Determination of Maximum Possible Fuel Economy of HEV for Known Drive Cycle: Genetic Algorithm Based Approach
Determination of Maximum Possible Fuel Economy of HEV for Known Drive Cycle: Genetic Algorithm Based Approach
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确定已知驾驶循环下混合动力汽车最大可能燃油经济性：基于遗传算法的方法
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发表时间：
2008
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通讯作者：
S. Karunarathna
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文献类型：
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作者：
R. Wimalendra;L. Udawatta;E. Edirisinghe;S. Karunarathna
本文描述了一种方法来研究混合动力汽车在一个已知的驱动循环下，并联配置所能达到的最大燃油经济性。采用回溯式混合动力汽车模型进行燃油经济性计算。优化过程是一个约束、多域、时变问题，具有高度非线性。在此，采用基于遗传算法（GA）的方法，找出两个动力源在其行驶循环中的最佳功率分配，使车辆在给定的行驶循环中获得最大的整体燃油经济性。该方法采用并联混合动力汽车（PHEV）配置，制定了以总油耗最小为目标的优化问题。整个驱动周期的电机功率贡献被编码为染色体。这些结果代表了混合动力电动汽车的任何电源管理系统在经过测试的HEV配置下所能达到的最大燃油经济性，并将为燃油经济性的测量设定一个基准。
This paper describes a methodological approach to investigate the maximum fuel economy that could be achieved by a hybrid vehicle with parallel configuration for a known drive cycle. A backward looking hybrid vehicle model is used for computation of fuel economies. The optimization process represents a constrained, multi-domain and time-varying problem, which is highly nonlinear. Here, genetic algorithm (GA) based approach was used to find out optimum power split between two power sources over their driving cycles that make maximum possible overall fuel economy for the given drive cycle by the vehicle. In this approach using Parallel Hybrid Electric Vehicle (PHEV) configuration, optimization problem is formulated so as to minimize the overall fuel consumption. The whole set of electric motor power contribution along the drive cycle is then coded as the chromosomes. These results represent the maximum fuel economy that could be ever achieved by any power management system of a Hybrid Electric Vehicle, with the tested HEV configuration and shall allow setting a benchmark against which the fuel economy is measured.