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-  2015 

采用NSGA-II算法的混合动力能量管理控制 多目标优化方法
A Multi??Objective Optimization Method for Energy Management Control of Hybrid Electric Vehicles Using NSGA??II Algorithm

DOI: 10.7652/xjtuxb201510023

Keywords: 混合动力,能量管理,Pareto最优解,NSGA-II算法,多目标优化
hybrid power
,energy management,Pareto optimal solution,NSGA??II algorithm,multi??objective optimization

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Abstract:

综合考虑燃油经济性、排放性与驾驶性对混合动力能量管理控制优化的优点,以某款并联混合动力汽车为研究对象,选取能量管理控制参数与传动系参数作为待优化参数,以动力性作为约束条件,建立混合动力能量管理控制多目标优化评价方法,提出基于NSGA??II算法的混合动力系统多目标优化方法,并与优化前控制策略进行仿真对比分析。结果表明:在满足基本约束的前提下,优化后燃油经济性最多提高了7.8%,平均提高了6.38%;驾驶性性能指标最多提高了27??12%,平均提高了21.74%;排放性综合指标平均提高了41.51%。提出的多目标优化算法具有良好的收敛性与分布性,得到的Pareto最优解集能够给混合动力能量管理控制策略提供更多的权衡选择方案,体现了多目标优化的优势。
A multi??objective optimization evaluation method for hybrid electric vehicle (HEV) is proposed by comprehensively considering the influences of fuel economy, emission and drivability on the energy management control for HEV. The multi??objective optimization algorithm based on NSGA??II (non??dominated sorting genetic algorithm??II) is established by setting the parameters of the energy management control and the driveline system as the optimal parameters for the parallel hybrid electric vehicles, and the dynamic performance as the constraint condition. Then the proposed method is comparatively analyzed with the traditional control strategy that only considers the fuel economy. Simulation results show that the maximum fuel economy performance increases by 7.8% and the average value increases by 6.38%; the maximum drivability performance increases by 42.28% and the average value increases by 21??74%; the average synthetic emission performance increases by 41.51%. The proposed multi??objective optimization algorithm has good convergence and distribution. The obtained Pareto optimum solutions may provide more trade??off options for HEV energy management control strategy, which reflect the advantages of multi??objective optimization

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