[1]许斌杰,王耀南.萤火虫算法的电动汽车综合成本运行优化研究[J].智能系统学报,2017,12(2):166-171.[doi:10.11992/tis.200603024]
XU Binjie,WANG Yaonan.Optimizing the composite cost of electric vehicles based on the firefly optimization model[J].CAAI Transactions on Intelligent Systems,2017,12(2):166-171.[doi:10.11992/tis.200603024]
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XU Binjie,WANG Yaonan.Optimizing the composite cost of electric vehicles based on the firefly optimization model[J].CAAI Transactions on Intelligent Systems,2017,12(2):166-171.[doi:10.11992/tis.200603024]
萤火虫算法的电动汽车综合成本运行优化研究
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
12
期数:
2017年第2期
页码:
166-171
栏目:
学术论文—机器学习
出版日期:
2017-05-05
- Title:
- Optimizing the composite cost of electric vehicles based on the firefly optimization model
- Keywords:
- electric vehicle; fuel consumption; emission; cost; engine; range extender
- 分类号:
- TP27;U469.7
- 摘要:
- 为实现节能减排，文章以增程式电动汽车为研究对象，提出了一种基于动态综合成本的增程器运行优化方法。首先以增程器发动机外特性为研究基础，根据实际工作状况分别建立了发动机燃油消耗率及CO排放率模型，再通过归一化后将多个目标加权求和的方法建立电动汽车综合成本运行优化模型。模型建立后，在全局优化及特定功率优化这两种常见模式下以萤火虫算法进行寻优，最后在不同的权重条件下得出最佳综合成本运行曲线。实验结果表明，文章提出的方法能够在不同的运行环境下通过动态调整权重值，实现基于燃油消耗率及CO排放的综合成本运行优化。
- Abstract:
- To achieve savings in energy and reductions of emissions, we propose a process operation optimization method based on dynamic comprehensive cost for range-extending electric vehicles. With the external characteristics of range-extending engines as the basis of our research, we first established engine fuel consumption and carbon monoxide (CO) emission rate models according to actual working conditions. Next, through normalization, we developed an operation optimization model of comprehensive cost for the electric vehicle by using a multi-goal weighted summation method. With our optimization model, we used the firefly algorithm to find the optimal operation value using the two patterns of global optimization and specific power optimization. Finally, we obtained the operation curve corresponding to the optimal comprehensive cost under different weight conditions. Our experimental results show that our proposed method can dynamically adjust the weight value in different operating environments, thus optimizing comprehensive cost based on both fuel consumption and CO emissions.
- 参考文献/References:
-
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[3] 闵海涛, 叶冬金, 于远彬. 增程式电动汽车控制策略的优化[J]. 汽车工程, 2014, 36(8): 899-903, 943. MIN Haitao, YE Dongjin, YU Yuanbin. Optimization of the control strategy for range extended electric vehicle[J]. Automotive engineering, 2014, 36(8): 899-903, 943.
[4] SUN Dongye, LIN Xinyou, QIN Datong, et al. Power-balancing instantaneous optimization energy management for a novel series-parallel hybrid electric bus[J]. Chinese journal of mechanical engineering, 2012, 25(6): 1161-1170.
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[6] 陈光, 毛召磊, 李济沅, 等. 计及碳排放的电动汽车充电站多目标规划[J]. 电力系统自动化, 2014, 38(17): 49-53, 136. CHEN Guang, MAO Zhaolei, LI Jiyuan, et al. Multi-objective optimal planning of electric vehicle charging stations considering carbon emission[J]. Automation of electric power systems, 2014, 38(17): 49-53, 136.
[7] 王军方, 付明亮, 丁焰, 等. 低速货车的污染物排放特性[J]. 农业工程学报, 2014, 30(14): 57-63. WANG Junfang, FU Mingliang, DING Yan, et al. Emissions characteristics of low speed vehicles[J]. Transactions of the Chinese society of agricultural engineering, 2014, 30(14): 57-63.
[8] 拓守恒. 一种优化神经网络的教与学优化算法[J]. 智能系统学报, 2013, 8(4): 327-332. TUO Shouheng. A modified teaching-learning-based optimization algorithm and application in neural networks[J]. CAAI transactions on intelligent systems, 2013, 8(4): 327-332.
[9] 卢嘉敏, 杨宜民, 付根平. 基于鱼群算法的仿人机器人步态优化[J]. 智能系统学报, 2013, 8(6): 526-531. LU Jiamin, YANG Yimin, FU Genping. Research on the gait optimization of humanoid robots based on the fish swarm algorithm[J]. CAAI transactions on intelligent systems, 2013, 8(6): 526-531.
[10] 曾冰, 李明富, 张翼, 等. 基于萤火虫算法的装配序列规划研究[J]. 机械工程学报, 2013, 49(11): 177-184. ZENG Bing, LI Mingfu, ZHANG Yi, et al. Research on assembly sequence planning based on firefly algorithm[J]. Journal of mechanical engineering, 2013, 49(11): 177-184.
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[1]夏飞,马茜,张浩,等.改进D-S证据理论在电动汽车锂电池故障诊断中的应用[J].智能系统学报,2017,12(4):526.[doi:10.11992/tis.201605001]
XIA Fei,MA Xi,ZHANG Hao,et al.Application of improved D-S evidence theory in fault diagnosis of lithium batteries in electric vehicles[J].CAAI Transactions on Intelligent Systems,2017,12():526.[doi:10.11992/tis.201605001]
备注/Memo
收稿日期:2016-3-15;改回日期:。
基金项目:国家“863”计划项目（2012AA111004）； 国家自然科学基金项目（61104088）．
作者简介:许斌杰,男,1989年生,硕士研究生,主要研究方向为电动汽车控制技术;王耀南,男,1957,教授,博士生导师,主要研究方向为电动汽车控制、智能控制理论与应用、智能机器人。技术成果曾获国家科技进步二等奖、中国发明创业特等奖、省部级科技进步一等奖、省部级科技进步二等奖。发表学术论文360余篇,其中SCI收录38篇、SCI引用175篇次、EI收录109篇,获国家专利12项。出版学术专著《智能控制系统》、《机器人智能控制工程》、《智能信息处理技术》、《计算机图像处理与识别技术》、《计算智能方法与应用》等。
通讯作者:许斌杰. E-mail:xubinjie@hnu.edu.cn.
更新日期/Last Update:
1900-01-01