基于AMGA算法的增程电动客车经济性优化

Q4 Engineering

International Journal of Vehicle Systems Modelling and Testing Pub Date : 2020-07-21 DOI:10.1504/ijvsmt.2020.10030670

Zhao Yunfei, Guo Ronghui, Fangwu Ma, Song Jinlong

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引用次数: 1

摘要

增程电动巴士的动力来自电池和增程器。如何设计车辆在行驶过程中的增程器工作点是实现节能减排的关键因素。为了解决这一问题，利用AVL巡航仿真软件建立了车辆模型。通过Cruise和Isight联合仿真优化，建立了百公里油耗和污染物排放的多目标优化模型。最优变量包括动力单元的上下限和增程器的工作点。采用自适应突变遗传算法(AMGA)作为优化算法。结果表明，该系统有效降低了燃油消耗和污染物排放。百公里油耗下降48.0%，一氧化碳排放量下降49.6%，碳氢化合物排放量下降47.28%，氮氧化物排放量下降51.1%。增程式电动巴士的经济性已大大提高。

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Economic optimisation of range-extended electric bus based on AMGA algorithm

The power of a range-extended electric bus comes from its battery and range-extender. How to design the range-extender working point for the vehicle in the process of running is the key factor to achieve energy conservation and emission reduction. To solve this problem, a vehicle model was built by using AVL Cruise simulation software. Through Cruise and Isight co-simulation optimisation, a multi-objective optimisation model for per 100-km fuel consumption and pollutant emission is established. Optimal variables include upper and lower limits of the power unit and working point of the range-extender. Adaptive mutation genetic algorithm (AMGA) was used as optimisation algorithm. Results showed that fuel consumption and pollutant emissions were effectively reduced. The per 100-km fuel consumption decreased by 48.0%, carbon monoxide emission decreased by 49.6%, hydrocarbon emission decreased by 47.28%, and nitrogen oxide emission decreased by 51.1%. The economics of range-extended electric bus have been greatly improved.

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来源期刊

International Journal of Vehicle Systems Modelling and Testing Engineering-Automotive Engineering

CiteScore

0.50

自引率

0.00%

发文量

期刊介绍： IJVSMT provides a resource of information for the scientific and engineering community working with ground vehicles. Emphases are placed on novel computational and testing techniques that are used by automotive engineers and scientists.