting genetic algorithmⅡ,NSGA-Ⅱ)的拖拉機(jī)傳動(dòng)系統(tǒng)匹配優(yōu)化方法。該方法引入正態(tài)分布交叉算子,在保證解集質(zhì)量的基礎(chǔ)上,擴(kuò)大空間搜索范圍,同時(shí)加入差分進(jìn)化變異算子,抽取其中的差分向量與NSGA-Ⅱ算法結(jié)合,從而避免算法陷入局部最優(yōu),改善種群分布性。隨后,以變速箱各擋傳動(dòng)比為輸入變量,以驅(qū)動(dòng)功率損失率和比燃油消耗損失率均最低為優(yōu)化目標(biāo),通過(guò)分析拖拉機(jī)設(shè)計(jì)理論車速、傳動(dòng)比公比、驅(qū)動(dòng)附著力限制等約束條件,建立了變速箱傳動(dòng)比匹配優(yōu)化模型,利用改進(jìn)算法對(duì)拖拉機(jī)變速箱傳動(dòng)比進(jìn)行優(yōu)化,并與原NSGA-Ⅱ算法及加權(quán)遺傳算法進(jìn)行對(duì)比。分析結(jié)果表明,改進(jìn)NSGA-Ⅱ算法求得的解集分布評(píng)價(jià)指標(biāo)SP優(yōu)于原NSGA-Ⅱ算法,表明Pareto最優(yōu)解分布更均勻,且更接近測(cè)試函數(shù)的真實(shí)Pareto前沿。經(jīng)本文算法優(yōu)化后,理論上拖拉機(jī)驅(qū)動(dòng)功率損失率和比燃油消耗損失率分別降低了41.62%和62.8%,運(yùn)輸擋頭擋爬坡度可提高2.35%,整機(jī)綜合性能得到明顯改善,且優(yōu)化效果均優(yōu)于對(duì)比算法,驗(yàn)證了本文方法的有效性,可為拖拉機(jī)傳動(dòng)系統(tǒng)設(shè)計(jì)與優(yōu)化提供一定參考。

    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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傅生輝,李 臻,杜岳峰,毛恩榮,朱忠祥.基于改進(jìn)NSGA-Ⅱ算法的拖拉機(jī)傳動(dòng)系統(tǒng)匹配優(yōu)化[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),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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  • 在線發(fā)布日期: 2018-11-10
  • 出版日期: 2018-11-10