Optimal drivetrain component sizing for a Plug-in Hybrid Electric transit bus using Multi-Objective Genetic Algorithm

Chirag Desai, F. Berthold, S. Williamson
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引用次数: 20

Abstract

Plug-in Hybrid Electric Vehicles (PHEVs) can significantly reduce petroleum consumption and the only difference from hybrid electric vehicles (HEVs) is the ability of PHEVs to use off-board electricity generation to recharge their energy storage system. The fuel economy of PHEV is highly dependent on All-Electric-Range (AER), drivetrain component size and control strategy parameter. In this study we consider PHEV version of parallel hybrid NOVA transit bus model developed with the Powertrain System Analysis Toolkit (PSAT).A genetic based derivative free algorithm called Multi-Objective Genetic Algorithm (MOGA) is used to optimize conflicting drivetrain and control strategy parameters. The AER, fuel economy, emissions and main performance constraints of the PHEVs will be compared for the initial design and final optimal design.
基于多目标遗传算法的插电式混合动力公交传动系统部件尺寸优化
插电式混合动力汽车(phev)可以显著降低石油消耗,与混合动力汽车(hev)的唯一区别是phev能够使用车载发电为其储能系统充电。插电式混合动力汽车的燃油经济性在很大程度上取决于全电续航里程(AER)、传动系统部件尺寸和控制策略参数。在本研究中,我们考虑使用动力总成系统分析工具包(PSAT)开发的PHEV版本并联混合动力NOVA公交模型。采用一种基于遗传的无导数多目标遗传算法(MOGA)来优化冲突的传动系统和控制策略参数。在初始设计和最终优化设计中,将比较插电式混合动力汽车的AER、燃油经济性、排放和主要性能约束。
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