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    • Optimization of Driving Cycle Development Based on Multi-Objective Genetic Algorithm
    DOI：10.13949/j.cnki.nrjgc.2023.05.008
    Key Words:driving cycle  multi-objective optimization  genetic algorithm（GA）  non-dominated sorting genetic algorithm Ⅱ（NSGA-Ⅱ）  micro-trip
    Author NameAffiliationE-mail
    YU Yefeng* China-UK Low Carbon College， Shanghai Jiao Tong University， Shanghai 200240， China leafyu@sjtu.edu.cn 
    ZHANG Chen College of Smart Energy， Shanghai Jiao Tong University， Shanghai 200240， China chenzhang87@sjtu.edu.cn 
    GAO Zhan China-UK Low Carbon College， Shanghai Jiao Tong University， Shanghai 200240， China
    School of Mechanical Engineering， Shanghai Jiao Tong University， Shanghai 200240， China     		gaozhan@sjtu.edu.cn 
    WU Chenbo School of Mechanical Engineering， Shanghai Jiao Tong University， Shanghai 200240， China
    China Automotive Engineering Research Institute Co.， Ltd.， Chongqing 401122， China     		wuchenbo@caeri.com.cn 
    ZHU Lei* School of Mechanical Engineering， Shanghai Jiao Tong University， Shanghai 200240， China tonyzhulei@sjtu.edu.cn 
    HUANG Zhen College of Smart Energy， Shanghai Jiao Tong University， Shanghai 200240， China
    School of Mechanical Engineering， Shanghai Jiao Tong University， Shanghai 200240， China     		z-huang@sjtu.edu.cn 
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    Abstract:In the construction of driving cycles using micro-trip method， to comprehensively consider the typicality of micro-trips and the representativeness of driving cycles， the typicality and representatives were quantified and used as two objective functions in the multi-objective optimization process， and the non-dominated sorting genetic algorithm Ⅱ（NSGA-Ⅱ） was introduced to optimize the selection of micro-trips. The NSGA-Ⅱ optimization method was compared with the commonly used micro-trip selection method， and 7 582， 7 209， 9 615 and 20 candidate driving cycles were constructed by the random selection method， correlation method， distance method and NSGA-Ⅱ， respectively， in the same time. The results show that， among the four methods， on the whole， the driving cycles generated by NSGA-Ⅱ are concentrated in areas with high representativeness while making the micro-trips constituting the driving cycles more typical in general. In the comparison of the optimal cycles， the optimal cycles of NSGA-Ⅱ have the smallest relative errors in the characteristic parameters with the original data and contain the micro-trips to the closest average distance from the cluster center， indicating that the optimization of micro-trip selection process using the NSGA-Ⅱ algorithm helps to improve the quality of the construction of driving cycles from multiple perspectives.
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