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首页> 外文期刊>International Journal of Swarm Intelligence and Evolutionary Computation >Application of a Pheromone-Based Bees Algorithm for Simultaneous Optimisation of Key Component Sizes and Control Strategy for Hybrid Electric Vehicles
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Application of a Pheromone-Based Bees Algorithm for Simultaneous Optimisation of Key Component Sizes and Control Strategy for Hybrid Electric Vehicles

机译：基于信息素的蜜蜂算法同时优化混合动力汽车关键部件尺寸和控制策略的应用

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APheromone-Based Bees Algorithm (PBA) is employed to optimize the key component sizes and control strategy for parallel Hybrid Electric Vehicles (parallel HEVs) presented. The Basic Bees Algorithm (BBA) is an intelligent optimization tool mimicking the food foraging behavior of honey bees. In this research, however, a new version of BBA which uses pheromones, chemical substances secreted by bees and other insects into their environment, enabling them to communicate with other members of their own species, is applied. The PBA employs the pheromone to attract bees to explore the promising regions of the search space, and the parallel HEV configuration and an Electric Assist Control Strategy are used to formulate the research. The value of the key component size and control strategy parameters is adjusted according to PBA to obtain the minimization of weighted sum of Fuel Consumption (FC) and emissions while vehicle performance that satisfy the PNGV constraints. In this research, ADVISOR software has been used as the simulation tool, and driving cycles, FTP, ECE-EUDC and UDDS, are employed to evaluate FC, emissions and dynamic performances. Following a description of the algorithm, the paper shows the results obtained for the simultaneous optimization of key component sizes and control strategy for parallel Hybrid Electric Vehicles. The results prove that PBA is a strong algorithm for determining the optimal parameters of component sizes and control strategy resulting in improvement of FC and emissions without sacrificing vehicle performance. Compared to BBA, the new version, PBA, showed an improvement of about 25% in convergence speed with the nearly same results of optimization targets.
机译：基于APheromone的蜜蜂算法（PBA）用于优化提出的并联混合动力电动汽车（parallel HEV）的关键部件尺寸和控制策略。基本蜜蜂算法（BBA）是模仿蜜蜂的食物觅食行为的智能优化工具。但是，在这项研究中，使用了一种新版的BBA，它使用信息素，蜜蜂和其他昆虫分泌的化学物质进入环境，从而使它们能够与自己物种的其他成员进行交流。 PBA利用信息素吸引蜜蜂来探索搜索空间中有希望的区域，并使用并行的HEV配置和电动辅助控制策略来制定研究。根据PBA调整关键部件尺寸和控制策略参数的值，以使满足PNGV约束的车辆性能中的燃油消耗（FC）和排放的加权总和最小。在这项研究中，ADVISOR软件被用作仿真工具，并使用行驶周期FTP，ECE-EUDC和UDDS来评估FC，排放和动态性能。在对算法进行描述之后，本文显示了同时优化并联混合电动汽车关键部件尺寸和控制策略的结果。结果证明，PBA是确定组件尺寸和控制策略的最佳参数的强大算法，可在不牺牲车辆性能的情况下改善FC和排放。与BBA相比，新版本PBA的收敛速度提高了约25％，优化目标的结果几乎相同。

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