Optimal fuel control of series-parallel input split hybrid electric vehicle using genetic algorithm based control strategy

A. Panday, H. Bansal
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引用次数: 4

Abstract

The present transportation system heavily relies on of internal combustion engine (ICE) based vehicles. These vehicles emit toxic gases, which results in environmental pollution and create massive health problem. For energy security and greener tomorrow, the concept of hybrid vehicle came into existence. Hybrid vehicles consist of alternative energy storages like fuel-cell, super capacitor, battery or hybrid storage. The presence of two power sources, i.e., engine and battery, makes it necessary to intelligently split the power between them to minimize the fuel consumption. An intelligent controller should be used to split the on road power demand for optimum fuel economy. This article applies a genetic algorithm based controller to toggle between engine and battery. The optimization is based on the selection of vital parameters such as state of charge in the battery, engine on time and power demand. The authenticity and feasibility of proposed controller are verified extensively through numerous simulation results.
基于遗传算法的串并联分路混合动力汽车燃油优化控制
当前的交通运输系统严重依赖内燃机车辆。这些车辆排放有毒气体,造成环境污染和严重的健康问题。为了能源安全和更环保的明天,混合动力汽车的概念应运而生。混合动力汽车由燃料电池、超级电容器、电池或混合存储等替代能源存储组成。由于存在发动机和电池两种动力源,因此有必要在两者之间进行智能分配,以最大限度地减少燃料消耗。为了达到最佳的燃油经济性,需要使用智能控制器来分割道路动力需求。本文采用基于遗传算法的控制器实现发动机与电池的切换。优化是基于电池充电状态、发动机开机时间和功率需求等关键参数的选择。通过大量的仿真结果,广泛验证了所提控制器的真实性和可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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