• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Applied Soft Computing >Optimal driving based trip planning of electric vehicles using evolutionary algorithms: A driving assistance system
【24h】

Optimal driving based trip planning of electric vehicles using evolutionary algorithms: A driving assistance system

机译：使用进化算法的电动汽车最佳驾驶跳闸规划：驾驶辅助系统

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The existing driving assistance systems (DAS) are not capable to manage the electric vehicle (EV) problems namely insufficiency of charging stations and inadequate range. A novel DAS is presented here to extend the range and overcome other EV drawbacks by suggesting the driver an optimal driving strategy (ODS) continuously throughout trip performing. ODS is decided by solving a multi-objective optimization problem (MOOP), subsequently adopting a multi-criterion decision making technique. Implementation of the DAS in real application requires both better optimization results and low computational time. A study was carried out to investigate the DAS performance with four contending evolutionary algorithms (EAs), NSGAII (a non-dominated sorting multi-objective genetic algorithm), PESA (Pareto envelope-based selection algorithm), PAES (Pareto archived evolution strategy), and SPEA 2 (Strength Pareto evolutionary algorithm). After an initial investigation of EA performances based on different matrices, NSGAII and PESA were found to be most suitable. The natures of decision variables in the Pareto-optimal solutions were analyzed. After an extensive analysis based on different micro-trip structures, it was found that without considering the computational time, PESA solutions possess better convergence and diversity properties than NSGAII solutions. Various approaches were adopted to minimize DAS computation time considering both NSGAII and PESA without significantly compromising the solution's optimality. (C) 2020 Elsevier B.V. All rights reserved.
机译：现有的驾驶辅助系统（DAS）能够管理电动车辆（EV）问题，即充电站的不足，并且范围不足。这里提出了一种新的DAS来扩展范围，并通过在整个行程中连续地促进驾驶员在整个行程中连续地进行最佳驾驶策略（ODS）来克服其他EV缺点。通过解决多目标优化问题（MOOP）来决定ODS，随后采用多标准决策技术。实际应用中DAS的实施需要更好的优化结果和低计算时间。进行了研究，以研究具有四个竞争进化算法（EAS），NSGaii（非主导排序多目标遗传算法），PESA（Pareto信封的选择算法），PAES（Pareto存档演进策略）进行研究。和SPEA 2（强度帕曲型进化算法）。在基于不同矩阵的初步调查基于不同矩阵的EA表演之后，发现NSGaii和PESA最合适。分析了帕累托 - 最佳解决方案中的决策变量的自然。在基于不同的微旅行结构的广泛分析之后，发现在不考虑计算时间，PESA解决方案比NSGaii解决方案具有更好的收敛和分集特性。采用各种方法来最小化考虑NSGaii和PESA的DAS计算时间，而不是显着影响解决方案的最优性。 （c）2020 Elsevier B.V.保留所有权利。

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