首页> 外文期刊>Computers & Industrial Engineering >The optimal economic design of the wireless powered intelligent transportation system using genetic algorithm considering nonlinear cost function
【24h】

The optimal economic design of the wireless powered intelligent transportation system using genetic algorithm considering nonlinear cost function

机译:考虑非线性成本函数的遗传算法无线智能交通系统的最优经济设计

获取原文
获取原文并翻译 | 示例
           

摘要

We present a new type of electric vehicle called the On-Line Electric Vehicle (OLEV™), developed by the Korea Advanced Institute of Science and Technology (KAIST). The OLEV uses an innovative wireless charging technology that enables the battery in the vehicle to be charged through a charging infrastructure installed under the road. The vehicle can be charged while stationary or moving. The OLEV is considered a revolutionary transport solution, as it overcomes the problems facing conventional battery-powered electric vehicles, such as long charging times and the need to stop frequently to charge. Several commercial versions of the OLEV have been successfully deployed, including the trolleys serving in Seoul Grand Park and the KAIST campus shuttles. In this paper, we propose a mathematical model to optimally allocate the charging infrastructure on the route, and to determine the vehicles battery size. The model is specifically concerned with the OLEV system applied to mass transport buses such as those used in Seoul Grand Park and on the KAIST campus. This paper deals with the optimization problem considering nonlinear cost function where the cost of the power transmitter is nonlinear. We propose Genetic Algorithms (GAs) as the solution methodology for this problem.
机译:我们介绍了一种由韩国高级科学技术研究院(KAIST)开发的新型电动汽车,称为在线电动汽车(OLEV™)。 OLEV使用创新的无线充电技术,可通过安装在道路下方的充电基础设施为车辆中的电池充电。车辆静止或移动时均可充电。 OLEV被认为是一种革命性的运输解决方案,因为它克服了传统的电池供电电动汽车所面临的问题,例如较长的充电时间和需要频繁停止充电的问题。 OLEV的几种商业版本已经成功部署,包括在首尔大公园服务的手推车和KAIST校园班车。在本文中,我们提出了一个数学模型来优化路线上的充电基础设施,并确定车辆的电池容量。该模型特别涉及应用于大规模运输巴士的OLEV系统,例如在首尔大公园和KAIST校园中使用的那些。本文考虑了考虑非线性成本函数的优化问题,其中功率发送器的成本是非线性的。我们提出了遗传算法(GAs)作为解决此问题的方法。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号