基于遗传算法的电动重卡充换电站充电策略优化  

Optimization of charging strategy for electric heavy truck charging and swapping stations based on genetic algorithm

作　　者：王博 杨克南 杨迎春 王少鹏 韩锦峰 WANG Bo;YANG Ke’nan;YANG Yingchun;WANG Shaopeng;HAN Jinfeng(XJ Electric Co.,Ltd,Xuchang,He’nan 461000;Xuchang Xuji Software Technology Co.,Ltd,Xuchang,He’nan 461000;College of Materials Science and Engineering,Zhengzhou University,Zhengzhou 450001)

机构地区：[1]许继电气股份有限公司,河南许昌461000 [2]许昌许继软件技术有限公司,河南许昌461000 [3]郑州大学材料科学与工程学院,郑州450001

出　　处：《电气技术》2025年第3期36-41,48,共7页Electrical Engineering

摘　　要：电动重卡充换电站快速发展,电池充电策略对站端运营成本和用户换电体验具有重要影响。如何在满足电动重卡用户日常换电需求的同时,最大限度地降低站端运营成本并缩短用户换电等待时间,是一个重点研究方向。首先,以某电动重卡充换电站为研究对象,使用统计分析方法,得到一天内各时段的用户换电需求;其次,提出以降低站端电池充电成本与寿命损失成本为目标的充电策略优化控制模型,结合换电需求与分时电价,使用遗传算法对一天内不同时段电池充电仓的充电倍率矩阵、充电截止电压进行求解;最后,通过算例验证了模型的有效性,也为其在实际充换电站的推广应用提供了参考。Electric heavy truck charging and swapping stations are developing rapidly,and battery charging strategies have an important impact on station-side operating costs and user battery swapping experience.How to meet the daily battery swapping needs of electric heavy trucks while minimizing station-side operating costs and shortening user battery swapping waiting time is a key research direction.First,a certain electric heavy truck charging and swapping station is taken as the experimental object,and statistical analysis methods are used to obtain user battery swapping needs at different times of the day.Secondly,a charging strategy optimization control model is proposed with the goal of reducing station-side battery charging costs and life loss costs.Combined with battery swapping demand and time-of-use electricity prices,a genetic algorithm is used to solve the charging rate matrix and charging cut-off voltage of the battery charging compartment at different times of the day.Finally,the effectiveness of the model is verified through experimental examples,which also provides reference for its wide application in actual charging and swapping stations.

关 键 词：电动重卡 充换电站 换电需求 充电策略 遗传算法(GA) 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] U491.8[自动化与计算机技术—控制科学与工程] TM910.6[交通运输工程—交通运输规划与管理]