基于改进NSGA-Ⅱ算法的拖拉机传动系统匹配优化
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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。
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.
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.
引文
1鞠超,王永强,董文华,等.有级拖拉机传动系挡间传动比分配研究[J].拖拉机与农用运输车,2016,43(4):28-34.JU Chao,WANG Yongqiang,DONG Wenhua,et al.Research on adjacent transmission ratio distribution of tractor range transmission[J].Tractor&Farm Transporter,2016,43(4):28-34.(in Chinese)
2机械电子工业部洛阳拖拉机研究所.拖拉机设计手册[M].北京:机械工业出版社,1994.
3宋强,叶山顶,李伟聪,等.基于多目标遗传算法的纯电动汽车AMT综合换挡规律研究[J].汽车工程学报,2017,7(1):44-51.SONG Qiang,YE Shanding,LI Weicong,et al.Research on AMT overall shift schedule for pure electric vehicles based on NSGA-Ⅱalgorithm[J].Chinese Journal of Automotive Engineering,2017,7(1):44-51.(in Chinese)
4李军.基于Cruise的某型大客车动力传动系统优化匹配研究[D].长春:吉林大学,2013.LI Jun.Research on matching of powertrain for coach based on Cruise[D].Changchun:Jilin University,2013.(in Chinese)
5 ZHOU X,QIN D,HU J,et al.Multi-objective optimization design and performance evaluation for plug-in hybrid electric vehicle powertrains[J].Applied Energy,2017,208:1608-1625.
6 FOFANA A,HAAS O,ERSANILLI V,et al.Multi-objective genetic algorithm for an automatic transmission gear shift map[J].IFAC-PapersOnLine,2016,49(3):123-128.
7张红.重型汽车动力传动系参数匹配优化[D].淄博:山东理工大学,2012.ZHANG Hong.Matching optimization of powertrain system parameters for heavy vehicle[D].Zibo:Shandong University of Technology,2012.(in Chinese)
8张抗抗,徐梁飞,华剑锋,等.基于多目标优化的纯电动车动力系统参数匹配方法[J].汽车工程,2015,37(7):757-765.ZHANG Kangkang,XU Liangfei,HUA Jianfeng,et al.A parameter matching method for the powertrain of battery electric vehicle based on multi-objective optimization[J].Automotive Engineering,2015,37(7):757-765.(in Chinese)
9程方晓.基于自适应保持多样性遗传算法的汽车动力传动系多目标优化[D].长春:吉林大学,2011.CHENG Fangxiao.Multi-objective optimization of automotive powertrain based on adaptive maintain diversity genetic algorithm[D].Changchun:Jilin University,2011.(in Chinese)
10雷嗣军.汽车动力传动系参数匹配与仿真优化[D].杭州:浙江大学,2010.LEI Sijun.Parameters matching and simulation optimization of automobile transmission[D].Hangzhou:Zhejiang University,2010.(in Chinese)
11 ZHANG P,CHEN Y,LIN M,et al.Optimum matching of electric vehicle powertrain[J].Energy Procedia,2016,88:894-900.
12王粉粉.拖拉机电控机械式自动变速系统设计研究[D].洛阳:河南科技大学,2014.WANG Fenfen.Study on the design for automated mechanical transmission system of tractor[D].Luoyang:Henan University of Science and Technology,2014.(in Chinese)
13叶素娣,徐敬华.精密变位斜齿轮行星轮系混合型变量多目标优化设计[J].机械设计,2016,33(7):98-104.YE Sudi,XU Jinghua.Multi-objective optimization design of mixed variables on precision variable helical planetary gear train[J].Journal of Machine Design,2016,33(7):98-104.(in Chinese)
14叶秉良,俞高红,赵匀.拖拉机最终传动多目标模糊可靠性优化设计[J].农业机械学报,2010,41(4):95-100.YE Bingliang,YU Gaohong,ZHAO Yun.Multi-objective fuzzy reliability optimization design of tractor's final transmission[J].Transactions of the Chinese Society for Agricultural Machinery,2010,41(4):95-100.(in Chinese)
15 ZHOU A,QU B Y,LI H,et al.Multi-objective evolutionary algorithms:a survey of the state of the art[J].Swarm and Evolutionary Computation,2011,1(1):32-49.
16 TAN K C,LEE T H,KHOR E F.Evolutionary algorithms for multi-objective optimization:performance assessments and comparisons[J].Artificial Intelligence Review,2002,17(4):251-290.
17 ELBELTAGI E,HEGAZY T,GRIERSON D.Comparison among five evolutionary-based optimization algorithms[J].Advanced Engineering Informatics,2005,19(1):43-53.
18 DEB K,PRATAP A,AGARWAL S,et al.A fast and elitist multi-objective genetic algorithm:NSGA-Ⅱ[J].IEEE Transactions on Evolutionary Computation,2002,6(2):182-197.
19 MOSBAH M,ARIF S,MOHAMMEDI R D.Multi-objective optimization for optimal multi DG placement and sizes in distribution network based on NSGA-Ⅱand fuzzy logic combination[C]∥International Conference on Electrical Engineering-Boumerdes,2017:1-6.
20王茜,张粒子.采用NSGA-Ⅱ混合智能算法的风电场多目标电网规划[J].中国电机工程学报,2011,31(19):17-24.WANG Qian,ZHANG Lizi.Multi-objective transmission planning associated with wind farms applying NSGA-Ⅱhybrid intelligent algorithm[J].Proceedings of the CSEE,2011,31(19):17-24.(in Chinese)
21 HU Y,BIE Z,DING T,et al.An NSGA-II based multi-objective optimization for combined gas and electricity network expansion planning[J].Applied Energy,2016,167:280-293.
22姜惠兰,安星,王亚微,等.基于改进NSGA2算法的考虑风机接入电能质量的多目标电网规划[J].中国电机工程学报,2015,35(21):5405-5411.JIANG Huilan,AN Xing,WANG Yawei,et al.Improved NSGA2 algorithm based multi-objective planning of power grid with wind farm considering power quality[J].Proceedings of the CSEE,2015,35(21):5405-5411.(in Chinese)
23 ABUL'WAFA A R.Optimization of economic/emission load dispatch for hybrid generating systems using controlled Elitist NSGA-Ⅱ[J].Electric Power Systems Research,2013,105(6):142-151.
24盛万兴,叶学顺,刘科研,等.基于NSGA-Ⅱ算法的分布式电源与微电网分组优化配置[J].中国电机工程学报,2015,35(18):4655-4662.SHENG Wanxing,YE Xueshun,LIU Keyan,et al.Optimal allocation between distributed generations and microgrid based on NSGA-Ⅱalgorithm[J].Proceedings of the CSEE,2015,35(18):4655-4662.(in Chinese)
25乔俊飞,魏静,韩红桂.基于改进NSGA2算法的给水管网多目标优化设计[J].控制工程,2016,23(12):1861-1866.QIAO Junfei,WEI Jing,HAN Honggui.Multi-objective optimization of water distribution system based on an improved NSGA2algorithm[J].Control Engineering of China,2016,23(12):1861-1866.(in Chinese)
26王春林,叶剑,曾成,等.基于NSGA-Ⅱ遗传算法高比转速混流泵多目标优化设计[J].农业工程学报,2015,31(18):100-106.WANG Chunlin,YE Jian,ZENG Cheng,et al.Multi-objective optimum design of high specific speed mixed-flow pump based on NSGA-Ⅱgenetic algorithm[J].Transactions of the CSAE,2015,31(18):100-106.(in Chinese)
27王会方,朱世强,吴文祥.基于INSGA-Ⅱ算法的机械手多目标轨迹规划[J].浙江大学学报(工学版),2012,46(4):622-628.WANG Huifang,ZHU Shiqiang,WU Wenxiang.INSGA-Ⅱbased multi-objective trajectory planning for manipulators[J].Journal of Zhejiang University(Engineering Science),2012,46(4):622-628.(in Chinese)
28刘孟楠,周志立,徐立友,等.基于多性能目标的拖拉机运输机组优化设计[J].农业工程学报,2017,33(8):62-68.LIU Mengnan,ZHOU Zhili,XU Liyou,et al.Multi-objective optimization and design of tractor trailer systems[J].Transactions of the CSAE,2017,33(8):62-68.(in Chinese)
29谢承旺,李凯,廖国勇.一种带差分局部搜索的改进型NSGA2算法[J].计算机科学,2013,40(10):235-237,238.XIE Chengwang,LI Kai,LIAO Guoyong.Improved NSGA2 algorithm with differential evolution local search[J].Computer Science,2013,40(10):235-237,238.(in Chinese)
30席志强.拖拉机动力换挡变速器控制系统研究[D].西安:西安理工大学,2016.XI Zhiqiang.Study on control system of tractor power-shift transmission[D].Xi'an:Xi'an University of Technology,2016.(in Chinese)
2机械电子工业部洛阳拖拉机研究所.拖拉机设计手册[M].北京:机械工业出版社,1994.
3宋强,叶山顶,李伟聪,等.基于多目标遗传算法的纯电动汽车AMT综合换挡规律研究[J].汽车工程学报,2017,7(1):44-51.SONG Qiang,YE Shanding,LI Weicong,et al.Research on AMT overall shift schedule for pure electric vehicles based on NSGA-Ⅱalgorithm[J].Chinese Journal of Automotive Engineering,2017,7(1):44-51.(in Chinese)
4李军.基于Cruise的某型大客车动力传动系统优化匹配研究[D].长春:吉林大学,2013.LI Jun.Research on matching of powertrain for coach based on Cruise[D].Changchun:Jilin University,2013.(in Chinese)
5 ZHOU X,QIN D,HU J,et al.Multi-objective optimization design and performance evaluation for plug-in hybrid electric vehicle powertrains[J].Applied Energy,2017,208:1608-1625.
6 FOFANA A,HAAS O,ERSANILLI V,et al.Multi-objective genetic algorithm for an automatic transmission gear shift map[J].IFAC-PapersOnLine,2016,49(3):123-128.
7张红.重型汽车动力传动系参数匹配优化[D].淄博:山东理工大学,2012.ZHANG Hong.Matching optimization of powertrain system parameters for heavy vehicle[D].Zibo:Shandong University of Technology,2012.(in Chinese)
8张抗抗,徐梁飞,华剑锋,等.基于多目标优化的纯电动车动力系统参数匹配方法[J].汽车工程,2015,37(7):757-765.ZHANG Kangkang,XU Liangfei,HUA Jianfeng,et al.A parameter matching method for the powertrain of battery electric vehicle based on multi-objective optimization[J].Automotive Engineering,2015,37(7):757-765.(in Chinese)
9程方晓.基于自适应保持多样性遗传算法的汽车动力传动系多目标优化[D].长春:吉林大学,2011.CHENG Fangxiao.Multi-objective optimization of automotive powertrain based on adaptive maintain diversity genetic algorithm[D].Changchun:Jilin University,2011.(in Chinese)
10雷嗣军.汽车动力传动系参数匹配与仿真优化[D].杭州:浙江大学,2010.LEI Sijun.Parameters matching and simulation optimization of automobile transmission[D].Hangzhou:Zhejiang University,2010.(in Chinese)
11 ZHANG P,CHEN Y,LIN M,et al.Optimum matching of electric vehicle powertrain[J].Energy Procedia,2016,88:894-900.
12王粉粉.拖拉机电控机械式自动变速系统设计研究[D].洛阳:河南科技大学,2014.WANG Fenfen.Study on the design for automated mechanical transmission system of tractor[D].Luoyang:Henan University of Science and Technology,2014.(in Chinese)
13叶素娣,徐敬华.精密变位斜齿轮行星轮系混合型变量多目标优化设计[J].机械设计,2016,33(7):98-104.YE Sudi,XU Jinghua.Multi-objective optimization design of mixed variables on precision variable helical planetary gear train[J].Journal of Machine Design,2016,33(7):98-104.(in Chinese)
14叶秉良,俞高红,赵匀.拖拉机最终传动多目标模糊可靠性优化设计[J].农业机械学报,2010,41(4):95-100.YE Bingliang,YU Gaohong,ZHAO Yun.Multi-objective fuzzy reliability optimization design of tractor's final transmission[J].Transactions of the Chinese Society for Agricultural Machinery,2010,41(4):95-100.(in Chinese)
15 ZHOU A,QU B Y,LI H,et al.Multi-objective evolutionary algorithms:a survey of the state of the art[J].Swarm and Evolutionary Computation,2011,1(1):32-49.
16 TAN K C,LEE T H,KHOR E F.Evolutionary algorithms for multi-objective optimization:performance assessments and comparisons[J].Artificial Intelligence Review,2002,17(4):251-290.
17 ELBELTAGI E,HEGAZY T,GRIERSON D.Comparison among five evolutionary-based optimization algorithms[J].Advanced Engineering Informatics,2005,19(1):43-53.
18 DEB K,PRATAP A,AGARWAL S,et al.A fast and elitist multi-objective genetic algorithm:NSGA-Ⅱ[J].IEEE Transactions on Evolutionary Computation,2002,6(2):182-197.
19 MOSBAH M,ARIF S,MOHAMMEDI R D.Multi-objective optimization for optimal multi DG placement and sizes in distribution network based on NSGA-Ⅱand fuzzy logic combination[C]∥International Conference on Electrical Engineering-Boumerdes,2017:1-6.
20王茜,张粒子.采用NSGA-Ⅱ混合智能算法的风电场多目标电网规划[J].中国电机工程学报,2011,31(19):17-24.WANG Qian,ZHANG Lizi.Multi-objective transmission planning associated with wind farms applying NSGA-Ⅱhybrid intelligent algorithm[J].Proceedings of the CSEE,2011,31(19):17-24.(in Chinese)
21 HU Y,BIE Z,DING T,et al.An NSGA-II based multi-objective optimization for combined gas and electricity network expansion planning[J].Applied Energy,2016,167:280-293.
22姜惠兰,安星,王亚微,等.基于改进NSGA2算法的考虑风机接入电能质量的多目标电网规划[J].中国电机工程学报,2015,35(21):5405-5411.JIANG Huilan,AN Xing,WANG Yawei,et al.Improved NSGA2 algorithm based multi-objective planning of power grid with wind farm considering power quality[J].Proceedings of the CSEE,2015,35(21):5405-5411.(in Chinese)
23 ABUL'WAFA A R.Optimization of economic/emission load dispatch for hybrid generating systems using controlled Elitist NSGA-Ⅱ[J].Electric Power Systems Research,2013,105(6):142-151.
24盛万兴,叶学顺,刘科研,等.基于NSGA-Ⅱ算法的分布式电源与微电网分组优化配置[J].中国电机工程学报,2015,35(18):4655-4662.SHENG Wanxing,YE Xueshun,LIU Keyan,et al.Optimal allocation between distributed generations and microgrid based on NSGA-Ⅱalgorithm[J].Proceedings of the CSEE,2015,35(18):4655-4662.(in Chinese)
25乔俊飞,魏静,韩红桂.基于改进NSGA2算法的给水管网多目标优化设计[J].控制工程,2016,23(12):1861-1866.QIAO Junfei,WEI Jing,HAN Honggui.Multi-objective optimization of water distribution system based on an improved NSGA2algorithm[J].Control Engineering of China,2016,23(12):1861-1866.(in Chinese)
26王春林,叶剑,曾成,等.基于NSGA-Ⅱ遗传算法高比转速混流泵多目标优化设计[J].农业工程学报,2015,31(18):100-106.WANG Chunlin,YE Jian,ZENG Cheng,et al.Multi-objective optimum design of high specific speed mixed-flow pump based on NSGA-Ⅱgenetic algorithm[J].Transactions of the CSAE,2015,31(18):100-106.(in Chinese)
27王会方,朱世强,吴文祥.基于INSGA-Ⅱ算法的机械手多目标轨迹规划[J].浙江大学学报(工学版),2012,46(4):622-628.WANG Huifang,ZHU Shiqiang,WU Wenxiang.INSGA-Ⅱbased multi-objective trajectory planning for manipulators[J].Journal of Zhejiang University(Engineering Science),2012,46(4):622-628.(in Chinese)
28刘孟楠,周志立,徐立友,等.基于多性能目标的拖拉机运输机组优化设计[J].农业工程学报,2017,33(8):62-68.LIU Mengnan,ZHOU Zhili,XU Liyou,et al.Multi-objective optimization and design of tractor trailer systems[J].Transactions of the CSAE,2017,33(8):62-68.(in Chinese)
29谢承旺,李凯,廖国勇.一种带差分局部搜索的改进型NSGA2算法[J].计算机科学,2013,40(10):235-237,238.XIE Chengwang,LI Kai,LIAO Guoyong.Improved NSGA2 algorithm with differential evolution local search[J].Computer Science,2013,40(10):235-237,238.(in Chinese)
30席志强.拖拉机动力换挡变速器控制系统研究[D].西安:西安理工大学,2016.XI Zhiqiang.Study on control system of tractor power-shift transmission[D].Xi'an:Xi'an University of Technology,2016.(in Chinese)